MEA Meeting 2014

Proceedings

MEA Meeting 2014

July 1 - July 4, 2014, Reutlingen, Germany 9th International Meeting on Substrate-Integrated Microelectrode Arrays

Tübingen Neurotech 2014 1st Tübingen Symposium on Current Topics in Neurotechnology

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9th Int. Meeting on Substrate-Integrated Microelectrode Arrays, 2014


Best Pictures for Cover Image Substrate-Integrated Microelectrode Arrays: Electrophysiological Tools for Neuroscience, Biotechnology and Biomedical Engineering 9th International Meeting on Substrate-Integrated Microelectrode Arrays, July 1 - July 4, 2014, Reutlingen, Germany

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Cover images by courtesy of: 1 Jones Ian Lloyd et. al., Characterization of Mammalian Retinal Ganglion Cell Response to Voltage Stimulus, ETH Zürich, Basel, Switzerland 2 Lakshmi Channappa et. al., Electrical Imaging of Local Field Potentials in Organotypic and Acute Hippocampal Slices Using CMOS MEAs, NMI Reutlingen, Germany


Conference Proceedings of the 9th International Meeting on Substrate-Integrated Microelectrode Arrays 1st Tübingen Symposium on Current Topics in Neurotechnology July 1 - July 4, 2014, Reutlingen, Germany


Meeting Organisation and Imprint Organiser

NMI Natural and Medical Sciences Institute at the University of Tuebingen Markwiesenstrasse 55, 72770 Reutlingen, Germany Phone: +49 7121 - 51 530 -0; Fax: +49 7121 - 51 530 16; E-Mail: [email protected] Internet: www nmi.de

Publisher

NMI Natural and Medical Sciences Institute at the University of Tuebingen Markwiesenstrasse 55, 72770 Reutlingen, Germany Phone: +49 7121 - 51 530 -0; Fax: +49 7121 - 51 530 16; E-Mail: [email protected] Internet: www nmi.de

Editors

Dr. Alfred Stett and Dr. Günther Zeck

Co-organiser

Bernstein Center Freiburg Hansastraße 9, 79104 Freiburg, Germany Tel.: +49 (0)761 203 9549 ; Fax: +49 (0)761 203 9559 ; E-Mail: [email protected] Internet: www.bcf.uni-freiburg.de

Organisation Team

Karla Bellack, Ira Digel, Nadja Gugeler, Priscilla Herrmann E-Mail: [email protected] Internet: http://www.nmi.de/meameeting

Scientific Programme Committee

Dr. Michela Chiappalone, Italian Institute of Technology, Genova, Italy Prof. Ulrich Egert, University of Freiburg, Germany Prof. Gaute Einevoll, Norwegian University of Life Sciences (UMB), Norway Prof. Michele Giugliano, University of Antwerp, Belgium Prof. Yael Hanein, Tel-Aviv University, Israel Prof. Andreas Hierlemann, Swiss Federal Institute of Technology, Basel, Switzerland Prof. Jary Hyttinen, Tampere University of Technology, Finland Prof. Sven Ingebrandt, University of Applied Sciences, Kaiserslautern, Germany Dr. Udo Kraushaar, NMI Natural and Medical Sciences Institute, Reutlingen, Germany Prof. Sergio Martinoia, University of Genova, Italy Dr. Thomas Meyer, Multi Channel Systems MCS GmbH, Reutlingen, Germany Prof. Wim L.C. Rutten, University of Twente, The Netherlands Prof. Cornelius Schwarz, University of Tübingen, Germany Prof. Micha Spira, The Hebrew University of Jerusalem, Israel Prof. Bruce Wheeler, University of Florida, USA

Conference Chair

Dr. Alfred Stett and Dr. Günther Zeck, NMI Natural and Medical Sciences Institute, Reutlingen, Germany

Sponsor

Multi Channel Systems MCS GmbH, Reutlingen, Germany (www.multichannelsystems.com)

ISSN

2199-1596

NMI Natural and Medical Sciences Institute at the University of Tuebingen

Bernstein Center Freiburg

Multi Channel Systems MCS GmbH


Foreword Substrate-Integrated Microelectrode Arrays: Electrophysiological Tools for Neuroscience, Biotechnology and Biomedical Engineering 9th International Meeting on Substrate-Integrated Microelectrodes, July 1 - 4, 2014

Current Topics in Neurotechnology – from Basic Research to Medical Application 1st Tübingen Neurotech Symposium, July 1, 2014

At present, the prime methodology for studying neuronal circuit-connectivity, physiology and pathol-ogy under in vitro or in vivo conditions is by using substrateintegrated microelectrode arrays.1 This quote from a recent review highlights the major research fields and topics in which microelectrode arrays (MEAs) are currently used. These areas, together with applications in electrical stimulation, stem cell research, and drug and toxicology tests will be addressed at this year’s meeting on substrate-integrated microelectrode arrays (MEA 2014). The broad range of bioelectric applications relies on customized hardware for efficient recording and stimulation. Today’s microelectrode arrays comprise between 30 and 30,000 recording sites, which are embedded in glass, silicon or flexible polymers. The electrodes are either made of titanium nitride or carbon nanotubes, or they may be manufactured as field effect transistors or innovative organic materials. The entire range of technological developments in this area will be presented at the MEA Meeting 2014. It is an honour and great pleasure for the Natural and Medicalw Sciences Institute (NMI) — which has been producing MEAs since the late 1980s — to organize and host the 9th MEA Meeting. We are pleased to welcome more than 250 students, senior researchers, developers, and new as well as experienced MEA us-ers to Reutlingen’s new City Hall. Several hundred au-thors and co-authors from 26 countries submitted ab-stracts for MEA 2014, out of which we have chosen 25 for oral presentations and 115 for the poster exhibi-tion. All the accepted abstracts and papers will be pub-lished online as part of the meeting’s proceedings. These contributions, together with the eight keynote lectures which will be delivered by internation-allyrenowned scientists and leaders in the industrial sector, as well as the sizeable number of exhibitors showcasing their products and applications will cer-tainly make the MEA 2014 experience in Reutlingen a valuable and fruitful one for MEA developers and us-ers! We are grateful to the Bernstein Center Freiburg for continuing to co-organize our MEA Meeting. We deeply appreciate the tremendous work done by the members of the Scientific Committee: they reviewed each submitted abstract and rated them carefully. 1

We also thank the Scientific Committee for suggesting key-note speakers and appraising images for the bestpicture award. This year’s cover collage reflects the application of high-density CMOS-based arrays in single-cell analysis and systems neuroscience. MEA 2014 will be preceded by the first Tübingen Neurotech Symposium, which has also been organized by the NMI. Speakers from local research institutes, hospitals and the industral sector will address current topics in neurotechnology – from basic research to medical application. The symposium features several state-of-the-art MEA applications which are likely to guide future MEA developments and applications. The highlight of this symposium is the evening session with keynote lectures and a panel discussion addressing future directions and possibilities in brain research, as well as the international endeavours being made towards advancing neurotechnology, particularly in terms of large-scale brain mapping projects (BRAIN Initiative in the US, Human Brain Project in Europe). We welcome you heartily to Reutlingen and are looking forward to an exciting meeting with lively and stimulating discussions – with old and new friends and colleagues.

Enjoy the meeting! Dr. Alfred Stett, Dr. Günther Zeck Conference chairs, NMI, Reutlingen Prof. Dr. Ulrich Egert Co-Organizer, Bernstein Center Freiburg

Follow us on facebook: https://www.facebook.com/MEA.Meeting Be part of the MEA user community on Yahoo: https://groups.yahoo.com/neo/groups/mea-users/info

M.E.Spira, A.Hai, Nature Nanotechnology, 8, 83-94, 2013


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Program at a Glance Tuesday, July 1 13:30 - 18:00

Satellite Symposium: Tübingen Neurotech

19:00 - 20:30

Welcome Addresses and Opening Lectures by John Donoghue (Brown Institute for Brain Science, Brown University) and Katrin Amunts (Institute of Neuroscience and Medicine, Forschungszentrum Jülich)

20:30

Opening Reception

Wednesday, July 2 8:30 - 10:10

Session: Stimulation strategies for neurons and fibers

10:10 - 10:40

Coffee break

10:40 - 12:40

Session: System Neuroscience (brain slices, retina, spinal cord)

12:40 - 14:00

Lunch break

14:00 - 15:40

Session: Pharmacology, toxicology, drug screening

15:40 - 16:10

Coffee break

16:10 - 17:50

Session: Neuron and network properties (“Analysis of (sub)-cellular neuron properties” and “signal analysis and statistics”)

18:30

Social event: Punt ride on the river Neckar in Tübingen

Thursday, July 3 9:00 - 14:15

Poster Sessions

12:00 - 13:00

Lunch break

15:00 - 16:00

Coffee break

16:00 - 17:30

Session: In vivo recordings and stimulation

18:30

Social event: Conference Dinner at Kelter, Metzingen

Friday, July 4

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8:30 - 9:30

Session: Primary and stem-cell derived cardiac myocyte cultures

9:30 - 10:00

Coffee break

10:00 - 12:00

Session: MEA technology (new materials, fabrication, culture techniques)

12:00 - 12:15

Closing remarks

12:15

Farewell lunch 9th Int. Meeting on Substrate-Integrated Microelectrode Arrays, 2014

Thank You to Our Sponsors

Our Exhibitors


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In vitro Electrophysiology Recording and stimulating for all applications • Standard MEAs from 32 to 256 electrodes • Multiwell-MEA-System with 24 and 72 wells • CMOS-System with over 5 000 electrodes

In vivo Electrophysiology Powerful equipment for your experiment • Wireless-System with 32 channels • Tethered systems with up to 256 channels

Visit us at our booth or at www.multichannelsystems.com


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Contents

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Contents

Contents 1st Tübingen Symposium on Current Topics in Neurotechnology - from Basic Research to Medical Application Brain-Machine Interfaces and Algorithms Brain-Machine-Interfaces (BMI) in Paralysis and Behavioral Disorders.......................................................29 N. Birbaumer1,2,3, G. Gallegos-Ayala1, R. Sitaram4, U. Chaudhary1, S. Soekadar1, A. Ramos-Murguialday1,5 1 Institute of Medical Psychology and Behavioral Neurobiology, University of Tuebingen, Tuebingen, Germany 2 Ospedale San Camillo, IRCCS, Istituto di Ricovero e Cura a Carattere Scientifico, Venezia – Lido, Italy 3 Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany and German Center for Diabetes Research (DZDe.V.), Neuherberg, Germany 4 Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA 5 Health Technologies Department, TECNALIA, San Sebastian, Spain

Combination of Brain-Machine Interfaces and Non-Invasive Brain Stimulation from Basic Science to Clinical Applications.......................................................................................................30 S. R. Soekadar1,2, Matthias Witkowski1,2, Niels Birbaumer2,3,4 1 University Hospital of Tübingen, Applied Neurotechnology Lab, Department of Psychiatry and Psychotherapy, Tübingen 2 Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany 3 Ospedale San Camillo, IRCCS, Istituto di Ricovero e Cura a Carattere Scientifico, Venezia – Lido, Italy 4 Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany and German Center for Diabetes Research (DZDe.V.), Neuherberg, Germany

Pharmaco-TMS-EEG: A Novel Approach for Probing GABAergic Activity in Human Cortex.................32 Ulf Ziemann, Isabella Premoli, Florian Müller-Dahlhaus Dept. Neurology & Stroke, and Hertie Institute for Clinical Brain Research, University of Tübingen, Germany

Near-Infrared Spectroscopy (NIRS) - A Promising Tool for Neurofeedback?..............................................33 Andreas J. Fallgatter, Beatrix Barth, Ann-Christine Ehlis Dept. of Psychiatry and Psychotherapy, University Hospital of Tübingen, Germany

Machine Learning Algorithms for Brain Signal Processing.............................................................................34 Martin Spüler1, Carina Walter1, Armin Walter1, Martin Bogdan1,2, Wolfgang Rosenstiel1 1 Technische Informatik, Eberhard-Karls-Universität Tübingen, Germany 2 Technische Informatik, Universität Leipzig, Germany


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Neurotechnology: Basic and Advanced Technologies Implantable Bioelectronic Interfaces for Diagnosis, Therapy and Rehabilitation.........................................38 Alfred Stett, Volker Bucher, Claus Burkhardt, Martina Cihova, Katja Gutöhrlein, Massimo Kubon, Gorden Link, Rene von Metzen, Wilfried Nisch, Sebastian Röhler, Ramona Samba, Michael Weinmann, Martin Stelzle NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany

CMOS – Based Microelectrode Arrays (‘Neurochips’) for Simultaneous Stimulation and Detection Action Potentials at Sub-Cellular Resolution...................................................................................41 Günther Zeck1, Max Eickenscheidt, Lakshmi Channappa, Roland Thewes2 1 Natural and Medical Sciences Institute at the University of Tübingen 2 Chair of Sensor and Actuator Systems, Faculty of EECS, TU Berlin, Berlin, Germany

Electronic Systems for Electrophysiology and Implantable Devices...............................................................42 Clemens Boucsein, Karl-Heinz Boven, Andreas Möller Multi Channel Systems MCS GmbH, Reutlingen, Germany

The Technology of Retinal Implants: Challenges and Solutions......................................................................43 Walter-G. Wrobel Retina Implant AG, Reutlingen, Germany

Tapping Into Decision-Related Activity in Primary Somatosensory Cortex to Control the Subject’s Percept...............................................................................................................................................44 Cornelius Schwarz, Dominik Brugger and Stine Dehnke Systems Neurophysiology, Werner Reichardt Centre for Integrative Neuroscience and Dept. for Cognitive Neurology, Hertie Institute for Clinical Brain Research

Neuroprosthesis and Neuromodulation: Applications and Prospects To What Extent can Retinal Prostheses Restore Vision?..................................................................................45 Zrenner, Eberhart1; Bartz-Schmidt, Karl-Ulrich1; Chee, Caroline2; Gekeler, Florian1; Jackson, Timothy L.3; MacLaren, Robert4; Nemeth, Janos5; Sachs, Helmut6; Stingl, Katarina1; Wong, David7 1 Werner Reichardt Center for Integrative Neurosciences and Institute for Ophthalmic Research, University of Tübingen, Germany 2 Dept.of Ophthalmology, National University Health System, Singapore, Singapore 3 Dept. of Ophthalmology, School of Medicine, King’s College, London, United Kingdom 4 Dept. of Clinical Neurosciences, Nuffield Laboratory of Ophthalmology, University of Oxford, Oxford, United Kingdom 5 Semmelweis University, Budapest, Hungary 6 Städtisches Klinikum Dresden-Friedrichstadt, Dresden, Germany 7 Dept. of Ophthalmology, University of Hong Kong, Hong Kong

Brain-State Dependent Neuromodulation with Closed-Loop Neuroprosthetics for Neurorehabilitation..........................................................................................................................................47 Alireza Gharabaghi Functional and Restorative Neurosurgery, Neurosurgical University Hospital, University of Tübingen, Germany

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9th International Meeting on Substrate-Integrated Microelectrode Arrays Stimulation Strategies for Neurons and Fibers_____________________ 49 Keynote Lecture: Bioelectronic Medicines: Can MEAs Help Bring About a New Class of Treatments?..............................................................................50 Daniel Chew1, Bryan McLaughlin2, Arun Sridhar1, Sonal Patel1, Hannah Tipney1, Roy Katso1, Imran Eba3 and Kristoffer Famm1 1 Bioelectronics R&D, GlaxoSmithKline, Stevenage, Hertfordshire SG1 2NY, UK 2 The Charles Stark Draper Laboratory, Cambridge, Massachusetts 02139, USA 3 Action Potential Venture Capital, Cambridge, Massachusetts 02142, USA

Oral Presentation: Response Profiles of Auditory Neurons on Multielectrode Arrays...............................51 Stefan Hahnewald1, Anne Tscherter3, Marta Roccio1, Emanuele Marconi1, Carolyn Garnham2, Pavel Mistrik2, Teresa Melchionna2, Jürg Streit3, Julien Brossard4, Alexandra Homsy4, Herbert Keppner4, Hans-Rudolf Widmer5, Pascal Senn1 1 Inner Ear Research Laboratory, Department of Clinical Research, University of Bern and University Department of Otorhinolaryngology, Head & Neck Surgery, Bern, Switzerland 2 MED-EL Elektromedizinische Geraete GmbH (MED-EL), Innsbruck, Austria 3 Department Physiology, University of Bern, Bern, Switzerland 4 Haute Ecole Arc Ingénierie, La Chaux-de-Fonds, Switzerland 5 Department of Neurosurgery, Bern University Hospital, Bern, Switzerland

Oral Presentation: PEDOT-CNT Coated Electrodes Stimulate Retinal Neurons with Low Voltage Amplitudes...............................................................................................................................54 Ramona Samba1, Thoralf Herrmann2, Günther Zeck2 1 BioMEMS and Sensors Group, NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany 2 Neurochip Research Group, NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany

Oral Presentation: Wide-Field Photostimulation of Cortical Networks: Selective Activation of Excitatory Neurons and Its Impact on Evoked Reverberating Responses In Vitro..................................56 Rocco Pulizzi1, Gabriele Musumeci1, Chris Van Den Haute2, Veerle Baekelandt2, and Michele Giugliano1,3,4 1 Department of Biomedical Sciences, University of Antwerp, Belgium 2 Department of Neurosciences, KU Leuven, B-3000 Belgium 3 Brain Mind Institute, EPFL, Switzerland 4 University of Sheffield, S1 4DP Sheffield, UK

Restoring Light Sensitivity in Blind Retinae Using Next-Generation Photopharmacology........................58 Laura Laprell, Martin Sumser, Dirk Trauner Ludwig-Maximilians-Universitaet, Munich, Germany

Finding the Most Effective Site for Extracellular Neuronal Stimulation........................................................60 Milos Radivojevic, David Jäckel, Jan Müller, Vijay Viswam, Ian Lloyd Jones, Andreas Hierlemann, and Douglas Bakkum ETH Zürich, Bio Engineering Laboratory, D-BSSE, Basel, Switzerland


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Stimulation and Recording from the Same Microelectrode Using Low-Gain High-Resolution Data Acquisition System.........................................................................................................62 Hyunjun Jung, Yoonkey Nam Neural Engineering Laboratory, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

Combining an Optical Eye Model and MEA Recordings for Testing of Retinal Prostheses........................64 John M Barrett1, Lionel Chaudet2, Mark Neil2, Na Dong3, Xiaohan Sun3, Evelyne Sernagor1, Patrick Degenaar4 1 Institute of Neuroscience, Newcastle University, United Kingdom 2 Imperial College, London, United Kingdom 3 South East University, Nanjing, China 4 School of Electrical and Electronic Engineering, Newcastle University, United Kingdom

Simultaneous Stimulation and Recording from Healthy and Blind Mouse Retinas Using Implantable Subretinal Microchips and Flexible Microelectrode Arrays.......................................................66 Henrike Stutzki, Florian Helmhold, Max Eickenscheidt, Günther Zeck Neurochip Research Group,NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany

Localized Laser-Triggered Activity of Optogenetically Modified Neurons....................................................68 Thoralf Herrmann1, Christine Dürr2, Martin Kriebel2, Günther Zeck1 1 Neurochip Research Group, NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany 2 Molecular Biology, NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany

Effects of Long-term Electrical Stimulation on Retinal Ganglion Cell (RGC) Response in Retinal Degeneration Model (rd1) Mice........................................................................................................70 Joo Yeon Kim1,2, Wang Woo Lee1,2, Yong Sook Goo1,2 1 Department of Physiology, Chungbuk National University School of Medicine, Cheongju, S. Korea 2 Nano Artificial Vision Research Center, Seoul National University Hospital, Seoul, S. Korea

All Electrical or Optoelectrical Communication...............................................................................................72 Vanessa Maybeck, Wenfang Li1, Jan Schnitker, Andreas Offenhäusser ICS/PGI-8 Bioelectronics, Forschungszentrum Jülich GmbH, Germany

Characterization of Mammalian Retinal Ganglion Cell Response to Voltage Stimulus...............................74 Jones Ian Lloyd, Russell Thomas, Fiscella Michele, Franke Felix, Müller Jan, Radivojevic Milos, Hierlemann Andreas ETH Zürich, Bio Engineering Laboratory, D-BSSE, Basel, Switzerland

Autonomous Control of Network Activity.........................................................................................................76 Sreedhar Saseendran Kumar1,2, Jan Wülfing2, Samora Okujeni1, Joschka Boedecker3, Ralf Wimmer3, Martin Riedmiller3, Bernd Becker4, Ulrich Egert1,2 1 Biomicrotechnology, Institute of Microsystems Engineering, University of Freiburg, Freiburg, Germany 2 Bernstein Center Freiburg, Freiburg, Germany 3 Machine Learning Lab, University of Freiburg, Freiburg, Germany 4 Chair of Computer Architecture, University of Freiburg, Freiburg, Germany

Simultaneous Stimulation and Recording of Retinal Action Potentials Using Capacitively Coupled High- Density CMOS-based MEAs..............................................................................78 Dmytro Velychko1, Max Eickenscheidt1, Roland Thewes2, and Günther Zeck1 1 Neurochip Research Group, NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany 2 Chair of Sensor and Actuator Systems, Faculty of EECS, TU Berlin, Berlin, Germany

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Systems Neuroscience (brain slices, retina, spinal cord)______________80 Oral Presentation: On the Nature and Transmission of Information Among Cell-Assemblies: A Network and Information Analysis of Propagation Between Neural Populations in a Two-Chamber In Vitro System.....................................................................................................................81 DeMarse, Thomas1, Alagapan, Sankarleengam1, Pan, Liangbin1, Franca, Eric1, Leondopulos, Stathis1, Brewer, Gregory2, and Wheeler, Bruce1 1 Pruitt Dept. of Biomedical Engineering, University of Florida, Gainesville, Florida, USA 2 Dept. of Biomedical Engineering, University of California, Irvine, California, USA

Oral Presentation: Pairwise Reconstruction of the Hippocampal Tri-Synaptic Loop: EC-DG, DG-CA3, CA3-CA1...............................................................................................................................83 Gregory J. Brewer1, Harsh Desai1, Himanshi Agrawal1, Stathis S. Leondopulos2, Bruce C. Wheeler2 1 University of California Irvine, USA 2 University of Florida, Gainesville, FL USA

Oral Presentation: Electrical Imaging of Local Field Potentials in Organotypic and Acute Hippocampal Slices Using CMOS MEAs................................................................................................85 Lakshmi Channappa, Florian Helmhold, Günther Zeck Neurochip Research Group, NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany

Oral Presentation: Technique for Analysis of Purkinje Cell Sub-Cellular Functional Dynamics in Acute Cerebellar Slices Using a High-Density Microelectrode Array............................................................88 Marie Engelene J. Obien1, Andreas Hierlemann2, Urs Frey1,2 1 RIKEN Quantitative Biology Center, Kobe Japan 2 Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland

Oral Presentation: Changes of Axonal Conduction in Bursting Activity......................................................91 Kenta Shimba, Koji Sakai, Kiyoshi Kotani, Yasuhiko Jimbo The University of Tokyo, Tokyo, Japan

Oral Presentation: Off-like Responses to the Blue-light in rd1/ChR2 Mouse Retina..................................95 Jae Hong Park, Hyuk-June Moon, Dong-Hoon Kang, Sujin Hyung , Hyun Jun Shin, Jun-Kyo Francis Suh The Center for Bionics, Korea Institute of Science and Technology, Seoul, Republic of Korea

Decoding of Motion Directions by Direction-Selective Retina Cells.............................................................98 Michele Fiscella, Felix Franke, Jan Müller, Ian L. Jones, Andreas Hierlemann Bio Engineering Laboratory, ETH Zurich, Basel, Switzerland

High Resolution Large-Scale Recordings of Light Responses from Mouse Retinal Ganglion Cells.........100 Gerrit Hilgen1, Alessandro Maccione2, Stefano Di Marco2, Luca Berdondini2, Evelyne Sernagor1 1 Institute of Neuroscience, Medical Sciences, Newcastle University, Newcastle upon Tyne, UK 2 Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genova, Italy

Novel Insights into Visual Information Processing of Human Retina..........................................................102 Katja Reinhard1, Marion Mutter1, Michele Fiscella2, Jan Müller2, Felix Franke2, Andreas Hierlemann2, Thomas Münch1 1 Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany 2 ETH Zürich, D-BSSE, Basel, Switzerland


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Investigation of Functional Connectivity in Degenerative Retina Using MEA-STA System.....................103 Hyuk-June Moon, DongHoon Kang, JaeHong Park, SuJin Hyung, HyunJoon Shin, JunKyo Francis Suh Center for Bionics, Biomedical Research Institute, KIST, Seoul, Korea

Pharmacology, Toxicology, and Drug Screening___________________105 Keynote Lecture: Neurotoxicity Screening Using Multi-Well Microelectrode Array Plates: Lessons Learned and a Path Forward................................................................................................................106 Timothy J Shafer Integrated Systems Toxicology Division, NHEERL, ORD, United States Environmental Protection Agency, RTP, NC USA

Oral Presentation: Cytotoxicity of the Antimalarial Drug Mefloquine and Novel Protection by Quinolinic Acid....................................................................................................................................................109 Katelyn E. Holmes, David Smith, Guenter W. Gross Center for Network Neuroscience, Department of Biological Sciences, University of North Texas, Denton, USA

Oral Presentation: Norepinephrine Modulates Bursting Activity Differently During Development......112 Lui Yoshida, Kiyoshi Kotani, Yasuhiko Jimbo The University of Tokyo, Tokyo, Japan

Oral Presentation: Microelectrode Array Recording and Ca2+ Imaging in Hippocampal Cultures Enzymatically Treated with Hyaluronidase......................................................................................................114 Mukhina Irina1,2, Vedunova Maria1,2, Mitrosina Elena1,2, Sakharnova Tatiana1,2, Zakharov Yury1, Pimashkin Alexey1, Dityatev Alexander1,3 1 N.I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia 2 Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia 3 German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany

Cortical Networks Respond with Major Activity Changes to Sildenafil Citrate (Viagra)..........................116 Nicole K Calderon1,2, Daniel Ledee1,2, Kamakshi Gopal1,2, Guenter W. Gross2,3 1 Department of Speech and Hearing Sciences 2 Center for Network Neuroscience 3 Department of Biological Sciences University of North Texas, Denton, USA

Role of Tachykinin1 and αCGRP on Modulating Inflammatory Sensitization of TRPV1.........................118 Sakthikumar Mathivanan, Christoph Jakob Wolf, Isabel Devesa, Clotilde Ferrandiz Huertas, Antonio Ferrer-Montiel Instituto de Biología Molecular y Celular, Universidad Miguel Hernandez de Elche, Alicante, Spain

Pharmacological Studies of Synaptic Communication in Maturing Human Pluripotent Stem Cell Derived Neural Networks...................................................................................................................................120 Meeri Mäkinen, Laura Ylä-Outinen, Dimitriy Fayuk, Susanna Narkilahti NeuroGroup, BioMediTech, University of Tampere, Tampere, Finland

Automated Cytocentering™ Patch Clamp Recordings of Primary and iPSC-derived Cells Open New Paths in Basic and Applied Ion Channel Research......................................................................122 Dirck Lassen1,2, Olaf Scheel1, Stefanie Frech1, Thomas Knott1, Peter van Stiphout2 1 Cytocentrics Bioscience GmbH, Rostock, Germany 2 Cytocentrics BV, Eindhoven, The Netherlands

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Ostreopsis cf. Ovata Toxicity Evaluation by Using Microelectrode Array Based Platform.......................124 Susanna Alloisio1,2, Valentina Giussani3, Marika Zanin1, Valentina Asnaghi3, Mariachiara Chiantore3, Antonio Novellino1,2 1 ETT S.p.A., via Sestri 37, Genoa, Italy 2 Institute of Biophysics, National Research Council, Genoa, Italy 3 DiSTAV, University of Genoa, Genoa, Italy

Biphasic Reaction on Sodium Valproic Acid (NaVPA)- Treatment in Neuronal Networks......................126 Matthias Nissen1, Sebastian Bühler2, Marco Stubbe1 and Jan Gimsa1 1 University of Rostock, Biophysics, Rostock, Germany 2 Leibniz-Institut für Nutztierbiologie (FBN), Dummerstorf, Germany

Thymoquinone Protects Cultured Hippocampal and Human Induced Pluripotent Stem Cells-Derived Neurons Against α-Synuclein-Induced Synapse Damage...........................................127 Alhebshi Amani, Odawara Aoi, Gotoh Masao,Suzuki Ikuro Graduate School of Bionics, Tokyo University of Technology, Tokyo, Japan

Exciting New Easy-to-Use Hybrid Measurement System for Extracellular Potential And Impedance Recording in a 96-Well Format......................................................................................................129 David Guinot, Leo Doerr, Sonja Stoelzle-Feix, Ulrich Thomas, Matthias Beckler, Michael George, Niels Fertig Nanion Technologies GmbH, Munich, Germany

Electrical Stimulation of Neuronal Cultures as a New Method for Neuropharmacological and Neurotoxicological Evaluation...........................................................................................................................131 Marika Zanin2, Enrico Defranchi1, Susanna Alloisio2,3, Antonio Novellino2,3, Sergio Martinoia1 1 DIBRIS, University of Genoa, Genoa, Italy 2 Alternative Toxicity Services, ETT s.p.a., Genoa, Italy 3 Institute of Biophysics, National Research Council, Genoa, Italy

Real-time Monitoring of Lesion Healing by Impedance Spectrometry on Chip........................................133 Heinz D. Wanzenboeck1, Andreas Exler1, Johann Mika1, Emmerich Bertagnolli1 Elisabeth Engleder2, Michale Wirth2, Franz Gabor2 1 Vienna University of Technology, Institute for Solid State Electronics, Vienna, AUSTRIA 2 University of Vienna, Department of Pharmaceutical Technology and Biopharmaceutics, Vienna, AUSTRIA

Functional Phenotypic Compound Screening with Primary Brain-Region Specific Cell Cultures..........135 Alexandra Voss, Corina Ehnert, Konstantin Jügelt, Anna-Maria Pielka, Angela Podßun, Olaf H.-U. Schröder NeuroProof GmbH, Rostock, Germany

MEA technology - a New Player in Diabetes Research...................................................................................137 Sven Schönecker1, Peter Krippeit-Drews2, Gisela Drews2, Karl-Heinz Boven3, Elke Guenther1, Udo Kraushaar1 1 NMI Natural and Medical Sciences Instute at the University of Tübingen, Dept. Electrophysiology, Reutlingen, Germany 2 University of Tübingen, Institute of Pharmacy, Dept. Pharmacology, Tübingen, Germany 3 Multi Channel Systems, Reutlingen, Germany


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Neuron and Network Properties Analysis of (Sub)-Cellular Neuron Properties______________________ 138 Oral Presentation: Glutamate Mediated Astrocytic Filtering of Neuronal Activity...................................139 Gilad Wallach, Yael Hanein School of Electrical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel

Oral Presentation: Analysis of Related Molecules to Synchronized Burst Activity of Rat Cultured Cortical Networks.........................................................................................................................143 Daisuke Ito1,2, Kazutoshi Gohara3 1 Division of Functional Life Science, Faculty of Advanced Life Science, Hokkaido University, Sapporo, Japan 2 Research Fellow of the Japan Society for the Promotion of Science, Tokyo, Japan 3 Division of Applied Physics, Faculty of Engineering, Hokkaido University, Sapporo, Japan

Oral Presentation: Functional Connectivity Estimate from Spontaneous and Stimulus Evoked Activities in Dissociated Cultured Neurons on a High-Density CMOS Microelectrode Array................147 Takeshi Mita1, Douglas J. Bakkum2, Urs Frey3, Andreas Hierlemann2, Ryohei Kanzaki1, Hirokazu Takahashi1 1 The University of Tokyo, Tokyo, Japan 2 ETH Zurich, Basel, Switzerland 3 RIKEN Quantitative Biology Center, Kobe, Japan

Oral Presentation: Frequency Multiplications Accompanied by Sudden Synchrony Leaps and Ultrafast Neuronal Plasticity...............................................................................................................................149 Hagar Marmari1, Roni Vardi1, Amir Goldental2 and Ido Kanter1,2 1 Gonda Interdisciplinary Brain Research Center and the Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel 2 Department of Physics, Bar-Ilan University, Ramat-Gan, Israel

Oral Presentation: Simultaneous Intra- and Extracellular Recordings using a Combined High-Density Microelectrode Array and Patch-Clamp System....................................................................153 David Jäckel1, Jan Müller1, Thomas L. Russell1, Milos Radivojevic1, Felix Franke1, Urs Frey2, Douglas J. Bakkum1 and Andreas Hierlemann1 1 ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland 2 RIKEN Quantitative Biology Center, Kobe, Japan.

An MEA-Based Model for Rapid Acceleration Injury to Neuronal Networks and Studies of Recovery..............................................................................................................................................156 David C. Smith, Guenter W. Gross Department of Biological Sciences, Center for Network Neuroscience University of North Texas, Denton, USA

Exploring the Role of Microtubule-Associated Protein Tau (Mapt) in Synaptic Plasticity........................158 Torsten Bullmann1,2, Tanja Treutlein2, Jens T. Stieler2, Max Holzer2, Thomas Arendt2, Urs Frey1 1 RIKEN Quantitative Biology Center, Kobe, Japan 2 Paul Flechsig Institute, Leipzig University, Leipzig, Germany

3D Finite Element Modeling of Single Neurons and the Microelectrode Array Microenvironment.......160 Douglas J. Bakkum1, Milos Radivojevic1, David Jäckel1, Felix Franke1, Thomas L. Russell1, Urs Frey2, Hirokazu Takahashi3, Andreas Hierlemann1 1 ETH Zurich, Basel, Switzerland 2 RIKEN Quantitative Biology Center, Kobe, Japan 3 The University of Tokyo, Tokyo, Japan

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Multi-Parameter Analysis Platform for Neuronal Growth, Differentiation and Communication...........162 L. Micholt, D. Braeken Life Science Technologies, imec, Heverlee, Belgium

Characterizing the Human Embryonic Stem Cell – derived Neural Cultures: the Effect of Astrocytes on Network Properties...............................................................................................164 Marja Pajunen, Tanja Paavilainen, Dmitriy Fayuk, Laura Ylä-Outinen, Susanna Narkilahti BioMediTech, University of Tampere, Finland

Neuronal Migration and Activity in Mature Primary Cultures on a High-Density CMOS Array...........166 Hirokazu Takahashi1, Takeshi Mita1, Satoru Okawa1, Ryohei Kanzaki1, Urs Frey2, Andreas Hierlemann3, Douglas Bakkum3 1 The University of Tokyo, Tokyo, Japan 2 RIKEN Quantitative Biology Centre, Kobe, Japan 3 ETH Zurich, Basel, Switzerland

Simultaneous Microelectrode Array Recording and Intracellular Ca2+ Imaging of Human Pluripotent Stem Cell–Derived Neural Cultures.............................................................................................168 Fayuk Dmitriy, Paavilainen Tanja, Meeri Mäkinen, Pajunen Marja, Heikkilä Juha, Narkilahti Susanna BioMediTech, University of Tampere, Tampere, Finland

Spontaneous and Evoked Activity in Patterned Multi- Cluster Neuronal Networks..................................170 Margarita Anisimova1, Keiko Yokoyama1,2, Daisuke Ito2,3, Masaaki Suzuki4, Tsutomu Uchida1, Kazutoshi Gohara1 1 Division of Applied Physics, Faculty of Engineering, Hokkaido University, Sapporo, Japan 2 Research Fellow of the Japan Society for the Promotion of Science, Tokyo, Japan 3 Division of Functional Life Science, Faculty of Advanced Life Science, Hokkaido University, Sapporo, Japan 4 National Institute of Advanced Industrial Science and Technology (AIST), Sapporo, Japan

Evaluation of Action Potential Conduction Velocity for Myelination Research.........................................172 Koji Sakai, Kenta Shimba, Kiyoshi Kotani, Yasuhiko Jimbo The University of Tokyo, Tokyo, Japan

Electrophysiological Characterization of Individual Neurons in Sparse Cortical Cultures.......................174 Weir Keiko, Blanquie Oriane, Kilb Werner, Luhmann Heiko J. and Sinning Anne Institute of Physiology, University Medical Center Mainz, Mainz, Germany

Axon-Isolating Channels on a High Density Microelectrode Array.............................................................176 Marta K. Lewandowska, Douglas J. Bakkum, Andreas Hierlemann Department of Biosystems Science and Engineering, ETH Zürich, Basel Switzerland

Pathomechanisms of Acquired Autoimmune Encephalitis Disorders Associated with Epilepsy and Anti- NMDA Receptors and Anti-LGI1 Autoantibodies........................................................178 Elsen G. E.1, Wolking S.1, Maljevic S.1, Wandinger K.P.2, Lerche H.1, Dihné M.3 1 Hertie Institute for Clinical Brain Research, Department of Neurology and Epileptology, University of Tübingen, Germany; 2 Institute for Clinical Chemistry and Department of Neurology, University Hospital Schleswig-Holstein, Lübeck, Germany 3 Department of Neurology, St. Lukas Klinik, Solingen, Germany

Effects of Epilepsy-Associated Ion Channel Mutations on Neuronal Network Activity............................179 Snezana Maljevic, Yvonne Füll, Heidi Löffler, Filip Rosa, Yuanyuan Liu, Holger Lerche Dept. of Neurology and Epileptology, Herti Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany


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Neuron and Network Properties Signal Analysis and Statistics___________________________________ 180 Paired Spiking Is an Ubiquitous Response Property in Network Activity...................................................181 Aurel Vasile Martiniuc1, Rouhollah Habibey2, Asiyeh Golabchi2, Victor Bocos-Bintintan3, Alois Knoll1, Axel Blau2 1 Computer Science Department VI, Technical University Munich, Garching, Germany 2 Dept. of Neuroscience and Brain Technologies (NBT), Fondazione Istituto Italiano di Tecnologia (IIT), Genoa, Italy 3 Faculty of Environmental Science & Engineering, Babes- Bolyai University, Cluj-Napoca, Romania

Cultured Cortical Neurons Can Separate Source Signals from Mixture Inputs..........................................183 Takuya Isomura1,2, Kiyoshi Kotani3, Yasuhiko Jimbo3 1 Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan 2 Japan Society for the Promotion of Science (JSPS), Tokyo, Japan 3 Department of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan

High-Throughput, High-Performance, Low-Latency Spike-Sorting Hardware Platform..........................185 Dragas Jelena, Jäckel David, Franke Felix, Hierlemann Andreas ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland

CARMEN, HDF5 and NDF: Reproducible Research on MEA Signals from Mouse and Ferret Retina.....................................................................................................................................187 L. S. Smith1, S. J. Eglen2, E. Sernagor3 1 Computing Science and Mathematics, University of Stirling, Stirling, Scotland UK 2 DAMTP, Centre for Mathematical Sciences, University of Cambridge, Cambridge, England, UK 3 Institute of Neuroscience, Faculty of Medical Sciences, University of Newcastle, Newcastle, England, UK

Post Stimulus Activity Patterns Reflect Stimulus Responses..........................................................................188 Jelle Dijkstra1 and Joost le Feber1,2 1 Biomedical Signals and Systems, University of Twente, Enschede, the Netherlands 2 Clinical Neurophysiology, University of Twente, Enschede, the Netherlands

Detecting Significant Changes in Oscillatory Power with Cluster-Based Permutation Testing................190 Ben van Lier1,2, Andreas Hierlemann2 and Frederic Knoflach1 1 F. Hoffmann - La Roche Ltd. Pharmaceutical Research and Early Development, Neuroscience, Basel, Switzerland 2 ETH Zürich, Department of Biosystems Science and Engineering, Basel, Switzerland.

Topological Effects on Bursting and Reverberation Dynamics......................................................................192 Chun-Chung Chen1 Institute of Physics, Academia Sinica, Taipei, Taiwan

A Multivariate Spike-Detection Algorithm to Asses Activity Patterns of Three-Dimensional In Vitro Models.................................................................................................................194 Andreas W. Daus, Robert Bestel, Christiane Thielemann Biomems lab, University of Applied Sciences Aschaffenburg, Germany

Partial Correlation Analysis for Functional Connectivity Studies in Cortical Networks...........................196 Daniele Poli, Vito Paolo Pastore, Simonluca Piazza, Paolo Massobrio and Sergio Martinoia Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, Genova, Italy 16


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Modulating Functional Connectivity in Hippocampal Cultures Using Hebbian Electrical Stimulation...........................................................................................................................198 Víctor Lorente1, José Manuel Ferrández1, Eduardo Fernández2, Félix de la Paz3 1 Departamento de Electrónica, Tecnología de Computadores y Proyectos, Universidad Politécnica de Cartagena, Spain 2 Instituto de Bioingeniería, Universidad Miguel Hernández, Alicante, Spain 3 Departamento de Inteligencia Artificial, UNED, Madrid, Spain

Learning and Plasticity in Hippocampal Cultures Using Antiepilectic Drugs............................................200 José Manuel Ferrández1, Victor Lorente1, Arancha Alfaro3, Eduardo Fernández2 1 Departamento de Electrónica, Tecnología de Computadores y Proyectos, Universidad Politécnica de Cartagena, Spain 2 Instituto de Bioingeniería, Universidad Miguel Hernández, Alicante, Spain 3 Departamento de Neurología, Hospital Vega Baja de Orihuela, Alicante, Spain

A Web-Based Framework for Semi-Online Parallel Processing of Extracellular Neuronal Signals Recorded by Microelectrode Arrays..................................................................................................................202 Mufti Mahmud1,2, Rocco Pulizzi1, Eleni Vasilaki3, Michele Giugliano1,3,4 1 Theoretical Neurobiology & Neuroengineering Lab, Dept. of Biomedical Sciences, University of Antwerp, Wilrijk, Belgium 2 Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh 3 Department of Computer Science, University of Sheffield, Sheffield, UK 4 Brain Mind Institute, Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland

Comparison of Neuronal Dynamics in 2D and 3D In Silico Networks........................................................204 Inkeri Vornanen, Jarno M. A. Tanskanen, Jari Hyttinen, Kerstin Lenk Tampere University of Technology, Department of Electronics and Communications Engineering, and BioMediTech, Tampere

Estimating Stationary Functional Connection Underlying Switching-State Network Activity................206 Yuichiro Yada1,2,3, Osamu Fukayama1, Takayuki Hoshino1, Kunihiko Mabuchi1 1 Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan 2 Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan 3 Research Fellow of the Japan Society for the Promotion of Science, Tokyo, Japan

Network Dynamics in Connected Neuronal Sub-Populations During In Vitro Development.................208 Marta Bisio, Alessandro Bosca, Valentina Pasquale, Luca Berdondini and Michela Chiappalone Istituto Italiano di Tecnologia (IIT), Genova, Italy

Evoked Activity of Major Burst Leaders in Cultured Cortical Networks.....................................................210 Valentina Pasquale1, Sergio Martinoia2, Michela Chiappalone1 1 Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genova, Italy 2 Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova, Genova, Italy

Use of Entropy to Discriminate Between Different Conditions in MEA Recordings.................................212 Capurro Alberto1, Perrone Sandro1, Heuschkel Marc2, Makohliso Solomzi2, Baez Candise1 and Luthi-Carter Ruth1 1 Department of Cell Physiology and Pharmacology, University of Leicester, UK 2 Qwane Biosciences SA, EPFL Innovation Park, Lausanne, Switzerland

Initiation of Spontaneous Bursts in Dissociated Neuronal Cultures Is Poisson Process............................214 Arthur Bikbaev1, Abdelrahman Rayan2, Martin Heine1 1 AG Molecular Physiology, Leibniz Institute for Neurobiology, Magdeburg, Germany 2 AG Molecular Neuroplasticity, Deutsches Zentrum für Neurodegenerative Erkrankungen, Magdeburg, Germany


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Selective Laser Ablation of Interconnections Between Neuronal Sub-Populations: A Test-Bed for Novel Neuroprosthetic Applications.......................................................................................216 Alberto Averna1, Marta Bisio1, Valentina Pasquale1, Paolo Bonifazi2, Francesco Difato1 and Michela Chiappalone1 1 Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genova, Italy 2 School of Physics and Astronomy, Tel Aviv University (TAU), Israel

Simulation of hPSC Derived Neuronal Networks with Short and Long Reaching Axons.........................218 Kerstin Lenk1, Laura Ylä-Outinen2, Lukas H B Tietz1, Susanna Narkilahti2, Jari A K Hyttinen1 1 BioMediTech and Department of Electronics and Communications Engineering, Tampere University of Technology, Tampere, Finland 2 NeuroGroup, BioMediTech, University of Tampere, Tampere, Finland

Simplicial Complex and Betti Numbers to Characterize Co-Activation Patterns in Cortical Cultures..............................................................................................................................................220 Virginia Pirino1, Paolo Massobrio1, Eva Riccomagno2, Sergio Martinoia1 1 Neuroengineering and Bio-nano Technology Group (NBT), Department of Informatics Bioengineering Robotics and System Engineering, University o Genova, Genova, Italy 2 Department of Mathematics, University of Genova, Genoa, Italy

Investigation of Neuronal Activity Recorded with Multi-Electrode Arrays Using Principal Component Analysis...............................................................................................................222 Patrick Schuller1, Johann K. Mika1, Heinz Wanzenböck1, Petra Scholze2, Emmerich Bertagnolli1 1 Institute of Solid State Electronics, Vienna University of Technology, Vienna, Austria 2 Center for Brain Research, Medical University of Vienna, Vienna, Austria

Modelling Seizure-Like Superburst Activity of Dissociated Neuronal Cultures.........................................224 Simonov Alexander1, Esir Pavel1, Mukhina Irina1,2, Kazantsev Victor1,3 1 Lobachevsky State University of Nizhny Novgorod, Russia 2 Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia 3 Institute of Applied Physics RAS, Nizhny Novgorod, Russia

Cross-Correlation of Neural Culture Spiking Activity on Multielectrode Arrays is Strongly Dependent on Bursting Activity Pattern...........................................................................................226 Pimashkin Alexey1,2, Gladkov Arseniy1,3, Lepina Anastasia1,3, Mukhina Irina1,2,3, Kazantsev Victor1,2 1 Neuroscience Center, N.I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia 2 Department of Nonlinear Dynamics, Institute of Applied Physics RAS, Nizhny Novgorod, Russia 3 Normal Physiology Department, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

Spontaneous and Stimulus Induced Connectivity Changes in Cultured Neural Network........................228 Arseniy Gladkov1,3, Alexey Pimashkin1,2, Anastasia Lepina1,3, Victor Kazantsev1,2, Irina Mukhina1,3 1 Neuroscience Center, N.I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia 2 Department of Nonlinear Dynamics, Institute of Applied Physics RAS, Nizhny Novgorod, Russia 3 Normal Physiology Department, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

Short ISI Stimulation Modifies Firing Property of a Cultured Neuronal Network.....................................230 Hidekatsu Ito, Suguru N. Kudoh School of Science and Technology, Kwansei Gakuin University, Sanda, Japan

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In Vivo Recordings and Stimulation____________________________232 Oral Presentation: Skin-like Neuroprosthesis Based on Elastomeric Composites for Chronic Epidural Electrical Stimulation of Paralyzed Rats............................................................................233 Alexandre Larmagnac1, Niko Wenger2, Pavel Musienko2, Janos Vörös1, Grégoire Courtine2 1 Laboratory of Biosensors and Bioelectronics, ETH Zurich, Switzerland 2 International Paraplegic Foundation Chair in Spinal Cord Repair, Center for Neuroprosthetics, EPFL, Switzerland

Electrical Stimulation of the Auditory Brainstem with a Flexible Polyimide Microelectrode Array.......235 Amélie Guex1, Rohit U. Verma2, Ariel E. Hight2, M. Christian Brown2, Philippe Renaud3, Daniel J. Lee2, Stéphanie P. Lacour1 1 LSBI, Center for neuroprosthetics, EPFL, Lausanne, Switzerland 2 Eaton-Peabody Laboratories, Massachusetts Eye and Ear Infirmary, Boston, USA 3 LMIS4, STI, EPFL, Lausanne, Switzerland

PDMS Based μ-ECoG .........................................................................................................................................237 Arthur Hirsch1, Ivan Minev1, Natalia Pavlova2, Léonie Asboth2, Grégoire Courtine2, Stéphanie P. Lacour1 1 Laboratory for Soft Bioelectronic Interfaces, Centre for Neuroprosthetics, EPFL, Switzerland 2 UPCOURTINE, Brain Mind Institute, EPFL, Switzerland


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MEA Technology Oral Presentations____________________________________________ 239 Keynote Lecture: Conducting Polymer Devices for In Vivo Electrophysiology..........................................240 George G. Malliaras Department of Bioelectronics, Ecole Nationale Supérieure des Mines, Gardanne, France

Oral Presentation: In-vitro Recordings of Electrogenic Cells Activity with Organic Field Effect Transistors.............................................................................................................................................................242 A. Spanu1,2, S. Lai1, P. Cosseddu1, M. Tedesco2, A.Bonfiglio1,3, and S. Martinoia2 1 University of Cagliari – Dept. of Electrical and Electronic Engineering, Cagliari, Italy 2 University of Genova – Dept. of Informatics, Bioengineering, Robotics and System Engineering, Genova, Italy 3 CNR – Institute of Nanoscience, Modena, Italy

Oral Presentation: High-Density CMOS-based Microelectrode Array Platform for Large-Scale Neuronal Network Analysis...........................................................................................................244 Jan Müller1, Marco Ballini1, Yihui Chen1, Paolo Livi1, Milos Radivojevic1, Urs Frey2, Douglas Bakkum1 and Andreas Hierlemann1 1 ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland. 2 RIKEN Quantitative Biology Center, Kobe, Japan.

Oral Presentation: A Capacitively-Coupled CMOS-MEA with 4225 Recording Sites and 1024 Stimulation Sites..........................................................................................................................................247 Gabriel Bertotti1, Norman Dodel1, Stefan Keil1, Dirk Wolansky2, Bernd Tillak2, Matthias Schreiter3, Max Eickenscheidt4, Günther Zeck4, Alfred Stett4, Andreas Möller5, Karl-Heinz Boven5, and Roland Thewes1 1 TU Berlin, Faculty of EECS, Chair of Sensor and Actuator Systems, Berlin, Germany 2 IHP GmbH, Technology/Process Research, Frankfurt/Oder, Germany 3 Siemens AG, Corporate Technology, Research & Technology Center, Munich, Germany 4 NMI Natural and Medical Sciences Institute at the University of Tübingen, Neurochip Research Group, Reutlingen, Germany 5 Multi Channel Systems GmbH, Reutlingen, Germany

Oral Presentation: Electronic Motion Tracking of Single Migrating T Cells Using Field-Effect Transistor Arrays.............................................................................................................................251 Jessica Ka-Yan Law1, Xuan-Thang Vu1, Xiao Zhou2, Saya Ri Heidingsfelder1, Bin Qu2, Markus Hoth2, Sven Ingebrandt1 1 Department of Informatics & Microsystem Technology, University of Applied Sciences Kaiserslautern, Zweibrücken, Germany 2 Department of Biophysics, Saarland University, Homburg, Germany

Oral Presentation: Nano-Needle Gated Field Effect Transistor (NGFET) Array for Intracellular Recording and Stimulation of Neurons......................................................................................255 Rao Rashmi B.1, Singh Katyayani1, Sikdar Sujit K.2 and Bhat Navakanta3 1 Centre for Nano Science and Engineering 2 Molecular Biophysics Unit 3 Centre for Nano Science and Engineering & Electrical Communication Engineering, Indian Institute of Science, Bangalore, INDIA

Oral Presentation: Two-Chamber MEA to Unidirectionally Connect Neuronal Cultures......................259 Joost le Feber1,2, Wybren Postma1, Wim Rutten3, Eddy de Weerd3, and Marcel Weusthof1 1 Biomedical Signals and Systems, University of Twente, Enschede, the Netherlands 2 Clinical Neurophysiology, University of Twente, Enschede, the Netherlands 3 Neural Engineering Dept./Biomedical Signals and Systems, University of Twente, Enschede , the Netherlands 4 BIOs-Lab on a Chip Group, University of Twente, Enschede, the Netherlands

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MEA Technology New Materials and Designs_____________________________________ 263 Effect of Ion Sensitivity on Extracellular Recordings with Graphene Transistors.......................................264 Lucas Hess, Martin Lottner, Michael Sejer Wismer, Jose A. Garrido Walter Schottky Institut, Physik-Department, Technische Universität München, Germany

Boron Doped Diamond for Neural Stimulation or Recording......................................................................266 Fabien Sauter-Starace1,Jean-LouisDivoux2, Clément Herbert3, Hugues Girard3, Christine Mer3, Philippe Bergonzo3 1 CEA LETI CLINATEC, Minatec Campus, Grenoble France 2 MXM Group,VALLAURIS, France 3 CEA-LIST, Diamond Sensors Laboratory, Gif-sur-Yvette, France

Impedance Considerations on MEA – the Effect of Electrode Materials and Coatings.............................268 Tomi Ryynänen1, Manoj Sivasubramaniapandian1, Marja Peltola2, Susanna Narkilahti2, Jukka Lekkala1 1 Department of Automation Science and Engineering and BioMediTech, Tampere University of Technology, Tampere, Finland 2 NeuroGroup, BioMediTech, University of Tampere, Tampere, Finland

Carbon Nanotube Multi-Electrode Array Chips for Noninvasive Real-Time Measurement of Dopamine, Action Potentials, and Postsynaptic Potentials.......................................................................270 Mao Fukuda1, Keiichi Shirakawa1, Hideyasu Jiko1, Ikuro Suzuki2 1 Alpha MED Scientific Inc., 209 Saito-incubator, 7-7-15 Saito-asagi, Ibaraki, Osaka, Japan 2 Graduate School of Bionics, Tokyo University of Technology, 1404-1 Katakura, Hachioji, Tokyo, Japan

Evaluation of Materials and Fabrication Techniques for Capacitive Electrodes..........................................272 Sebastian Röhler1, Claus J. Burkhardt1, Dieter P. Kern2, Alfred Stett1 1 NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany 2 Institute of Applied Physics, University of Tübingen, Tübingen, Germany

Chemical Vapor Deposition Grown Carbon Nanotubes on Recessed Contacts of Flexible Polyimide MEAs................................................................................................................................274 Kerstin Schneider1, Boris Stamm2, Katja Gutöhrlein2, Claus Burkhardt2, Alfred Stett2 and Dieter P. Kern1 1 Institute for Applied Physics, University of Tübingen, Tübingen, Germany 2 NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany

Diamond-Based Platforms for Long-Term Growth and Investigation of Neurons....................................276 Alexandra Voss1, Hongying Wei2, Maria Giese2, Monika Stengl2, Johann Peter Reithmaier1, Cyril Popov1 1 Institute of Nanostructure Technologies and Analytics, CINSAT, University of Kassel, Germany 2 Department of Animal Physiology, CINSAT, University of Kassel, Germany

Cost-Effective Technique to Create CNTs Layers Enhancing Neuronal Network Development and Activity..................................................................................................................................278 Amélie Béduer1, Arushi Varshney1, Emmanuel Flahaut2, Marc Heuschkel3, Philippe Renaud1 1 Microsystems Laboratory, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland 2 University of Toulouse; UPS, INP; Institut Carnot Cirimat, Toulouse, France 3 Qwane Biosciences SA, EPFL Innovation Park, Lausanne, Switzerland

A Novel 3D Microelectrode Array for Extracellular Signal Recording of Acute Brain Slices....................280 Carolin K. Kleber, Manuel Martina, Claus J. Burkhardt, Elke Guenther, Udo Kraushaar NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany


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Realisation of Co-Culture MEA Biochips Using a Novel SU-8 Fabrication Processfor Applications in Axonal and Synaptic Biology..................................................................................................282 Marc Heuschkel, Nader Jedidi, Solomzi Makohliso Qwane Biosciences SA, EPFL Innovation Park, Building C, Lausanne, Switzerland

Advances in Diamond Microelectrode Arrays.................................................................................................284 T. Conte1,2, Z. Gao1, J.B. Destro-Filho2, V. Carabelli3, E. Carbone3 and A. Pasquarelli1 1 Institute of Electron Devices and Circuit, Ulm University, Ulm, Germany 2 School of Electrical Engineering, Federal University of Uberlandia, Brazil 3 Department of Drug Science, NIS Centre, CNISM, University of Torino, Torino, Italy

Towards Stretchable High-Density Multielectrode Arrays.............................................................................286 Flurin Stauffer1, Vincent Martinez1, Mohammed Adagunodo2, Janos Vörös1, Alexandre Larmagnac1 1 Laboratory of Biosensors and Bioelectronics, ETH Zurich, Switzerland 2 Faculty of Medicine, Biomedical Engineering, University of Bern, Switzerland

Towards Local Chemical Stimulation Through a Nanopore Array on a Microelectrode Array...............287 Peter D. Jones, Paolo Cesare, and Martin Stelzle NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany

Polymer-Based In Vivo Transducers with Conductive Polymer Mixture Polydimethylsiloxane Composite Electrodes...................................................................................................289 Asiyeh Golabchi, Rouhollah Habibey, Marina Nanni, Francesca Succol, Axel Blau Fondazione Istituto Italiano di Tecnologia (IIT), Dept. of Neuroscience and Brain Technologies (NBT), Neurotechnologies (NTech), Genoa, Italy, www.iit.it

Nanostructured Diamond Microelectrode Arrays for Neural Recording and Stimulation.......................291 Gaëlle Piret1,2, Clément Hébert3, Lionel Rousseau4, Emmanuel Scorsone3, Raphaël Kiran3, Myline Cottance4, Gaëlle Lissorgues4, Marc Heuschkel5, Serge Picaud6, Blaise Yvert1,2, Philippe Bergonzo3 1 INSERM, Clinatec, U1167, Grenoble, France 2 CEA-Léti, Clinatec, U1167, Grenoble, France 3 CEA LIST, Diamond Sensors Laboratory, Gif-sur-Yvette, France 4 Université Paris-Est, ESYCOM, ESIEE Paris, Noisy le Grand, France 5 Qwane Biosciences SA, EPFL Innovation Park, Lausanne, Switzerland 6 Institut de la Vision, UMRS 968 Inserm/UPMC, UMR7210 CNRS, Paris, France

New Developments on the Fabrication of High Density and Low Density Diamond-Based MEA.........293 A. Battiato1,2,4, E. Bernardi1,2,4, V. Carabelli3,4, E. Carbone3,4, S. Gosso3,4, P. Olivero1,2,4, A. Pasquarelli5, F. Picollo2,1,4 1 Physics Department and NIS Inter-departmental Centre, University of Torino, Torino, Italy 2 INFN section of Torino, Torino, Italy 3 Department of Drug Science and Technology and NIS Inter-departmental Centre ,University of Torino, Torino, Italy 4 Consorzio Nazionale Inter-universitario per le Scienze fisiche della Materia (CNISM), Section of Torino 5 Institute of Electron Devices and Circuits, Ulm University, Ulm, Germany

Graphene and Silicon-Nanowire Field Effect Transistors for Neuronal Interfacing...................................295 Farida Veliev1, Anne Briancon-Marjolet2, Mireille Albrieux3, Dipankar Kalita1, Antoine Bourrier1, Zheng Han1, Vincent Bouchiat1, Cécile Delacour1 1 Institut Néel, Grenoble France 2 Laboratoire HP2, UJF-INSERM U1042, Grenoble, France 3 Grenoble Institut des Neurosciences, Inserm, Univ. Joseph Fourier, Grenoble, France

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MEA Technology Advances in Fabrication and Instrumentation______________________ 297 A CMOS-based Microelectrode Array System for Stimulation and Recording in Neural Preparations In Vitro..............................................................................................................................298 Takashi Tateno Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan

A Novell LabVIEWTM based Multi-Channel Closed- Loop Stimulator.....................................................300 Christiane Thielemann, Maike Stern, Christoph Nick and Robert Bestel University of Applied Sciences Aschaffenburg, BioMEMS lab, Germany

An Automated Method for Characterizing Electrode properties of High-Density Microelectrode Arrays.........................................................................................................................................302 Vijay Viswam1, David Jäckel1, Marco Ballini1, Jan Müller1, Milos Radivojevic1, Urs Frey2, Felix Franke1, Andreas Hierlemann1 1. ETH Zürich, Bio Engineering Laboratory, D-BSSE, Basel, Switzerland 2. RIKEN Quantitative Biology Center, Kobe, Japan

3D Electrodes for In-Cell Recording: Electrogenic Cell Membrane Deformation and Seal Resistance Estimation..................................................................................................................................304 Francesca Santoro, Jan Schnitker, Sabrina Ullmann, Andreas Offenhäusser CS-8/PGI-8 Institute of Bioelectronics, Forschungszentrum Jülich, Germany

A Benchtop Device for Prolonged Experiments with Microelectrode Arrays.............................................306 Regalia Giulia1, Achilli Silvia1, Biffi Emilia2, Menegon Andrea3, Ferrigno Giancarlo1, Pedrocchi Alessandra1 1 Electronics, Information and Bioengineering Department, Politecnico di Milano, Milan, Italy 2 Bioengineering Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy 3 Advanced Light and Electron Microscopy Bio-Imaging Centre, San Raffaele Scientific Institute, Milan, Italy

Development of a Stand-alone Integrated MEA Biochip System for Chronic Recordings........................308 Paolo De Pasquale1,2, Antonin Sandoz1, Igor Charvet1, Philippe Passeraub1, Michela Chiappalone2, Luc Stoppini1 1 Hepia, HES-SO Geneva, Switzerland 2 Istituto Italiano di Tecnologia (IIT), Genova, Italy

3D Multi Electrode Arrays..................................................................................................................................310 Martin Baca1, Heike Bartsch2, Tom Porzig2, Adam Williamson1, Andreas Schober1 1 FG Nanobiosystemtechnik, Technische Universität Ilmenau (TUI), Germany 2 FG Elektroniktechnologie, Technische Universität Ilmenau (TUI), Germany

The Influence of Medium Conductivity on Cellular Impedance Measurements with Field-Effect Transistor Arrays.............................................................................................................................312 Felix Hempel, Nathalie Bernhard, Jessica Ka-Yan Law, Anna Susloparova, Dieter Koppenhöfer, Xuan-Thang Vu and Sven Ingebrandt Department of Informatics & Microsystem Technology, University of Applied Sciences Kaiserslautern, Zweibruecken, Germany


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Organic Field-Effect Transistor Arrays as a Possible Low-Cost Alternative for In-Vitro Monitoring of Different Cell Cultures...............................................................................................314 Lotta Emilia Delle1, Anna Susloparova1, Xuan Thang Vu1,2, Olga Fominov1, Felix Hempel1, Jessica Ka-Yan Law1, Sven Ingebrandt1 1 Department of Informatics & Microsystem Technology, University of Applied Sciences Kaiserslautern, Zweibruecken, Germany 2 Present address: Department of Physics IA, RWTH Aachen University, Aachen, Germany

Oriented Cortical Networks in Polydimethylsiloxane Microchannel Scaffolds for Recordings from Network Modules and Individual Connections................................................................316 Rouhollah Habibey, Asiyeh Golabchi, Anilkrishna Konduri, Marina Nanni, Francesco Difato, Axel Blau Fondazione Istituto Italiano di Tecnologia (IIT), Dept. of Neuroscience and Brain Technologies (NBT), Genoa, Italy, www.iit.it

A Microfluidic Device for Selective Positioning of Invertebrate Neurons....................................................318 Katharina Krumpholz1, Akram El Hasni2, Andreas Neuhof1, Rudolf Degen1, Uwe Schnakenberg2, Peter Bräunig1, Katrin Bui-Göbbels1 1 RWTH Aachen University, Institute of Biology II, Aachen, Germany 2 RWTH Aachen University, Institute of Materials in Electrical Engineering (IWE 1), Aachen, Germany

Amplifier ASIC for Real 3D-Multichannel-MEA............................................................................................320 Thomas Just, Peter Husar Technische Universität Ilmenau, Germany

Simultaneous Electrical Investigation of Isolated Neurites Using a Neurite-Isolation Device as Neurite Regeneration Model...........................................................................322 Johann K. Mika1, Karin Schwarz2, Heinz Wanzenböck1, Petra Scholze2, Emmerich Bertagnolli1 1 Institute of Solid State Electronics, Vienna University of Technology, Vienna, Austria 2 Center for Brain Research, Medical University of Vienna, Vienna, Austria

Ultra-Low Cost Arrays of Printed Carbon Electrodes for Extracellular Recordings..................................324 Jan Schnitker, Alexey Yakushenko, Francesca Santoro, Fabian Brings, Bernhard Wolfrum, Andreas Offenhäusser ICS-8/PGI-8 Institute of Bioelectronics, Forschungszentrum Jülich, Germany

Stimulation Artifact Suppression Techniques for High-Density Microelectrode Arrays...........................326 A.Shadmani, J. Müller, P. Livi, M. Ballini, Y Chen, A. Hierlemann ETH Zürich, Department of Biosystems Science and Engineering, Basel, Switzerland

Large-Area, High-Density Arrays of Micro-Needles for Acute Slice Electrophysiology...........................328 D.E. Gunning1, P. Hottowy2, W. Dabrowski2, A. Sher3, J.M. Beggs4, C.J. Kenney5, D.A. Young1, A.M. Litke3 and K. Mathieson1 1 Institute of Photonics, SUPA, University of Strathclyde, Glasgow, UK 2 AGH University of Science and Technology, Krakow, Poland 3 SCIPP, University of California Santa Cruz, Santa Cruz, California, USA 4 Biocomplexity Institute, Indiana University, Bloomington, Indiana, USA 5 SLAC National Accelerator Laboratory, Menlo Park, California, USA

A Hybrid Multi Optrode Electrode Array for Optogenetics..........................................................................330 Luis Hoffman1, Alexandru Andrei1, Robert Puers1,2, George Gielen1,2, Dries Braeken1 1 Imec, Leuven, Belgium 2 ESAT, KULeuven, Leuven, Belgium

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Image Processing Based Quantitative Analysis of MEA Cultured Neurons................................................332 João Fernando Mari1,2, José Hiroki Saito2,3, Amanda Ferreira Neves5, Celina Monteiro da Cruz Lotufo4, João-Batista Destro-Filho4, Alberto Pasquarelli6 1 Universidade Federal de Viçosa, Campus Rio Paranaíba, Brazil 2 Department of Computer Science, Federal University of São Carlos, Brazil 3 Faculty of Campo Limpo Paulista, Brazil 4 Federal University of Uberlândia, Campus Santa Mônica, Brazil 5 Department of Structural and Functional Biology, University of Campinas, Brazil 6 University of Ulm, Germany

MEA Technology Culture Techniques___________________________________________ 334 Long-Term Cultivation and Recording from Organotypic Brain Slices on High-density Micro-electrode Arrays...............................................................................................................335 Wei Gong1, David Jäckel1, Jan Müller1, Michele Fiscella1, Milos Radivojevic1, Douglas Bakkum1, Felix Franke1, Frederic Knoflach2, Beat Gähwiler3, Branka Roscic1, Thomas Russell1, Andreas Hierlemann1 1 Bio Engineering Laboratory, D-BSSE, ETH Zürich, Basel, Switzerland 2 F. Hoffmann-La Roche, Basel, Switzerland 3 University of Zürich, Brain Research Institute, Zürich, Switzerland

Four-Layer Closed-Loop Neuronal Networks..................................................................................................337 Gregory J. Brewer1, Liangbin Pan2, Sankaraleengam Alagapan2, Eric Franca2, Thomas De- Marse2, Bruce C. Wheeler2 1 University of California Irvine, USA 2 University of Florida, Gainesville, FL USA

3D Neural Networks Coupled to Planar Microtransducer Arrays: An Innovative In-Vitro Experimental Model for Investigating Network Dynamics..................................339 Monica Frega1, Mariateresa Tedesco1, Paolo Massobrio1, Mattia Pesce2, Sergio Martinoia1 1 Neuroengineering and Bio-nano Technology Group (NBT), Department of Informatics Bioengineering Robotics and System Engineering, University of Genova, Genova, Italy 2 Department of Neuroscience and Brain Technologies, Italian Institute of Technology (IIT), Genova, Italy

Directed Neuronal Networks on Multi-Electrode Arrays..............................................................................341 László Demkó, Csaba Forró, Harald Dermutz, János Vörös Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, ETH Zurich, Switzerland

Human Pluripotent Stem Cell Derived Neural Cells Obtained Either Using Adherent or Neurosphere Based Differentiation Have Ability to Form Spontaneously Active Neuronal Networks..............................................................................................................................................342 Tiina Joki1, Pauliina Salonen2, Tarja Toimela2, Riikka Äänismaa1, Timo Ylikomi2, Susanna Narkilahti1 1 NeuroGroup, BioMediTech, University of Tampere, Tampere, Finland 2 Finnish Centre for Alternative Methods (FICAM), University of Tampere, Tampere, Finland

Design and Fabrication of a Second-Generation Perfusion Cap for Enhanced Optical Access and Manipulation of Cell Cultures.......................................................................344 Anil Krishna Konduri1, Dirk Saalfrank2, Rouhollah Habibey1, Asiyeh Golabchi1, Marina Nanni1, Axel Blau1 1. Dept. of Neuroscience and Brain Technologies (NBT), the Italian Institute of Technology (IIT), Genoa, Italy, www.iit.it 2. Dept. of Informatics and Microsystem Technology, University of Applied Sciences Kaiserslautern, Zweibrücken, Germany, www.fh-kl.de


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Primary and Stem-Cell Derived Cardiac Myocyte Cultures__________346 Keynote Lecture: Combined MEA Recording and Ca-Ratiometry in hiPSC-Derived Cardiomyocytes for Cardiac Safety Assessment...................................................................347 Georg Rast Drug Discovery Support, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany

Oral Presentation: Human Stem-Cell Derived Neuronal Cell Based In Vitro Platforms for Neurotoxicity and Disease Modelling...............................................................................................................348 Susanna Narkilahti NeuroGroup, BioMediTech, University of Tampere, Tampere, Finland

Comparative Assessment of Different Types of Human Stem Cell Derived Cardiomyocytes for Predictive Electrophysiological Safety Screening - Using Microelectrode Arrays (MEA).........................351 Udo Kraushaar1, Dietmar Hess3, Hua Rong Lu2, David Gallacher2 and Elke Guenther1 1 NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany 2 Janssen Pharmaceutica NV, Beerse, Belgium 3 NMI-TT GmbH, Reutlingen, Germany

On-Chip Measuring of Drug-Induced Cardiac Action Potential Elongation of Single Cells with Patch Clamp Resolution....................................................................................................352 Danny Jans, Ben Huybrechts, Simon Schaerlaeken, Andim Stassen, Luis Hoffman, Jordi Cools, Geert Callewaert, Dries Braeken imec, Leuven, Belgium

Pharmacological Response of a Human Induced Pluripotent Stem Cell-Derived Neuron and Astrocyte Co-Culture...........................................................................................................................................354 Aoi Odawara1,3, Masao Gotoh1, Ikuro Suzuki2 1 School of Bioscience and Biotechnology, Tokyo University of Technology, Hachioji, Tokyo, Japan 2 Department of Electronics and Intelligent Systems, Tohoku Institute of Technology, Japan 3 Japan Society for the Promotion of Science, Tokyo, Japan

Human Embryonic and Induced Pluripotent Stem Cells Form Functional Neuronal Networks in MEA................................................................................................356 Ylä-Outinen Laura, Hannuksela Eija, Narkilahti Susanna NeuroGroup, BioMediTech, University of Tampere, Tampere, Finland

A Novel AFM-Based Technique for Measuring Coupled Phenomena in Cardiac Myocytes....................358 Caluori Guido, Andrade_Caicedo Henry 1,2, Tedesco Mariateresa1, Raiteri Roberto1 1 Department of Informatics Bioengineering and Systems Engineering, Università di Genova, Genova, Italy 2 Centro de Bioingeniería; Universidad Pontificia Bolivariana, Medellín, Colombia

Author Index_______________________________________________361

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Symposium on Current Topics in Neurotechnology

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1st Tübingen Symposium on Current Topics in Neurotechnology- from Basic Research to Medical Application

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Brain-Machine-Interfaces (BMI) in Paralysis and Behavioral Disorders N. Birbaumer1,2,3, G. Gallegos-Ayala1, R. Sitaram4, U. Chaudhary1, S. Soekadar1, A. Ramos-Murguialday1,5 1 Institute of Medical Psychology and Behavioral Neurobiology, University of Tuebingen, Tuebingen, Germany 2 Ospedale San Camillo, IRCCS, Istituto di Ricovero e Cura a Carattere Scientifico, Venezia – Lido, Italy 3 Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany and German Center for Diabetes Research (DZDe.V.), Neuherberg, Germany 4 Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA 5 Health Technologies Department, TECNALIA, Mikeletegi Pasalekua 1, 20009 San Sebastian, Spain

Abstract Results from three applications of BMIs are described and their scientific, clinical and societal impact evaluated. (1) BMIs for brain-communication in the locked-in patients (2) BMIs as a rehabilitation modality for severe chronic stroke (3) BMI-metabolic fMRI-neurofeedback in neuropsychiatric disorders

1 BMIs for brain-communication in the locked-in patients BMIs using EEG, implanted Electrocorticogram (ECoG) were used extensively for brain communication in patients with severe paralysis, mainly amyotrophic lateral sclerosis (ALS). Up to the locked-in (LI) stage different types of EEG-ECoG-BMIs were used successfully to select letters or answers with brain oscillations (mainly sensorimotor rhythm, SMR) or cognitive event-related potentials from a computer menue. However, attempts to use BMI in the completely locked-in-syndrome (CLIS) were largely unsuccessful despite intensive research efforts. The first successful attempt in a CLIS patient using nearinfrared-spectroscopy (NIRS) BMI-systems will be described (Gallegos-Ayala et al 2014).

2 BMIs as a rehabilitation modality for severe chronic stroke A controlled outcome study of severe chronic stroke patients without residual hand mobility and without any available treatment modality left was recently published by this group (Ramos et al 2013) using a non-invasive extensive BMI-training with a neuroprosthetic device driven by sensorimotor brain oscillations (SMR) provides the first evidence of the clinical usefulness of BMI in this patient group. Reasons for this successful behavioral change and productive brain reorganization are presented and the necessity of future invasive BMI based on direct recordings from motor neurons comparable to those of Hochberg et al (2006, 2012) presented here by J. Donoghue in severe chronic stroke is emphasized.

3 BMI-metabolic fMRIneurofeedback in neuropsychiatric disorders Neurofeedback of EEG oscillations was used as a behavioral training-approach with considerable success in attentional disorders and epilepsy. Recently real-time-functional magnetic resonance (rt-fMRI) neurofeedback was developed by the Tuebingen group and tested in several neuropsychiatric disorders such as psychopathy, addiction, schizophrenia, obsessivecompulsive disorder and obesity (see Birbaumer et al 2013 for a review). Rt-fMRI-neurofeedback allows self-regulation of circumscribed well-defined cortical and subcortical brain systems and their connectivity. Some recent results in addiction are reported and the problem of brain-behavior associations related to BMI-training discussed. Acknowledgement Supported by Deutsche Forschungsgemeinschaft DFG (Koselleck BI 195/69-1), German Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZDe.V.,01GI0925), VW, Baden-Württemberg Stiftung, EU-FP7 and HORIZON 2020 (WAY, BRAINTRAIN, ERC to Rosenstiel & Birbaumer), CIN, FORTUNE (Faculty of Medicine, Univ. of Tuebingen).


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Combination of brain-machine interfaces and non-invasive brain stimulation - from basic science to clinical applications S. R. Soekadar1,2*, Matthias Witkowski1,2, Niels Birbaumer2,3,4 1

University Hospital of Tübingen, Applied Neurotechnology Lab, Department of Psychiatry and Psychotherapy, Tübingen Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany Ospedale San Camillo, IRCCS, Istituto di Ricovero e Cura a Carattere Scientifico, Venezia – Lido, Italy 4 Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany and German Center for Diabetes Research (DZDe.V.), Neuherberg, Germany * Corresponding author. E-mail address: [email protected] 2 3

Abstract Brain-machine interfaces (BMI) translate electric, magnetic or metabolic brain activity into control signals of external devices or machines, and allow restoration of communication or versatile control of prosthetic limbs or robots. By providing contingent feedback of brain activity, BMI systems can be used as training tools to augment these activities resulting in recovery of brain function. However, learning to control BMI systems can be cumbersome, particularly for individuals with brain lesions. Also, most BMI systems use sensory feedback for operant conditioning of BMI control-related brain activity, but do not provide a direct bidirectional link independent of afferent pathways. Based on previous work showing that non-invasive brain stimulation (NIBS) can improve learning while allowing for direct modulation of brain activity, the combination of NIBS and BMI seemed particularly desirable. Here we present the most recent technological advances developed in a collaborative research effort between the National Institutes of Health (USA) and the University of Tübingen that have successfully established combined NIBS and BMI. Different stimulation paradigms and recording methods, e.g. magnetoencephalography (MEG), electroencephalography (EEG) or functional near-infrared spectroscopy (NIRS), will be introduced and their clinical applications in neurological and psychiatric disorders described. Finally, current challenges and future developments will be discussed.

1 Introduction While it was known for centuries that application of electric currents to the brain can regulate mood, cognition and behavior, the relevance of brain stimulation for both, basic and clinical science has substantially increased in the last two decades. Supported by the recent development of tools allowing systematic investigation of the physiological basis of such brain stimulation effects, its recent success may also relate to the potential specificity and immediacy of the intervention compared to other, e.g. pharmacological approaches. Besides invasive stimulation techniques, such as deep brain stimulation (DBS) or motor cortex stimulation (MCS), non-invasive brain stimulation (NIBS), including transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) are increasingly used. For instance, it was shown that tDCS, i.e. the application of weak transcranial direct currents at intensities of 1-2mA, can improve learning and skill consolidation throughout different domains. Moreover, when applied over the ipsilesional motor cortex of chronic stroke patients, reaction time and pinch force of the affected hand increased. However, simultaneous application of electric currents to the head during assessment of electric or magnetic brain oscillations was considered unfeasible 30

due to stimulation artifacts. Recently, we overcame this limitation and established various paradigms allowing for combined application of transcranial electric currents during BMI control.

2. Combination of BMI and brain stimulation In a first study conducted at the National Institute of Neurological Disorders and Stroke (NINDS, NIH, USA), it was investigated whether application of tDCS over the primary motor cortex (M1) can regulate the ability to control sensorimotor rhythms (SMR, 8-15Hz) used to control an orthotic device. 30 participants received either anodal, cathodal or sham stimulation prior to a daily BMI training performed over one week. At the end of the training, BMI control of participants who received anodal stimulation was superior to those who trained under cathodal stimulation. One month after the end of training, BMI control was still better in the group that received anodal stimulation compared to the other groups [1]. As several studies indicated that timing of stimulation relative to training can influence the stimulation effect, development of new strategies allowing simultaneous or brain state-dependent stimulation promises


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to improve applicability and effectiveness of BMI training protocols. Recently, feasibility of simultaneous tDCS during EEG-based BMI control was demonstrated and its limitations outlined [2]. While use of EEG limits the possibility to reconstruct brain activity of regions immediately underneath the stimulation electrode, another strategy recently introduced by the same group allows for in vivo assessment of neuromagnetic brain oscillations of brain regions directly below the stimulation electrode [3]. Using this paradigm, Soekadar et al. [4] showed for the first time that a chronic stroke patient without residual finger movements can utilize SMR of the M1 hand knob to control an orthotic device to perform grasping motions while this region, the ipsilesional M1, underwent anodal tDCS (Figure 1). This new strategy may lead to the refinement of existing stimulation protocols and to a better understanding of the relationship between brain physiology, cognition and behavior, particularly in individuals with neurological and psychiatric disorders.


independent of afferent pathways, and thus become an effective novel treatment approach for various neurological and psychiatric disorders. Acknowledgement Supported by Deutsche Forschungsgemeinschaft DFG (Koselleck BI 195/69-1, NIH-DFG Research Career Transition Award to SRS, SO932-2), German Federal Ministry of Education and Research (BMBF, grant number 01GQ0831, 16SV5838K), the BMBF to the German Center for Diabetes Research (DZDe.V., grand number 01GI0925), VW, Baden-Württemberg Stiftung, EU-FP7 and HORIZON 2020 (WAY, grant number 288551, BRAINTRAIN, ERC to Rosenstiel & Birbaumer), CIN and FORTUNE (Faculty of Medicine, University of Tübingen). References [1]

[2]

[3]

Soekadar SR, Witkowski M, Garcia-Cossio E, Birbaumer N, Cohen LG (2014b) Learned EEG-based brain self-regulation of motor-related oscillations during application of transcranial electric brain stimulation: feasibility and limitations. Front Behav Neurosci 8:93 Soekadar SR, Witkowski M, Garcia-Cossio E, Birbaumer N, Robinson SE, Cohen LG (2013a) In vivo assessment of human brain oscillations during application of transcranial electric currents. Nature Communications 4:2032 Soekadar SR, Witkowski M, Robinson SE, Birbaumer N (2013b) Combining electric brain stimulation and sourcebased brain-machine interface (BMI) training in neurorehabilitation of chronic stroke. Journal of the Neurological Sciences 333-e542

Fig. 1. Illustration of simultaneous electric brain stimulation during in vivo assessment of neuromagnetic brain oscillations using wholehead magnetoencephalography (MEG) [2]. As neuromagnetic brain activity passes through the stimulation electrodes, reconstruction of cortical activity of brain areas immediately below the stimulation electrode is possible.

3 Conclusion Systems that combine BMI with direct, frequency tuned or brain-state dependent electric brain stimulation are novel and powerful tools to investigate the link between neurophysiology, brain function and behavior. Besides fostering neuroplasticity in the context of BMI training paradigms, this combination can provide direct bi-directional brain-machine interaction


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Pharmaco-TMS-EEG: A Novel Approach for Probing GABAergic Activity in Human Cortex Ulf Ziemann, Isabella Premoli, Florian Müller-Dahlhaus Dept. Neurology & Stroke, and Hertie Institute for Clinical Brain Research, University of Tübingen, Germany

Abstract TMS-EEG, i.e. the combination of transcranial magnetic stimulation (TMS) with electroencephalography (EEG) has only recently become available because TMS-compatible EEG amplifiers had to be developed. TMS-EEG allows for recording responses directly from the brain to highly synchronized input by TMS. This is a major advance compared to conventional electromyographic (EMG) recording of muscle responses elicited by TMS of motor cortex (TMS-EMG). TMS-EEG reveals a number of TMS-evoked EEG responses (TEPs) that may represent brain excitability and connectivity, but much of the underlying physiology is still unknown. This talk will give an introduction into this novel technique and, in addition, will present some very recent data from our group where TMS-EEG was combined with pharmacology to characterize the pharmaco-physiology of TEPs.

1 Background / Aims

4 Conclusion/Summary

Combining transcranial magnetic stimulation (TMS) and electroencephalography (EEG) constitutes a powerful tool to directly assess human cortical excitability and connectivity (Ilmoniemi et al., 1997; Ilmoniemi and Kicic, 2010; Ziemann, 2011). TMS of the primary motor cortex elicits a sequence of TMSevoked EEG potentials (TEPs) (Bonato et al., 2006). It is currently speculated that inhibitory neurotransmission through gamma-amino butyric acid type A receptors (GABAARs) modulates early TEPs (< 50ms after TMS), whereas gamma-amino butyric acid type B receptors (GABABRs) play a role for later TEPs (at around 100ms after TMS) (Rogasch and Fitzgerald, 2013). However, the physiological underpinnings of TEPs have not been directly tested yet.

In conclusion, pharmaco-TMS-EEG allows study of the effects of CNS-active drugs directly on cortical activity. Findings demonstrate a differential effect of GABAAergic versus GABABergic neurotransmission on specific TEP components. Therefore, TMS-EEG may be used to study GABAAergic and GABABergic inhibition in neurological and psychiatric disorders, in which abnormal cortical inhibition has been implicated in the pathophysiology, such as epilepsy or schizophrenia. In addition, pharmaco-TMS-EEG offers the opportunity to study the actions of CNS-active drugs on human cortex in greater detail than it is hitherto possible with TMS or EEG alone. Future studies will have to address systematically the effects of other neurotransmitter systems (glutamate, dopamine, norepinephrine, serotonin, acetylcholine) on TEPs.

2 Methods / Statistics In a recent series of experiments (Premoli et al., 2014) we have studied the role of GABAAR/GABABR activation for TEPs in healthy subjects using a pharmaco-TMS-EEG approach. We tested the effects of a single oral dose of alprazolam or diazepam (classical benzodiazepines acting as allosteric positive modulators at α1-, α2-, α3- and α5subunit-containing GABAARs), zolpidem (a hypnotic acting as positive modulator with high affinity at the α1-GABAAR), and baclofen (a GABABR agonist) on TEP amplitudes in double-blinded, placebocontrolled, crossover studies.

References [1]

[2] [3]

[4]

[5]

3 Results Alprazolam and diazepam increased the amplitude of the negative potential at 45ms after stimulation (N45) and decreased the negative component at 100ms (N100), whereas zolpidem increased the N45 only. In contrast, baclofen specifically increased the N100 amplitude.

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[6]

Bonato C, Miniussi C, Rossini PM (2006) Transcranial magnetic stimulation and cortical evoked potentials: a TMS/EEG co-registration study. Clin Neurophysiol 117:16991707. Ilmoniemi RJ, Kicic D (2010) Methodology for combined TMS and EEG. Brain topography 22: 233-248. Ilmoniemi RJ, Virtanen J, Ruohonen J, Karhu J, Aronen HJ, Naatanen R, Katila T (1997) Neuronal responses to magnetic stimulation reveal cortical reactivity and connectivity. Neuroreport 8:3537-3540. Premoli I, Castellanos N, Rivolta D, Belardinelli P, Bajo R, Zipser C, Espenhahn S, Heidegger T, Müller-Dahlhaus F, Ziemann U (2014) TMS-EEG signatures of GABAergic neurotransmission in the human cortex. J Neurosci 34:5603– 5612. Rogasch NC, Fitzgerald PB (2013) Assessing cortical network properties using TMS-EEG. Human brain mapping 34:16521669. Ziemann U (2011) Transcranial Magnetic Stimulation at the Interface with Other Techniques: A Powerful Tool for Studying the Human Cortex. The Neuroscientist: a review journal bringing neurobiology, neurology and psychiatry 17:368 - 381.


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Near-Infrared Spectroscopy (NIRS) - a Promising Tool for Neurofeedback Andreas J. Fallgatter, Beatrix Barth, Ann-Christine Ehlis Dept. of Psychiatry, University of Tübingen, Germany

Abstract Pharmacotherapy, mainly with stimulants and supplemented with psychotherapy are the standard therapies of ADHD in childhood and adulthood. As many patients don’t accept pharmacotherapy for a sufficient time and in an adequate dose, alternative treatment starategies are urgently needed. In this respect, the development of neurofeedback training protocols in order to improve altered brain function in ADHD are proposed. In this talk the current status of NIRS-based neurofeedback protocols in our research group will be presented.

1 Backround / Aims Hypofrontality, a decrease in frontal lobe activity that is associated with a number of clinical symptoms and psychiatric disorders, has been demonstrated in a wide range of fNIRS studies with psychiatric patients, and in particular in patients with ADHD. In more detail, dysfunctions of lateral prefrontal brain regions have been described in groups of ADHD patients during tasks probing working memory, response inhibition, word fluency and delay discounting. These findings from brain imaging may be used to directly identify hypofunctional brain regions underlying a disorder like ADHD and, in a second step, to specifically increase this brain function. This can be done by transforming this brain activity in a visible signal, e.g. an aeroplane flying on a computer screen, and allowing the subject to modulate the brain activity, e.g. by increasing the height of the aeroplane on the screen. This strategy is called neurofeedback.

niques are its limited spatial as well as depth resolution and its restriction to cortical areas. Preliminary results for the application of NIRS as a neurofeedback method in children and adults with ADHD are shown. Advantages and disadvantages compared to other neurofeedback technologies like EEG or fMRI are discussed.

4 Conclusion/Summary Further technical development and a broadened implementation are necessary in order to develop NIRS neurofeedback as an alternative or additional treatment option for ADHD, References [1]

[2]

2 Methods / Statistics Based on such findings.Functional Near-Infrared Spectroscopy (fNIRS) can be used for an in-vivo assessment of activation changes in brain tissue. Due to its simple and quick applicability as well as the absence of side effects, fNIRS is particularly well tolerated by psychiatric patients and can hence markedly contribute to the understanding of the neurobiological basis of psychiatric disorders. Validity and reliability of fNIRS measurements to assess task-related cognitive activation have been repeatedly confirmed among healthy subjects.

[3]

[3]

[4]

[5]

3 Results Beyond that, the application of fNIRS in order to detect altered cortical oxygenation in psychiatric patients during cognitive tasks has been highly intensified over the last two decades. Despite these encouraging findings in ADHD and the variety of beneficial properties of the method, the most apparent disadvantages of NIRS compared to other imaging tech-

[6]


Ehlis, A.-C., Schneider, S., Dresler, T., Fallgatter, A.J. Application of functional near-infrared spectroscopy in psychiatry. In press: NeuroImage Haeussinger, F.B., Dresler, T., Heinzel, S., Schecklmann, M., Fallgatter, A.J., Ehlis, A.-C. Reconstructing functional nearinfrared spectroscopy (fNIRS) signals impaired by extracranial confounds: An easy-to-use filter method. In press: Neuroimage Heinzel, S., Haeussinger, F.B., Hahn, T., Ehlis, A.-C., Plichta, M.M., Fallgatter, A.J. Variability of (functional) hemodynamics as measured with simultaneous fNIRS and fMRI. In press: Neuroimage Rea, M., Rana, M., Lugato, N., Terekhin, P., Gizzi, L., Brötz, D., Fallgatter, A., Birbaumer, N., Sitaram, R., Caria, A. Lower limb movement preparation in chronic stroke: towards anfNIRS-BCI for gait rehabilitation. In press: Neurorehabilitation and Neural Repair Schecklmann, M., Ehlis, A.-C., Plichta, M.M., Romanos, J., Heine, M., Boreatti-Hümmer, A., Jacob, C., Fallgatter, A.J. Diminished prefrontal oxygenation with normal and aboveaverage verbal fluency performance in adult ADHD. Journal of Psychiatric Research 43:98-106, 2008. Schecklmann, M. Romanos, R., Bretscher F., Plichta M.M., Warnke A., Fallgatter, A.J. Prefrontal oxygenation during working memory in ADHD. Journal of Psychiatric Research 44:621-628, 2010. Schneider, S., Christensen, A, Hausinger, F.B., Fallgatter, A.J., Giese, M.A., Ehlis, A.-C. Show me how you walk and I tell you how you feel – A functional near-infrared spectroscopy study on emotion perception based on human gait. In press: NeuroImage

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Machine Learning Algorithms for Brain Signal Processing Martin Spüler1, Carina Walter1, Armin Walter1, Martin Bogdan1*,2, Wolfgang Rosenstiel 1 1 Technische Informatik, Eberhard-Karls-Universität Tübingen, Germany 2 Technische Informatik, Universität Leipzig, Germany * Corresponding author. E-mail address: [email protected]

Abstract The presented paper gives a brief overview of the activities of the research group “artificial neural networks and machine learning” related to current projects on brain signal processing and the application of machine learning techniques. The research group’s reputation regarding artificial neural networks and machine learning is based on the application, modification and further development of these algorithms in a manifold of different areas. Here we focus on machine learning methods for brain signal processing related to Brain-Computer Interfaces (BCIs), either for the detection of mental states, the use as communication device and for stroke rehabilitation. As a related aspect, we also demonstrate some works regarding the use of machine learning methods for brain stimulation.

1 Introduction The research group „artificial neural networks and machine learning” has previously worked on the application, modification and development of machine learning and signal processing algorithms in different areas. For example, machine learning has been successfully applied to several projects with industrial cooperation such as quality control in chip fabrication, the prediction of workload in mainframes or the interpretation and processing of sensory data in chemical and biomedical applications. Despite these successful applications in the industrial area, most of the reputation has been achieved due to the working focus of the research group being in the field of biomedical applications. To name a few, the feasibility of controlling commercial limb prosthesis by means of recorded nerve signals has been proved. Also, the control of a pig’s limb due to functional neuroelectrical stimulation whereas stimulation patterns were provided by artificial neural nets have been demonstrated. On the way towards the interpretation of multi-electrode recordings in the central nervous system (CNS), remarkable advances have been achieved in the field of source separation as well as in the field of spike sorting. Together with the Hertie-Institute for Clinical Brain research (Systems Neurophysiology) outstanding results have been achieved in stimulating the CNS of the barrel cortex of rats in order to imprint neural activity. A further internationally renowned outcome of these close interdisciplinary cooperations are algorithms for feature extraction in EEG (electroencephalogram) and ECoG (electrocorticography) recordings as well as automatic electrode discrimination towards their impact of information content regarding communication possibilities in BCI. Currently, we are working on several funded projects to improve the processing of brain signals as well as functional electrical stimulation using machine learning methods.

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2 Methods for Brain Signal Processing In the following sections, we will present a selection of our recent work and give an overview how and where machine learning methods can be applied for brain signal processing. Our work has a strong focus on Brain-Computer Interface (BCI) [1] applications, where we develop methods to improve BCIs as a communication device. Further we investigate the use of machine learning to improve rehabilitation of stroke patients and for detection of mental states (i.e. workload = WL). Lastly, we will show how machine learning methods can be used in the context of functional brain stimulation.

2.1 Machine Learning to improve BrainComputer Interfacing One problem when using BCIs for communication is the non-stationarity of brain signals, meaning that the recorded brain signals change over time that leads to decreased performance over prolonged BCI usage. To improve BCI performance, spatial filters generated by Canonical Correlation Analysis can be used [2] to enhance classification accuracy of evoked or event related potentials, which can be applied to a variety of different brain signals used for BCI control (i.e. P300, ErrP, c-VEP). Further, we investigated the use of new BCI paradigms, like the use of code-modulated visual evoked potentials (c-VEPs) [3], to find new methods that can lead to faster BCI communication. A screenshot of a spelling application being controlled by the c-VEP BCI is shown in figure 2, but the c-VEP system is not limited to this application and can be used to emulate mouse and keyboard on the


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level of the operating system which theoretically allows the control of arbitrary applications.


and user to find out under which constraints the positive effect of BCI adaptation can reach its full potential [10].

2.2 BCI for mental state detection

Fig. 1. Screenshot of the c-VEP BCI spelling application. A: screenshot during a trial. The letter Ö (lower right corner) serves as a backspace and allows the user to correct mistakes. B: screenshot of the letter N being selected and the other characters being grayed out to indicate a selection.

As a particularly promising approach to improve the robustness of a BCI system, several adaptive methods have been developed. We have shown that methods for adaptation can either be applied to the data before or in the classification process itself by implementing adaptive machine learning methods. To reduce non-stationarity in the data we introduced a method that uses principal component analysis (PCA) to extract non-stationary components in the brain signals and use a covariate shift minimization technique to reduce their effect. We could show that the reduction of non-stationarity leads to significantly improved BCI performance [4]. By using an incremental training procedure for a Support Vector Machine (SVM) and combining it with a co-training approach, we realised an online adaptation of the SVM-classifier that is fast enough to work real-time even with high-dimensional MEG-data (275 channels). This approach was also shown to improve BCI performance significantly as well compared to non-adaptive methods [5]. While we have shown that error-related potentials can be detected in the brain signals and used to automatically correct erroneous responses of the BCI [6], we further used the detection of error-related potentials to improve adaptation of the c-VEP BCI. In combination with improved spatial filtering and the introduction of a one-class SVM for the classification of the c-VEP [7], a system which enables the user to write more than 20 error-free letters per minute have been developed which currently is the fastest noninvasive BCI system [8] and therefore an important step towards better BCI-driven communication. Adaptive machine learning methods cannot only be used to improve BCI performance, but also to enable an unsupervised calibration. We have shown for the first time that an unsupervised online calibration of a BCI is possible [9] and argue that it potentially could be used for a new approach regarding the communication with complete locked-in patients Since the use of adaptive methods opens many new questions about the interaction between the adaptive BCI system and the learning user, we addressed those questions by creating a simulation environment to easily explore the interaction between adaptive BCI

The detection of mental states (i.e. effort of WL) using machine learning algorithms is the goal of our WissenschaftsCampus project, in cooperation with Prof. Gerjets (Knowledge Media Research Center, Tübingen). As the amount of WL during learning should be held within an optimal range of learner’s memory capacity, our project is developing EEG based WL detection as well as prediction methods. These methods could be utilized in learning environments to adapt instructional material to the students individual WL online and thereby support the learning process successfully. In contrast to BCI applications, it is not feasible for this application to use data from the same subject and same task for classifier training as well as testing since the subject may learn during training time. Therefore we developed two new classification methodologies based on EEG-signals, cross-task classification as well as cross-subject regression. Prior to the application of machine learning methods characteristics in EEG-signals that reliably represent WL states has to be found. The power spectrum in θ- and α-frequencies composed of frontal as well as parietal electrodes (figure 2), can be used as features for WL classification.

Fig. 2. Left: r²-values indicating the locations with highest differentiations while solving easy and difficult diagram tasks. Right: Spectral power plot for electrodes F3, Fz, F4, FC9, FC10, CP1, CP2, P3, Pz, P4) → significant increased θ-power as well as significant reduced α-power in diagram tasks.

Using cross-task classification, where a SVM was trained on EEG-data recorded while participants had to solve two working memory tasks (reading span, numerical n-back) and tested on EEG-data recorded during solving complex mathematical tasks, lead to accuracies around chance level [11]. To improve the relatively poor cross-task classification results, we used subjective cognitive-load ratings for defining classes, to reduce the varying difficulty across the tasks. A significant improvement of classifier performance (p < 0.05, permutation test) could be reached. The classifier was able to classify the subjectively eas-


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ier versus harder set of math problems with a mean classification accuracy of 73 %. While we have shown that modified cross-task classification can be used for WL detection, we further used a cross-subject regression method, for WL prediction. Therefore we applied a cross-subject linear regression method, using data from the same task but different subjects to calibrate the classifier. As most of the EEG-based WL classifications are subjectspecific, this approach would enable training a classifier once that could handle multiple subjects. By applying linear regression cross-subjects a precise WL prediction could be reached (on average: Correlation Coefficient = 0.84). In an upcoming study, these results will be used in an online learning environment to predict the user's WL and adapt the presented exercises accordingly, to support students successfully in their learning process.

nology as an addition to physiotherapy in order to facilitate neuroplasticity near the affected brain area. Within the BCCI project we took this next step by directly coupling brain stimulation and the hand orthosis with a BCI that detects the intention to move the paralyzed limb from the brain signal, forming the first bidirectional brain-computer interface applicable for the restoration of movement [12,14]. The main schematic setup for the new BCCI system is shown in figure 3.

2.3 BCI for stroke rehabilitation One project in which we applied machine learning methods to improve BCI-based stroke rehabilitation is the “Bidirectional cortical communication interface” (BCCI) project led by Prof. Rosenstiel in cooperation with Prof. Birbaumer (Institute for Medical Psychology and Behavioral Neurobiology, University Hospital Tübingen) and Prof. Gharabaghi (Department of Neurosurgery, University Hospital Tübingen). Over the course of this ERC-funded project, we have developed a BCI-based rehabilitation system, which detects the movement intention of the patient and couples this intention with haptic feedback given by an orthosis moving the paralyzed hand of the patient. During this project, several stroke patients used this system for rehabilitation purposes. For the majority of the patients, the brain activity was measured non-invasively by EEG and the intention to move was detected by the BCI. Additionally, 3 patients were also implanted with epidural ECoG electrodes. Based on those ECoG recordings, we could show that up to 7 different types of intended movements from the same limb could be classified successfully. These findings can further be used to improve BCIguided rehabilitation, by not only coupling orthosis feedback to intended movement, but also giving different and more specific feedback for different movement intentions. Further, we could show that ipsilesional ECoG activity yields enough information to not only discriminate different movement intentions, but also to reconstruct movement trajectories.

2.4 Machine Learning and Brain Stimulation Since classical physiotherapy is often not very effective for stroke survivors with arm or hand paralysis and BCI-based rehabilitation might have its limits, new strategies are needed for rehabilitation. One idea to improve stroke rehabilitation is the use of brain stimulation techniques in combination with BCI tech36

Fig. 3. Schematic setup of the BCCI system for hand movement restoration: A classifier detects the patient's intention to move the paralyzed hand using EEG or implanted ECoG electrodes. The output of the classifier controls an orthosis moving the hand and a brain stimulator, either transcranial magnetic stimulation (TMS) or electrical stimulation over the implanted electrodes.

As the signals are highly affected by stimulation artifacts this combination of BCI with brain stimulation introduces new challenges when recording and analysing the brain signals. Therefore new methods are needed [12] that are able to deal with these artifacts and allow a reliable estimation of the power spectrum despite the presence of stimulation artifacts. Several chronic stroke patients were noninvasively treated with the combination of BCI and cortical stimulation, in addition to 3 stroke patients with a paralyzed hand that had been implanted with epidural electrocorticography (ECoG) electrodes. After 2-4 weeks of training with the BCCI system, 2 out of 3 implanted stroke patients regained control over some finger movements in the formerly paralyzed hand. Furthermore, we conducted studies on the effects of cortical stimulation and the decoding of movement intention from these patients to optimize the system for future studies. We found that information about stimulation parameters and the intended movement at the moment of stimulation is encoded in the stimulation-evoked neural activity. Using appropriate machine learning methods, this information can be decoded even for single stimulation pulses and be used to identify optimal stimulation parameters and time points per patient [13]. Also, a change of the evoked potentials due to the stimulation of the cortex can be shown. Figure 4 shows the development of evoked potentials depending over different electrode sites over 4 weeks of stimulation. Please note, that only some of the recording sites of the


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evoked potentials are changing during the 4 weeks whereas others don’t. This effect is not correlated with the distance from the stimulation electrode. Thus the hypothesis can be formulated, that due to the stimulation some effects on the neural connections have been realized [14].


[5]

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Fig. 4. Change of the evoked potentials over 4 weeks. Every diagram represents the corresponding electrode of the implanted 4x4 ECoG grid. The electrode without diagram represents the electrode used for the stimulation.

3 Summary This paper summarizes the recent research effort of our group regarding the use of machine learning methods for brain signal processing. We have shown different applications where the use of machine learning algorithms is beneficial by advancing knowledge in the corresponding research applications and in the area of BCI in general as well. Future work will head the same research directions by further enhancing the performance of the applications in question. Also, the adaption of the presented algorithms to other applications mainly in the biomedical field will be investigated.

[11]

[12]

[13]

[14]

stationarity in a MEG-based brain-computer interface. EURASIP Journal on Advances in Signal Processing, 2012:129. Spüler, M., Rosenstiel, W., Bogdan, M. (2012). Adaptive SVM-based classification increases performance of a MEGbased Brain-Computer Interface (BCI). In Artificial Neural Networks and Machine Learning–ICANN 2012, pp.669-676. Spüler, M., Bensch, M., Kleih, S., Rosenstiel, W., Bogdan, M., Kübler, A. (2012). Online use of error-related potentials in healthy users and people with severe motor impairment increases performance of a P300-BCI. Clinical Neurophysiology, 123(7),pp. 1328-1337. Spüler, M., Rosenstiel, W., Bogdan, M. (2012). One class SVM and Canonical Correlation Analysis increase performance in a c-VEP based Brain-Computer Interface (BCI). In Proceedings of 20th European Symposium on Artificial Neural Networks (ESANN 2012),pp. 103-108. Spüler, M., Rosenstiel, W., Bogdan, M. (2012). Online adaptation of a c-VEP Brain-Computer Interface (BCI) based on Error-related potentials and unsupervised learning. PloS one, 7(12),e51077. Spüler, M., Rosenstiel, W., Bogdan, M. (2013). Unsupervised Online Calibration of a c-VEP Brain-Computer Interface (BCI). In Artificial Neural Networks and Machine Learning– ICANN 2013, pp. 224-231. Spüler, M., Rosenstiel, W., Bogdan, M. (2012). Co-adaptivity in Unsupervised Adaptive Brain-Computer Interfacing: a Simulation Approach. In COGNITIVE 2012, The Fourth International Conference on Advanced Cognitive Technologies and Applications, pp. 115-121. Walter, C., Schmidt, S., Rosenstiel, W., Gerjets, P., Bogdan, M. (2013). Using Cross-Task Classification for Classifying Workload Levels in Complex Learning Tasks. In Proceedings of the 5th IEEE ACII 2013, pp.876-881. Walter, A., Murguialday, A., Spüler, M., Naros, G., Leão, M. T., Gharabaghi, A., Birbaumer, N., Rosenstiel, W., Bogdan, M. (2012). Coupling BCI and cortical stimulation for brainstate-dependent stimulation: methods for spectral estimation in the presence of stimulation after-effects. Frontiers in neural circuits, 6. Walter, A., Naros, G., Spüler, M., Gharabaghi, A., Rosenstiel, W., Bogdan, M. (2014). Decoding stimulation intensity from evoked ECoG activity. Neurocomputing, http://dx.doi.org/10.1016/j.neucom.2014.01.048 . Walter, A., Naros, G., Spüler, M., Rosenstiel, W., Gharabaghi, A., Bogdan, M. (2013). Dynamics of a Stimulation-evoked ECoG Potential During Stroke Rehabilitation - A Case Study. NEUROTECHNIX 2013 - International Congress on Neurotechnology, Electronics and Informatics, pp.241-243.

Acknowledgement We are grateful to thank the supporters of the presented projects, namely WissenschaftsCampus, the Land Baden-Württemberg, BMBF (BFNT F*T, Grant UTü 01 GQ 0831), DFG (KOMEG) and the European Research Council ERC (Grant 227632). References [1]

[2]

[3]

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Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., Vaughan, T. M. (2002). Brain–computer interfaces for communication and control. Clinical neurophysiology, 113(6), 767-791. Spüler, M., Walter, A., Rosenstiel, W., Bogdan, M. (2014). Spatial Filtering Based on Canonical Correlation Analysis for Classification of Evoked or Event-Related Potentials in EEG Data," IEEE Transactions on Neural Systems and Rehabilitation Engineering. Bin, G., Gao, X., Wang, Y., Li, Y., Hong, B., & Gao, S. (2011). A high-speed BCI based on code modulation VEP. Journal of neural engineering, 8(2), 025015. Spüler, M., Rosenstiel, W., Bogdan, M. (2012). Principal component based covariate shift adaption to reduce non-


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Implantable bioelectronic interfaces for diagnosis, therapy and rehabilitation Alfred Stett*, Volker Bucher, Claus Burkhardt, Martina Cihova, Katja Gutöhrlein, Massimo Kubon, Gorden Link, Rene von Metzen, Wilfried Nisch, Sebastian Röhler, Ramona Samba, Michael Weinmann, Martin Stelzle NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany * Corresponding author. E-mail address: [email protected]

Abstract Electrical implants like cardiac pacemakers and neuronal stimulators basically consist of a solid housing protecting the electronics and the energy storage. For applications, where only a small volume is available for inclusion of the implant like in the eye, ear and brain, and where cabling is not applicable, miniaturized, cable-free implants with wireless power supply and data transmission are required. The NMI together with its partners develops highly-integrated implantable systems for biosensor and electrical stimulation applications. For measurement and stimulation of biological signals the implants are equipped with multifunctional microelectrode arrays. The implant development aims at applications in diagnosis and therapy of neurological and metabolic disorders.

1 Background Electrically active implantable medical devices (AIMD) are utilized for the management of numerous disorders, like cardiac pacing, eyesight, epilepsy, headaches, hearing, movement disorders, chronic pain, psychiatric and neurobehavioral disorders, gastric motility urinary frequency and urgency, and more [1]. Recently, the need for devices that targets single cells of neural circuits in the peripheral nervous system for the treatment of diseased organ functions has been described [2]. With regard to personalized medicine, implantable electrochemical biosensors raise the possibility for continuous monitoring of clinical biomarkers by direct tissue measurements [3] and for new therapies of metabolic disorders like diabetes [4]. State-of-the-art AIMDs for long-term use like cardiac pacemakers and deep brain stimulators basically consist of a solid metal housing protecting the electronics and the energy storage, and the stimulation electrode which is connected to the electronic circuitry via a lead and a interconnect header (Fig. 1) [5]. For applications, where only a small volume is available for inclusion of the implant like in the eye [6], brain and subcutaneous metabolic monitoring, and where large batteries and cabling are not applicable, miniaturized and compact system architectures are required, containing means for on-board data management and wireless communication with external devices for energy and data transmission. A special challenge is posed by the demand for biocompatibility and safe long-term function.

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IC

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coating sensors actors

© N MI 2014

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Fig. 1. Schematic drawing of an active electronic implant, its components and interactions with the external technical and biological environment. The implant has to be biocompatible and biostable under the condition of use. IC integrated circuit, SMD surface mounted passive device, PCB printed circuit board.

Ongoing research activities at the NMI pursue the idea of highly-integrated and miniaturized electronic implants with cable-free system architectures and multifunctional interfaces for stimulation and recording of neuronal activity and electrochemical detection of ions and biochemical molecules.

2. Cable-free electronic implants 2.1 Flexible active board for intracranial recording For subchronic electrocorticography (ECoG) a polymer based system, which comprises a flexible printed circuit board (PCB) as well as the electronics and sensors without a hard shell, has been designed, manufactured and tested.


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Fig. 2. Active ECoG grid with 64 channels for cortical recordings with implemented analog-to-digital conversion, multiplexing, optical data communication and a telemetric link for power supply.

The substrate is a flexible polyimide with embedded conductors. After assembly of the electronic components, the implant was coated with approx. 11 µm Parylene C. The active part of the grid was additionally coated with a PECVD SiOX-layer and finally in silicone rubber to protect the thin encapsulation layers mechanically especially from the tools applied in the implantation surgery. The approach of an active, cable-free, flexible and very thin implant for ECoG recordings is a substantial progress compared to the standard passive grids that are well-established but possess the drawback of the percutaneous cable. Moreover, the MEMS based manufacturing technologies offer more design freedom than conventional, hand crafted electrode grids, especially towards much smaller electrode sites, enabling an improved spatial resolution, while the number of recording sites is easily expandable.

2.2 PEEK capsule for in vivo biosensor applications For subcutaneous electrochemical measurements a sensor capsule (Fig. 3) is being developed, where the electronic part of the system is housed in a sealed PEEK shell. The capsule contains amperometric, potentiometric and impedance sensors allowing the electrochemical measurement of the oxygen concentration, pHvalue and impedance in the vicinity of the implant. The implant system consists of application specific integrated circuits (ASICs) and microcontrollers for sensor readout, inductive power transmission and bidirectional data transfer, and a rechargeable battery. Miniaturization of the implant comprising high functionality and a wireless link to the external device has been obtained by defining economical operation modes, designing energy efficient electronic circuits and the compact integration of the board and battery in a small capsule intended for subcutaneous implantation.

Fig. 3. Implantable electrochemical biosensor capsule (diameter 30 mm, height 7 mm).

3 Multifunctional sensor arrays 3.1 Microelectrodes for neuromonitoring and stimulation Besides the encapsulated electronics, implants contain a number of contacts to record and stimulate cellular and molecular activity in the adjacent tissue. For this purpose we developed microelectrodes that are able to provide highly localized stimulation and to record cellular signals and local field potentials. Using MEMS technology, a wide variety of materials and insulators can be used to produce planar and 3D electrodes of small dimensions, high functionality and long-term stability (Fig. 4). For recording with a high signal-to-noise ratio, nano-columnar TiN electrodes are used, whereas for stimulation the high chargeinjection capability of nanoporous Ir/IrOx is employed. There is a strong demand for new materials to fabricate multifunctional microelectrodes that allow for recording, stimulation, and for electrochemical sensing of neuroactive chemical species [7]. As PEDOT–CNT composites are one of the most promising materials, we established a robust procedure for the production of PEDOT–CNT electrode arrays [8]. TiN

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Fig. 4. Microelectrodes for recording and stimulation.


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3.2 Electrochemical sensors For electrochemical monitoring a sensor array comprising microelectrodes for O2-, pH-value- and impedance measurement is available for integration into the implants (Fig. 5). An amperometric sensor, realized in a three electrode configuration with working (Pt), counter (Pt) and reference (Ag/AgCl) electrode, is employed to measure oxygen concentration. A potentiometric sensor in a two electrode configuration comprising a IrOx working and Ag/AgCl reference electrode is employed to measure the pH.

Contributions by and collaboration with Multi Channel Systems MCS GmbH and the SMART Implant consortium are kindly acknowledged. References [1] Krames, E.S., et al. What Is Neuromodulation? Chap. 1, p. 38. In: Krames E.S., Peckham P.H., Rezai A.R. (eds): Neuromodulation. Academic Press, 2009 [2] Famm, K.: A jump-start for electroceuticals. Nature (2013) 469:159-161 [3] Rogers M.L., Boutelle M.G.: Real-Time Clinical Monitoring of Biomolecules. Annual Review of An-alytical Chemistry (2013) 6:427-453 [4] Klonoff D.C.: The Benefits of Implanted Glucose Sensors. J Diabetes Sci Technol. (2007) 1(6): 797–800. [5] Peckham P.H., Ackermann D.M.: Implantable Neural Stimulators. Chap. 18, p. 215-228. In: In: Krames E.S., Peckham P.H., Rezai A.R. (eds): Neuromodulation. Academic Press, 2009 [6] Zrenner E. et al.: Subretinal electronic chips allow blind patients to read letters and combine them to words. Proc. R. Soc. B (2011) 278:1489–1497 [7] Samba R et al. Application of PEDOT-CNT microelectrodes for neurotransmitter sensing. Electroanalysis (2014) 26:548555 [8] Gerwig R et al.: PEDOT–CNT composite microelectrodes for recording and electrostimulation applications: fabrication, morphology, and electrical properties. Front. Neuroeng. (2012) 5:8.

Fig. 5. Sensorarray with amperometric, potentiometric and impedance sensors allowing the electrochemical measurement of the oxygen concentration, the pH and the impedance in the vicinity of the implant.

4 Conclusion/Summary Miniaturization of active, cable-free implants comprising high functionality and wireless links to external devices can be obtained by designing energy efficient electronic circuits and by using MEMS technology for fabrication of multifunctional sensor arrays and for their integration in compact substrates and housings to protect the electronics from body liquids. Those miniaturized implants will open up a whole new spectrum of health care applications, facilitating more specific diagnosis, more effective therapies and aids used on a daily basis by patients suffering e.g. from neurological, movement or metabolic disorders. Acknowledgement This work was supported by the Federal Ministry of Education and Research (BMBF) through grants no. 01GQ0834 (Bernsteinfocus Neurotechnology: Hybrid brain), no. 16SV3782 (incrimp project, Rahmenprogramm Mikrosysteme, Förderinitiative Intelligente Implantate), no. 16SV5009 (Begleitforschung Intelligente Implantate), no. 16SV5979K (SMART Implant, a project of the SSI platform in the Cluster microTEC Südwest).

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CMOS – Based Microelectrode Arrays (‘Neurochips’) for Simultaneous Stimulation and Detection Action Potentials at Sub-Cellular Resolution Günther Zeck1, Max Eickenscheidt, Lakshmi Channappa, Roland Thewes 2 1 Natural and Medical Sciences Institute at the University of Tübingen 2 Chair of Sensor and Actuator Systems, Faculty of EECS, TU Berlin, Berlin, Germany

Abstract The action of arbitrary electrical stimuli upon neurons embedded in the complex tissue environment can be revealed by simultaneous recording of the evoked activity. Towards such goal integration of stimulation and recording sites at high-spatial resolution is required. Here we present first results obtained with a newly developed CMOS-based microelectrode array (“Neurochips”) interfaced to either retina or hippocampal slices.

1 Backround / Aims

4 Conclusion/Summary

Microelectrode arrays (MEAs) based on integrated complementary metal oxide semiconductor (CMOS) technology have been introduced recently by our groups [1 – 3] towards efficient electrical stimulation and recording of neuronal activity at high spatial (~10 µm) and temporal resolution (~ 10 kHz). Although either stimulation or recording has been demonstrated a loss-free, bidirectional interfacing remained a challenge. Detection of the induced activity at a sub-millisecond timescale is desired to address the stimulation effect on single neurons or axons.

Capacitively coupled CMOS-based MEAs are an attractive tool to characterize the response of neurons in planar preparations to arbitrary electrical stimulus patterns. Such experiments may advance our understanding of the dynamic response properties of neuronal populations and lead to improved neuroprosthetic stimulation strategies. References [1]

[2]

2 Methods / Statistics A CMOS-based MEA comprising 4225 recording sites and 1024 stimulation sites has been developed [5] and tested with respect to detection and stimulation performance in neural tissue [6]. The recording site pitch is 16 µm or 32 µm, thus allowing for highresolution electrical imaging of the interfaced neurons or neural tissue. Ex vivo light-responsive mammalian retina as well as hippocampal slices were interfaced to the planar sensor surface of the MEA. Single cell activity and local field potentials are induced by means of capacitive stimulation. The induced activity (action potentials and local field potentials respectively) and the propagation thereof are recorded and evaluated.

[3]

[4]

[5]

[6]

Eversmann, B., et al. (2004): A 128 × 128 CMOS biosensor array for extracellular record-ing of neural activity. IEEE Journal of Solid-State Circuits, 38 (12), 2306-2317. Zeck et al. (2011) Axonal transmission in the retina introduces a small dispersion of relative timing in the ganglion cell population response Plos One 6(6):e20810. doi:10.1371/journal.pone.0020810 Eickenscheidt, M. et al (2012) Electrical stimulation of retinal neurons in epiretinal and subretinal configuration using a multicapacitor array J. Neurophysiol. 107(10):2742-55. Eickenscheidt, M. and Zeck, G. (2014) Action potentials in retinal ganglion cells are initiated at the site of maximal curvature of the extracellular potential. J.Neural.Eng. 11(3):036006 Bertotti et al . (2014) A Capacitively-Coupled CMOS-MEA with 4225 Recording Sites and 1024 Stimulation Sites. Proc. of 9th Int. Meeting on Substrate-Integrated Microelectrodes Velychko et al. (2014) Simultaneous Stimulation and Recording of Retinal Action Potentials Using Capacitively Coupled High-Density CMOS-based MEAs. Proc. of 9th Int. Meeting on Substrate-Integrated Microelectrodes

3 Results Cathodal stimulus pulses applied at a remote position from retinal ganglion cell somata evoke backpropagating action potentials. This propagation is electrically imaged at subcellular resolution for a ganglion cell population. Cathodal stimulus pulses applied at different positions in a hippocampal slice evoke local field potentials. The propagation patterns of these local field potentials are discussed.


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Electronic systems for electrophysiology and implantable devices Clemens Boucsein*, Karl-Heinz Boven, Andreas Möller Multi Channel Systems MCS GmbH, Reutlingen * Corresponding author. E-mail address: [email protected]

1 Backround / Aims Providing electronics for brain-machine interfaces and neuroprosthesis imposes several challenges from an engineering point of view. While the signals from neuronal tissue that have to be recorded are in the microvolt range, successful stimulation requires voltages four orders of magnitude larger [1]. At the same time, electronic components that should be attached to or implanted into the body need to be extremely compact and light-weight, and have to be optimized for low energy consumption. Finally, it is highly favorable to avoid cable connections to implanted devices, making radio technologies for energy and data transmission highly attractive for neurotechnology.

2 Results Starting from cable-based miniature amplifiers for the recording of retinal signals, Multi Channel Systems has developed neurotechnology devices for several different applications, featuring the most important aspects for the successful interfacing of neuronal tissue for BMI and neuroprosthesis. Low-noise recording circuits (rms < 8µV) allow the acquisition

trans-cutaneous energy transfer and data transmission led to fully implantable devices, which were successfully tested in sheep. Circuits can be assembled on flexible printed circuit boards, allowing for a versatile implantation procedure with high flexibility concerning the placement of the implant.

Fig. 2. Implant electronics and electrodes assembled on flexible substrate (provided by NMI, Reutlingen). Coil is used for energy transmission, while data exchange is achieved using infrared light.

Recent attempts to combine recording and stimulation on the same electrodes have led to the generation of a custom application-specific integrated circuit (ASIC), featuring high-voltage switches that protect the sensible amplifier circuits needed for low-noise recordings from the high voltages necessary for successful neurostimulation. With the help of these custom ASICs, a fully implantable device for EcOG recording and neurostimulation has been realized [2], and is currently tested in a suitable animal model (Fig.1).

3 Outlook Following the successful introduction of retinal implants containing microelectronic devices, the next step will be the approval of implants for combined recordings from and stimulation of neuronal tissue, utilizing the very same electrodes. Animal testing of the respective prototypes is currently performed, and further developments in chip technology are likely to improve energy efficiency and noise levels of devices for BMI and neuroprosthesis control. References [1] Fig. 1. Implant for EcOG-recordings with surface electrodes (provided by CorTec, Freiburg), which was tested in sheep and sucessfully passed the first long-term stability tests.

[2]

of local field potentials from 16 channels simultaneously, providing signal qualities sufficient for the application of epilepsy detection algorithms and prosthesis control. Miniaturization and implementation of

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Logothetis NK, Augath M, Murayama Y, Rauch A, Sultan F, Goense J, Oeltermann A, Merkle H (2010) The effects of electrical microstimulation on cortical signal propagation. Nat Neurosci. 13:1283-91. Gierthmuehlen M, Wang X, Gkogkidis A, Henle C, Fischer J, Fehrenbacher T, Kohler F, Raab M, Mader I, Kuehn C, Foerster K, Haberstroh J, Freiman TM, Stieglitz T, Rickert J, Schuettler M, Ball T (2014) Mapping of sheep sensory cortex with a novel micro-electrocorticography grid. J Comp Neurol. doi: 10.1002/cne.23631.


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The Technology of Retinal Implants: Challenges and Solutions Walter-G. Wrobel Retina Implant AG, Reutlingen, Germany

Abstract After more than a decade of development, the first retinal implants are being introduced to the market. The technical design deviates remarkably from other active implants and poses new challenges. Among them are: corrosion of electrical conductors and chips, lifetime of electrodes, eye movements and cable breakage. We present newly developed testing procedures and design solutions.

1 Introduction Since the 1990’s numerous groups around the world are working on retinal implants. To date two products have achieved market approval. We have been developing the subretinal implant Alpha IMS (see Fig.1), which in July 2013 received CE marking. Subretinal implants have the advantage that they use an anatomically natural separation between retina and choroid for implantion, which minimizes adverse tissue reaction and scarring. The subretinal space is humid and salty, and the electrode size has to be quite small (50x50µm²), the number of electrodes quite high (1500 electrodes), to achieve an acceptable optical resolution, and the device requires external electrical power.

3 Results By careful selection of components and optimization of manufacturing processes, polymer cables can achieve lifetimes acceptable for clinical application. Electrodes are not a challenge. Power supply cables can be designed in such a way that they survive an appropriate number of large eye movements.

2 Methods Corrosion of metallic materials due to electrolyte migration can be tested by measuring electrical leakage currents. Current levels of > 1µA are an indicator of early failure. Manufacturing processes can be improved by evaluation of leakage current levels in the pA-range. For electrodes, iridium-oxide electrodes are preferred, as they survive > 500 mio. pulses. As the eye is one of the most mobile human organs, and Botox injections are unacceptable, power supply cables have to designed in such a way that they survive > 10 years of life. We have designed a testing apparatus and have compared results with clinical experiences.

Fig.2. Alpha IMS implant in the human eye

4 Conclusion There are no unsurmountable barriers to achieving with subretinal implants a lifetime of > 10 years. Required materials are available, and material properties are satisfactory. Careful and reproducible material processing is now the main challenge. Acknowledgement Many thanks to our collaborators, especially NMI Reutlingen, with special gratitude to more than 40 trial patients worldwide. References [1]

K.Stingl et.al., Artificial vision with wirelessly powered subretinal electronic implant alpha-IMS, Proc.R.Soc. B280 : 20130077

Fig. 1. Retina implant Alpha IMS


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Tapping into decision-related activity in primary somatosensory cortex to control the subject’s percept Brugger, Dominik, Dehnke, Stine and Schwarz, Cornelius 1Werner Reichardt Center for Integrative Neuroscience, Systems Neuroscience 2Hertie Institute for Clinical Brain Research, Dept. Cognitive Neurology Eberhard Karls University, Tübingen, Germany* Corresponding author. E-mail address: [email protected]

Abstract The input-output transformation of cortical neurons is highly activity-dependent (‘context dependent’) leading to a high variance of evoked neuronal responses on a trial by trial basis. We tested the hypothesis if part of this variability can be explained by local ongoing activity that is related to the upcoming decision of the animal. We used ‘dynamic brain stimulation’, a novel closed loop approach that applies cortical microstimulation dependent on immediately preceding cortical activity. Measuring the state of primary somatosensory via one electrode and dynamically adapting microstimulation pulses injected via another allowed us to stabilize evoked cortical activity and improve psychometric sensitivity of a rat trained to detect intracorticalmicrostimulation. Support: DFG SCHW 577/9-1

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To What Extent Can Retinal Prostheses Restore Vision? Zrenner, Eberhart1*; Bartz-Schmidt, Karl-Ulrich1; Chee, Caroline2; Gekeler, Florian1; Jackson, Timothy L.3; MacLaren, Robert4; Nemeth, Janos5; Sachs, Helmut6; Stingl, Katarina1; Wong, David7 1 Werner Reichardt Center for Integrative Neurosciences and Institute for Ophthalmic Research, University of Tübingen Germany. 2 Dept.of Ophthalmology, National University Health System, Singapore, Singapore. 3 Dept. of Ophthalmology, School of Medicine,, King's College, London, United Kingdom. 4 Dept. of Clinical Neurosciences, Nuffield Laboratory of Ophthalmology, University of Oxford, Oxford, United Kingdom. 5 Semmelweis University, Budapest, Hungary. 6 Städtisches Klinikum Dresden-Friedrichstadt, Dresden, Germany. 7 Dept. of Ophthalmology, University of Hong Kong, Hong Kong. *[email protected]

Abstract An interim report on a clinical trial is given that describes the results obtained in 26 patients blind from retinitis pigmentosa who have received the subretinal electronic implant Alpha IMS (Retina Implant AG, Reutlingen, Germany). It has turned out that the surgical procedure is safe and that the majority of patients have benefit for performing successfully visual tasks in daily life with this device that meanwhile has received approval for commercial use in Europe.

1 Backround There exists no cure yet for blindness caused by hereditary retinal degeneration of the photoreceptors (e.g. retinitis pigmentosa (RP) but restoration of vision by various electronic retinal implants [1,2] has made rapid progress in recent years with remarkable results concerning visual localization of objects, mobility, even reading and face recognition in some cases [3,4]. There are, in principle, three different approaches (fig. 1, from Zrenner [5]): a) Epiretinal implants that are positioned on top of the neuroretina, placing electrodes, controlled by a camera outside the body, at the functional output of the retina, i.e. near to retinal ganglion cell fibers; b) subretinal implants that utilize light sensitive photodiodes, each connected to an electrode, both placed beneath the retina, contacting the functional input of the retina on the photoreceptor side, and c) the suprachoroidal approach that inserts electrode arrays from the back of the eye, positioning them on top of the choroid.

lar coil from which a subdermal cable leads to the eyeball.

2 Methods / Statistics Alpha-IMS subretinal implants (Retina Implant AG, Reutlingen, Germany) were positioned beneath the foveal region of 14 male and 12 female RPpatients (mean age 53.2 ± 8.2). Each of the 1500 subfoveal photodiodes within a 15° field (diagonal) controls an amplifier that, depending on the strength of the light, emits currents via planar electrodes to stimulate over-lying bipolar cells. Power and control signals are supplied inductively via a subdermal, retroauricu-

Fig. 1. The human ascending visual pathway (A) and sites in

the retina where epiretinal (B), subretinal (C), and suprachoroidal (D) implants are inserted [5].


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Function was tested by 4 procedures - 1: Monitorbased standardized tests for light perception, light localization, movement detection, grating acuity and visual acuity with Landolt C-rings (2- or 4 alternativeforce-choice-tests); 2: Detection, localization and identification of objects placed on a table; 3: Reading letters; 4: Visual experiences during outdoor and daily-life activities.

3 Results 1: Implant-mediated light perception was possible for 22 (85%) patients; light localization for 15 (58%); movement detection (up to 35 cycles/degree) for 6 (23%); measurable grating acuity (up to 3.3 cycles/degree) for 14 (54%); and measurable visual acuity (up to 20/546) for 4 (18%). 2: On a visual ability scale from 0 (worst) to 4 (best) for 4 white geometric figures presented on a black table, patients averaged 3.12 ± 0.31 for detection, 2.94 ± 0.3 for localization and 1.06 ± 0.28 for identification at month 2 which was significantly better (p< 0.012) than with chip power switched off. Similar results were obtained with activities of daily living. Losses of approximately 1-1.5 units occured over 9-12 months. 3: Four patients (18%) could read letters 4-8 cm in size. 4: Twelve pa-tients (46%) reported useful visual experiences includ-ing recognition of details and 7 patients (27%) could localize objects in daily life without details. 5: Besides two treatable serious adverse events there were no safety concerns.

cal testing and self-reported outcomes show restoration of useful vision in a majority of patients. Subretinal implantation surgery is safe and a multicenter study is continuing with a slightly modified implant built for long term durability. Acknowledgement The authors thank the staff of the Retina Implant AG, Reutlingen, Germany, especially W. Wrobel, R. Rubow, A. Hekmat, A. Harscher, S. Kibbel, R. Rudorf, C. Eipper, C. Jansen, M. Kokelmann, G. Blaess, H. Wagner and E. Nestler for organizational background support in the trial. This work was supported by Retina Implant AG, Reutlingen, Germany and by the German Federal Ministry of Education and Research (BMBF; FKZ: 01GQ1002). References [1] [2] [3]

[4]

[5]

Zrenner E. Will Retinal Implants Restore Vision? Science 295, 1022-1025 (2002) Zrenner E. Artificial vision: Solar cells for the blind. Nature photonics 6: 344-345 (2012) Zrenner E. Bartz-Schmidt KU, Benav H, Besch D, Bruckmann A, Gabel V-P, Gekeler F, Greppmaier U, Harscher A, Kibbel S, Koch J, Kusnyerik A, Peters T, Stingl K, Sachs H, Stett A, Szurman P, Wilhelm B, Wilke R. Subretinal electronic chips allow blind patients to read letters and combine them to words. Proc. R. Soc B - Biol.Sci. 278, 1489-1497 (2011) Stingl K, Bartz-Schmidt KU, Besch D, Braun A, Bruckmann A, Gekeler F, Greppmaier U, Hipp S, Hörtdörfer G, Kernstock C, Koitschev A, Kusnyerik A, Sachs H, Schatz A, Stingl KT, Peters T, Wilhelm B, Zrenner E. 2013 Artificial vision with wirelessly powered subretinal electronic implant alpha-IMS. Proc R Soc B - Biol Sci 280: 20130077. http://dx.doi.org/10.1098/rspb.2013.0077 Zrenner E. Fighting Blindness with Microelectronics. Science Transl. Med. 5: 210ps16 (2013); DOI: 10.1126/scitranslmed.3007399

4 Summary The Alpha-MS implant has meanwhile received a CE mark for commercial use in Europe. Psychophysi-

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Brain-state dependent neuromodulation with closed-loop neuroprosthetics for neurorehabilitation Alireza Gharabaghi1* 1 Division of Functional and Restorative Neurosurgery & Division of Translational Neurosurgery, University Hospital Tübingen; Neuroprosthetics Research, Centre for Integrative Neuroscience, University of Tübingen * [email protected]

Abstract We introduced a concept for movement-related and brain-state dependent stimulation of the human motor cortex for severely affected stroke survivors. Our findings suggest that closing the loop between intrinsic brain state, cortical stimulation and haptic feedback provides a novel neurorehabilitation strategy for stroke patients lacking residual hand function.

1 Backround / Aims Motor deficits after stroke are an ongoing challenge. Brain stimulation and robotic rehabilitation have been explored as complementary therapeutic strategies for neurorehabilitation. Each of these approaches has methodological limitations, especially in the context of severely affected stroke patients: Up to now, brain stimulation protocols have been applied uncoupled to the ongoing brain activity of the patient. Similarly, current robotic training programs are not suitable for patients missing residual motor function.

2. Methods A necessary prerequisite for the presented approach is a set-up that allows for closed-loop brain stimulation based on intrinsic neural properties at the time of stimulation. In parallel, this framework has to provide the patient with volitional motor control of a robotic device that moves the paralyzed hand contingent to movement intention.

3 Results Here, we report on such brain state-dependent stimu-lation (BSDS) combined with haptic feedback provided by a robotic hand orthosis. Brain stimulation of the motor cortex and haptic feedback to the hand were controlled by sensorimotor desynchronization during motor-imagery and applied within a brainmachine interface environment in the chronic phase after stroke. BSDS significantly increased the excitability of the stimulated motor cortex, an effect not observed in non-BSDS protocols.

pendent interventions in paralyzed patients for inducing targeted plasticity. References Becker HG, Haarmeier T, Tatagiba M, Gharabaghi A. Electrical stimulation of the human homolog of the medial superior temporal area induces visual motion blindness. J Neurosci. 2013 Nov 13;33(46):18288-97 Bensch M, Martens S, Halder S, Hill J, Nijboer F, Ramos A, Birbaumer N, Bogdan M, Kotchoubey B, Rosenstiel W, Schölkopf B, Gharabaghi A. Assessing attention and cognitive function in completely locked-in state with event-related brain potentials and epidural electrocorticography. J Neural Eng. 2014 Apr; 11(2):026006. Gharabaghi A, Naros G, Walter A, Roth A, Bogdan M, Rosenstiel W, Mehring C, Birbaumer N. Epidural electrocor ticography of phantom hand movement following long-term upper limb amputation Front Hum Neurosci. 2014 May 6;8: 285. Gharabaghi A, Kraus D, Leão MT, Spüler M, Walter A, Bogdan M, Rosenstiel W, Naros G, Ziemann U. Coupling brain- machine interfaces with cortical stimulation for brain-state dependent stimulation: enhancing motor cortex excitability for neurorehabilitation. Front Hum Neurosci. 2014 March 5;8:122. Gharabaghi A, Naros G, Walter A, Grimm F, Schuermeyer M, Roth A, Bogdan M, Rosenstiel W, Birbaumer N. From assistance towards restoration with an implantaed brain-computer interface based on epidural electrocorticography. Restorative Neurology and Neuroscience 2014 (in press) Walter, A., Ramos Murguialday, A., Spuler, M., Naros, G.,Leao, M.T., Gharabaghi, A., Rosenstiel, W., Birbaumer, N., and Bogdan, M. (2012). Coupling BCI and cortical stimulation for brain-state-dependent stimulation: methods for spectral estimation in the presence of stimulation after-effects. Frontiers in neural circuits 6, 87. Vukelić M, Bauer R, Naros G, Naros I, Braun C, Gharabaghi A. Lateralized alpha-band cortical networks regulate volitional modulation of beta-band sensorimotor oscillations. Neuroimage. 2014 Feb 15;87: 147-53.

4 Conclusion/Summary We present a novel intervention using selfgenerated brain-signals for the volitional control of concurrent brain stimulation and robotic control of a paralyzed hand. Thereby, this set-up provides a closed-loop framework to explore brain-activity de-


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9th International Meeting on Substrate-Integrated Microelectrode Arrays

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Stimulation Strategies for Neurons and Fibers


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Stimulation strategies for neurons and fibers

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Bioelectronic Medicines: Can MEAs Help Bring About a New Class of Treatments? Daniel Chew1, Bryan McLaughlin2, Arun Sridhar1, Sonal Patel1, Hannah Tipney1, Roy Katso1 Imran Eba3 and Kristoffer Famm1* 1 Bioelectronics R&D, GlaxoSmithKline, Stevenage, Hertfordshire SG1 2NY, UK 2 The Charles Stark Draper Laboratory, Cambridge, Massachusetts 02139, USA 3 Action Potential Venture Capital, Cambridge, Massachusetts 02142, USA * Corresponding author. E-mail address: [email protected]

Abstract Bioelectronic medicines have potential to be a new class of treatments for major chronic diseases, but require significant innovation in high-resolution, non-disruptive peripheral nerve interfacing to become a reality. GlaxoSmithKline (GSK) wants to work with the MEA community towards this goal.

1 Bioelectronic medicines – an intro Virtually all visceral organs are under the influence of the nervous system - lungs, heart, liver, pancreas, kidney, bladder, gastrointestinal tract, and lymphoid and reproductive organs to mention a few. GSK believes modulation of neural signals in peripheral nerves to and from these organs could bring about a new class of treatments as neutrally-controlled organ functions are at the centre of major chronic diseases such as asthma, hypertension, diabetes, and arthritis. [1-3] In addition to offering a new approach to treat disease, bioelectronic medicines could offer higher specificity than current molecular treatments through the temporal and functional precision inherent in neural control. Our long-term ambition is to develop implantable micro-devices that directly interface with individual nerves, close to the end organ of interest. Such devices will read and write the full complexity of the signalling pattern within the nerve and operate in a closed loop fashion, regulating organ function with therapeutically optimised signatures. Electrical “treatment codes” could one day be used side by side with molecular medicines.

2 MEAs at a new challenging interface

3 Engaging with an emering community Bioelectronic medicines will only reach its full potential if a broad range of disciplines together build a solid research foundation, backed by resourcing and a commitment to bring treatments all the way to patients. GSK is seeking to play such an integrating role through its exploratory research funding program that currently brings together near 30 academic groups around the world [5], its Innovation Challenge focused on rapidly developing visceral nerve research platforms to the community [6], and its $50m venture arm dedicated to the technology and early treatment manifestations of bioelectronic medicines [7]. Through this and potential other mechanisms, GSK wants to work with the MEA community and visionary research funding bodies to bring next-generation microelectrode arrays to bear on visceral nerves. References

To deliver bioelectronic medicines, a revolution in visceral peripheral nerve interfacing is necessary. [4] To address 10x increase in spatial recording and stimulation density above existing arrays could be required, plus a 10x increase in the number of parallel channels. Importantly, this must be achieved without a reduction in performance and with minimal axonal damage through displacement. Another challenge lies in providing intimate contact with irregular plexi, and ganglia embedded in adipose tissue. A third is related to minimising artefacts from pulsating vasculature and movement of cardiac, diaphragm, intestinal, and so50

matic muscles while maintaining contact with the same set of nerve fibres over time. Shape adaptability, flexibility, integrated active electronics, and new materials could be key for MEAs to overcoming these challenges.

[1] [2]

[3]

[4]

[5] [6] [7]

Famm, K. et al. A jump start for electroceuticals. Nature 496, 159–161 (2013). Gassler, J. P. & Bisognano, J. D. Baroreflex activation therapy in hypertension. J. Hum. Hypertens. http://dx.doi.org/ 10.1038/ jhh.2013.139 (2014). Olofsson PS. et al.Rethinking inflammation : neural circuits in the regulation of immunity. Immunol Rev 248(1):188-204 (2012) Birmingham, K. et al. Bioelectronic medicines: a research roadmap Nature Reviews Drug Discovery In press, June (2014) http://www.gsk.com/partnerships/bioelectronics.html? tab=tabexploratory-funding-programme http://www.gsk.com/partnerships/bioelectronics.html? tab=tabmillion-dollar-innovation-challenge http://www.actionpotentialvc.com/



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Response Profiles of Auditory Neurons on Multielectrode Arrays Stefan Hahnewald1*, Anne Tscherter3, Marta Roccio1, Emanuele Marconi1, Carolyn Garnham2, Pavel Mistrik2, Teresa Melchionna2, Jürg Streit3, Julien Brossard4, Alexandra Homsy4, Herbert Keppner4, Hans-Rudolf Widmer5, Pascal Senn1 1 Inner Ear Research Laboratory, Department of Clinical Research, University of Bern and University Department of Otorhinolaryngology, Head & Neck Surgery, Bern, Switzerland. 2 MED-EL Elektromedizinische Geraete GmbH (MED-EL), Innsbruck, Austria. 3 Department Physiology, University of Bern, Bern, Switzerland. 4 Haute Ecole Arc Ingénierie, La Chaux-de-Fonds, Switzerland. 5 Department of Neurosurgery, Bern University Hospital, Bern, Switzerland. * Corresponding author. E-mail address: [email protected]

Abstract Cochlear implants (CIs) have become the gold standard treatment for deafness. Despite all the success, some limitations remain. Our project: “NANOCI” (www.nanoci.org) aims at developing a new generation of CIs where the peripheral processes of the auditory neurons grow towards the electrode array to form a gapless interface in the cochlea. In theory, this strategy should result in i) a better auditory resolution and ii) lower energy consumption, two of the main limitations of current CI systems. As a first step towards this ambitious goal, we are assessing auditory neuron activity on multi electrode arrays (MEAs) to investigate parameters that are relevant in the context of the project. If the distance of the stimulation electrodes to auditory neurons is changed, stimulation parameters will need to be adapted as well to fully exploit the theoretical advantages of a gapless interface. Optimum stimulation parameters and distance-related effects are experimentally addressed on a custom-made set up and first results confirmed the main hypothesis of the NANOCI project, namely that the smaller the distance between the stimulating electrode and the auditory neurons, the lower the Voltage needed to trigger neuronal activity and the larger the dynamic range of responses. Although preliminary, these results are the first of their kind in the auditory field and allow us to address more sophisticated stimulation protocols in the near future.

1 Background / Aims Hearing is one of the most important senses to interact with the environment. Hearing loss therefore has a considerable negative impact on the communication ability of affected individuals and high socioeconomic costs. For mild to moderate forms of hearing loss, hearing aids are widely used to alleviate the symptoms. In severe cases and deafness, a neuroprosthesis called cochlear implant (CI) can be surgically implanted to restore hearing. In the majority of CIrecipients, this treatment is effective, however, these devices still have limitations, which will be shown below, together with our attempts to address them. The potential impact in this field of research is considerable because over 360 Million people worldwide suffer from hearing loss (WHO, Feb. 2013; Fact Sheet N°300). The inner ear is a complex biological structure composed of two major compartments containing the vestibular organs responsible for balance and the cochlea responsible for hearing, respectively. For hearing, hair cells (HCs) are critical because they transduce mechanical vibration into electrical stimulation of the auditory spiral ganglion neurons (SGNs). Many forms of sensorineural hearing loss originate from defects of HCs and CIs essentially bypass lost or

not-functioning HCs by directly stimulating the SGNs electrically. One major limitation of CI systems is caused by the anatomical gap between the implant, consisting of linear array of 12 electrodes and the dendritic processes of the SGNs in the cochlea. As a consequence of this gap, stimulation from one electrode results in the simultaneous activation of a group of several hundreds of auditory neurons, thereby reducing the frequency resolution of the system and resulting in a high energy consumption of the device, a second drawback of the system. By creating a gapless interface between the electrode array and the auditory neurons, all these issues could be addressed simultaneously. The EU-FP7-funded project “NANOCI” (www.nanoci.org) (Grant agreement No.:281056) has been set up to achieve this ambitious goal combining biotechnology, nanotechnology and biomedical engineering in a balanced, multidisciplinary approach. In order to simulate the NANOCI concept on a chip in vitro, we rely on Multi Electrode Array (MEA) as the basis of our in vitro experiments. MEAs are adequate to study the CI technology because stimulation and recording of neurons can be done simultaneously and distance related effects, resolution and response profiles can be assessed on multiple channels in parallel. The MEAs can also be seen as two dimensional cochlear implant where the electrodes are arranged on


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a surface instead of a three dimensional helix. The MEA platform currently used in our laboratory (Qwane Biosciences, Lausanne) contains 64 platinum electrodes (40 x 40 µm with 200 µm space between two electrodes), which can all either be used for stimulation or for recording of extracellular single unit potentials. For our experiments, different culture types of mouse auditory neurons are tested on the MEA platform.

2 Methods SGNs along with neighboring supporting cells and fibroblasts were isolated from 3-7 days old C57/Bl6 mice. Four different types of cell cultures were compared for best functional assessment on the MEA platform: single dissociated cells, partly dissociated micro explants, organotypic explants and stem/progenitor cell derived neurons. Single dissociated cells and partially dissociated explants were obtained by dissociating the SGN for 10 min with Accumax (Merck Millipore, Germany) at room temperature followed by mechanical trituration. Single dissociated cells were obtained by filtering the suspension through a 100µm strainer, whereas the micro explants were directly plated without filtering. Organotypic explants correspond to linear sections from the SGN, keeping the original anatomy as intact as possible (Fig. 1). These three primary SGN cultures were grown in Neurobasal medium with 10% FBS (both Invitrogen, USA) for 2 weeks at 37°C. BDNF (25ng/ml; R&D Systems®, USA) was added to the medium for the first 4 days to initiate outgrowth of neurites. Stem/progenitor cells were isolated from the dissected SG tissue by an enzymatic digestion for 5 min with trypsin at 37°C and careful trituration. After filtering, single cells and small clumps were cultured in mitogen containing medium, following a published protocol to induce formation of floating spheres (Oshima et al., 2009). Second generation spheres were plated and cultured for a two-week to induce differentiation into auditory neurons after withdrawal of mitogenic growth factors in serum free medium. For functional assessment, the four different cell cultures were differentiated directly on the MEA slides coated with either laminin/poly-L-lysine (laminin diluted 1:200 in 0.01% poly-l-lysine, both from Sigma) or matrigel (1:10, BD Biosciences) and grown for 2 weeks. For immunohistochemistry, cultures were fixed and stained for βIII-tubulin (R&D, 1:200) to visualize the neurites, DAPI (Sigma, 10µg/ml) for cell nuclei and phalloidin (Sigma, 50µg/ml) for visualizing the actin cytoskeleton. The stained cultures were imaged using a fluorescent microscope (LSM700, Zeiss, Germany). Semiautomatic pixel counts were performed using the ImageJ™ software package. For MEA recordings, cultures were perfused with an extra cellular solution (ECS) (145mM NaCl, 4mM 52

KCl, 1mM MgCl2, 2mM CaCl2, 5mM Hepes, 2mM sodium pyruvate, and 5mM glucose [pH 7.4, room temperature]). To depolarize the cells and induce activity, cultures were perfused with ECS with 30mM potassium. To assess distance-related effects between the stimulating electrode and auditory neurons, cultures were stimulated via an external custom-designed MEDEL electrode (stimulus: 100ms, bi-phasic) that could be placed via a micromanipulator at distances ranging between 0 (gapless) and 70 µm (mimicking current CI systems) from the surface of the culture. The external electrode is a Teflon coated wire consisting of 90% platinum and 10% iridium and a diameter of 150µm. The stimuli were applied in 250 mV steps and ranged from 0 to 3 V. Single unit potentials were recorded via LabView software and analyzed via IgorPro (Wavemetrics).

Fig. 1: Primary SGN culture (explants) cultured on MEA for 9 days, red (neurons, βIII-tubulin staining), blue (Nuclei, DAPI). Scale bar: 200µm.

3 Results Comparative cell source assay: The morphological comparison of the four different culture types showed that SGN organotypic explants and micro explants had the highest densities of neurons quantified as number of βIII-tubulin positive pixels per DAPI positive pixels. In addition, the maximum length of neurites as assessed with ImageJ™ was highest in the same two preparations (data not shown). Density and length of neurites turned out to be important parameters for functional measurements, because a neurite has to cross at least two electrodes for stimulation/recording on the MEA setup. In case of single dissociated cells and stem/progenitor generated neuronal cells, the density on the MEA was not sufficient for stimulation and recording so far. For this reason, SGN organotypic explants were selected and further used for functional measurements on MEAs. MEA experiments: Cultures were perfused with custom made physiological ECS. Some cultures showed spontaneous activity, whereas others were only active after depolarizing the auditory neurons with a perfusion of ECS containing 30mM potassium. In the latter case, neuron activity started 15s to 20s after onset of perfusion with action potentials occurring in a



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variable range from 1 to 30 spikes per 100ms. We were also able to electrically stimulate the auditory neurons from MEA electrodes. Stimulation was reliably followed by one or more single unit events (action potentials) at one or more electrodes. Distance related measurements: The stimulation with the external electrode mounted on the micromanipulator clearly showed distance-related effects. The experiment confirms that the higher the distance from the stimulating electrode to the auditory neuron culture, the higher was the voltage needed to trigger a first response (activation threshold) and thus to evoke neuronal activity (measured on MEA) (Fig. 2).

4 Conclusion/Summary We were able to record single unit activity from organotypic cultures of spiral ganglion neurons on MEAs. Activity of these cultures was either spontaneous or could be triggered through a 30mM potassium perfusion or could be evoked by electrical stimulation from the MEA electrodes, which has not been reported previously. We were also able to do distance related stimulations in these cultures showing for the first time the efficacy of a cochlear implant might be increased dramatically by creating a gapless interface between the implant and the auditory neuron.

Fig. 2: Distance related effects on response profiles of auditory neurons when stimulated from an external electrode mounted on a micromanipulator positioned at a distance of 40µm (red), 20µm (green) and 0µm (gapless, blue). The average number of single unit potentials in response to 5 consecutive stimuli (y-axis) is plotted against the stimulation voltage (x-axis, mV).

Acknowledgement This study was financially supported by the European Union`s Seventh Framework Programme (FP7/2007-2013 under grant agreement No. 281056). We thank Ruth Rubli, Department of Physiology, University of Bern, Bern, Switzerland for help with experimental cultures and Robin Davis, Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, New Jersey for advice on neuronal culture. References [1]


Oshima K., Senn P. And Heller S Isolation of sphere-forming stem cells from the mouse inner ear (2009) Auditory and Vestibular Research: Methods and Protocols. Humana Press. pp 141 – 162

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PEDOT-CNT Coated Electrodes Stimulate Retinal Neurons with Low Voltage Amplitudes Ramona Samba1*, Thoralf Herrmann2, Günther Zeck2 1 BioMEMS and Sensors Group, NMI at the University of Tübingen, Reutlingen, Germany 2 Neurochip Research Group, NMI at the University of Tübingen, Reutlingen, Germany * Corresponding author. E-mail address: [email protected]

Abstract For electrical stimulation of neurons, methods that limit the polarization of the metal-based electrode to within safe electrochemical limits are desirable. Here we demonstrate that PEDOT-CNT coated electrodes are able to stimulate retinal circuitry presynaptic to the ganglion cells at stimulus thresholds between 200 – 600 mV for stimulus durations between 1000 – 100 ms respectively.

1 Background and aims Electrical stimulation of the retina offers the possibility of partial restoration of visual function after degenerative diseases like retinitis pigmentosa[1, 2]. Challenges with current stimulation electrodes include side effects due to high stimulation thresholds and non-ideal electrode-tissue interface. PEDOT-CNT, a composite of the conducting polymer poly(3,4ethylenedioxythiophene) and carbon nanotubes, has been recently presented as a highly biocompatible electrode material for neuronal applications, exceeding state-of-the-art electrodes in electrical performance[3]. In this work, PEDOT-CNT was investigated towards its performance in retinal stimulation, based on the hypothesis of lower stimulation thresholds due to higher charge transfer capacitance.

were 0.1, 0.2, 0.5 and 1 ms. Standard TiN microelectrodes served as control.

2 Methods The details on the fabrication of PEDOT-CNT MEA can be found in Gerwig et al., 2012[3]. Briefly, a standard MEA with gold microelectrodes (30 µm diameter) was coated employing electropolymerization in a suspension of single-walled carbon nanotubes, poly(styrenesulfonate) and 3,4-ethylenedioxythiophene. Impedance at 1 kHz was measured in PBS after fabrication and in between different sets of stimulation experiments. The retina of Sprague Dawley rats was prepared as described in Stutzki et al., 2014[4]. Briefly, isolated retinal portions were mounted ganglion cell side down on the MEA. During recordings, the retina was constantly perfused with warm oxygenated Ames’ medium. Extracellular stimulation was performed using constant cathodic voltage pulses applied to set of eight microelectrodes. Spikes from retinal ganglion cells (RGCs) were recorded by the electrode in the centre of the eight stimulation electrodes. Stimulus durations

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Fig. 1. Threshold stimulation voltages (Vthresh) are smaller for PEDOT-CNT electrodes as compared to TiN electrodes of the same size. Mean threshold voltage calculated for eight RGCs stimulated with TiN electrodes (black squares) and six RGCs stimulated with PEDOT-CNT coated gold electrodes (gray circles). Error bars represent standard error of the mean.

3 Results For each stimulus duration the voltage amplitude was increased in incremental steps. Stimulated ganglion cell activity was detected in a time window of 10 – 20 ms after stimulus onset. Spikes in this time window are evoked by indirect stimulation, i.e. stimulation of the retinal circuitry presynaptic to the ganglion cell[5]. The voltage threshold activity was determined as that value which evoked a spike in 50 % of the stimu-



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lus repetitions. Threshold voltages were estimated for each stimulus duration. The mean threshold voltage for ganglion cells stimulated with TiN electrodes (n=8 RGCs) is about twice as high as the mean threshold voltage for stimulation with PEDOT-CNT coated gold electrodes (n=6 RGCs) (see figure 1). Electrode impedance increased only slightly after several experiments and was still below 30 kΩ. No significant impedance difference was found between electrodes used for stimulation and unused ones.

4 Conclusion PEDOT-CNT microelectrodes enable stimulation of retinal circuitry at lower voltages than state-of-theart TiN microelectrodes. Lower voltages prevent damage by electrochemical side reactions. Therefore, PEDOT-CNT microelectrodes are attractive for neuroprosthetic applications.

Acknowledgement Funding for this research was in part obtained from the German Federal Ministry of Education and Research through grant no. 01GQ0834 (RS) and grant no. 1312038 (TH and GZ). We thank NMI TT GmbH and Multi Channel Systems GmbH for support in MEA technology. References [1] [2] [3] [4] [5]


A. Ahuja, et al., British Journal of Ophthalmology 2011, 95, 539-543. R. Wilke, et al., Investigative ophthalmology & visual science 2011, 52, 5995-6003. R. Gerwig, et al., Frontiers in Neuroengineering 2012, 5, 111. H. Stutzki, et al., Frontiers in Cellular Neuroscience 2014, 8, 38. M. Eickenscheidt, et al., Journal of neurophysiology 2012, 107, 2742-2755.

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Wide-Field Photostimulation of Cortical Networks: Selective Activation of Excitatory Neurons and Its Impact on Evoked Reverberating Responses In Vitro Rocco Pulizzi1Ϯ, Gabriele Musumeci1Ϯ, Chris Van Den Haute2, Veerle Baekelandt2, and Michele Giugliano1,3,4* 1 Department of Biomedical Sciences, University of Antwerp, Belgium; 2 Department of Neurosciences, KU Leuven, B-3000 Belgium; 3 Brain Mind Institute, EPFL, Switzerland; 4 University of Sheffield, S1 4DP Sheffield, UK. Ϯ Equal contribution * Corresponding author. E-mail address: [email protected]

Abstract Dissociated cortical networks developed on microelectrode arrays (MEAs) and expressing Channelrhodopsin-2 selectively in principal neurons, were our experimental model. We non-invasively monitored the spontaneous and light-evoked multi-unit responses to wide-field LED photoactivation. This approach allows us to manipulate the reverberating network response, thus proving to be a potential key tool for establishing closed-loop paradigms.

1 Background Closed-loop control of cell assemblies via optogenetics has been suggested as a pivotal (causative) approach, required to advance our current (correlative) understanding of the neuronal substrates for memory and behaviour [1, 2]. However, due to present limitations, in vivo photostimulation in the cortex or hippocampus generally addresses a population of cells simultaneously, rather than one cell at the time [3, 4]. Thus, besides the downstream responses, arising from direct excitation or inhibition, photostimulation may also elicit reverberating local responses. These are a consequence of recurrent connectivity and may reflect a variety of time-scales and dynamical properties, whose precise control is complex. Here, we explored photostimulation in recurrent networks, by a reduced in vitro model.

2 Methods We employed postnatal rat neocortical cell cultures, grown on substrate-integrated microelectrode arrays (MEAs) at 37°C, 5% CO2, 95% R.H. for several weeks. Five days after plating, neurons were transduced by adeno-associated viral particles (AAV) to express a double mutant version of ChannelRhodopsin-2 (C2-LCTC) conjugated with a reporter protein (mCherry). Exploiting the CaMKIIα promoter upstream the coding sequence, C2-LCTC could be restricted to excitatory neurons and enabling their activation by light [5]. We delivered wide-field LED photostimulation (2 mW*mm-2, 470 nm, Rebel, Quadica Devel., Canada), employing a stimulus isolator (STG1002, Multichannel Systems, Germany) and an externally dimmable DC Driver (LuxDrive, Randolph, USA). We simultaneously recorded neuronal multiunit 56

activity by the MEAs via an amplifier (MEA1060BC, Multichannel Systems) and studied spontaneous and light-evoked electrical activity, in mature cultures (i.e., 28-43 days in vitro). Recorded signals were analysed by using custom made software written in C and Matlab (The MathWorks, USA) [6]. The Pearson’s coefficient and its significance were employed to assess correlations.

3 Results ChannelRhodopsin-2 expression resulted in a large number of photosensitive (~80%) neurons, and led to normal ex vivo neuronal survival and development, with the typical irregular emergence of synchronized collective bursting, as in control cultures. Brief light pulses evoked highly reproducible network-wide responses, consisting of i) an early phase (i.e., 0-20ms, immediately after the stimulus), and of ii) a late phase, lasting up to 400ms and fully abolished by bath-applied synaptic blockers. Interestingly, the duration of the pulses (i.e., 0.1-20ms) appeared linearly correlated with time course and profile of the evoked firing response, during its late but not its early phase. In particular, a longer pulse reliably delayed the peak of the response (i.e., from 50 to 150ms). Furthermore, light stimulation elicited global coherent oscillatory firing, in the gamma range. Unexpectedly, the dominant oscillation frequency and its power were also linearly correlated with the duration of the light pulse.



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4 Conclusion Besides clarifying further the impact of optogenetic manipulations in recurrent microcircuits, our results provide a first indication that light duration can be employed to differentially manipulate network responses, altering feed forward or feedback components, which is extremely relevant for the design of closed-loop experimental paradigms aimed at steering network responses.

References [1]

[2]

[3]

[4]

Acknowledgement We are grateful to Mr. D. Van Dyck and Mr. M. Wijnants for excellent technical assistance. We thank Mr. J. Couto, Dr. M. Chiappalone and Dr. M. Mahmud for scientific discussions. Financial support from EC-FP7 (Marie Curie Network “NAMASEN”, grant n. 264872, ICT-FET project “ENLIGHTENMENT”, grant n. 284801), Research Foundation Flanders, grant n. G.0888.12N, and the Interuniversity Attraction Poles Program (IUAP), initiated by the Belgian Science Policy Office, is kindly acknowledged.


[5]

[6]

Olsen, S. R., Bortone, D. S., Adesnik, H., & Scanziani, M. (2012). Gain control by layer six in cortical circuits of vision. Nature, 483(7387), 47–52. Lee, S.-H., Kwan, A. C., Zhang, S., Phoumthipphavong, V., Flannery, J. G., Masmanidis, S. C., Dan, Y. (2012). Activation of specific interneurons improves V1 feature selectivity and visual perception. Nature, 488(7411), 379–83. Belzung, C., Turiault, M., & Griebel, G. (2014). Optogenetics to study the circuits of fear- and depression-like behaviors: A critical analysis, Pharmacology, Biochemistry and Behavior, 122, 144–157. Roux, L., Stark, E., Sjulson, L., & Buzsáki, G. (2014). In vivo optogenetic identification and manipulation of GABAergic interneuron subtypes. Current Opinion in Neurobiology, 26C, 88–95. Prigge, M., Schneider, F., Tsunoda, S. P., Shilyansky, C., Wietek, J., Deisseroth, K., & Hegemann, P. (2012). Colortuned channelrhodopsins for multiwavelength optogenetics. The Journal of Biological Chemistry, 287(38), 31804–12. Mahmud, M., Pulizzi, R., Vasilaki, E., & Giugliano, M. (2014). QSpike tools: a generic framework for parallel batch preprocessing of extracellular neuronal signals recorded by substrate microelectrode arrays. Frontiers in Neuroinformatics, 8 (March), 1–14.

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Restoring Light Sensitivity in Blind Retinae Using Next-Generation Photopharmacology Laura Laprell*, Martin Sumser, Dirk Trauner Ludwig-Maximilians-Universitaet, Munich, Germany * Corresponding author. E-mail address: [email protected]

Abstract It has been estimated that more than 300 million people worldwide are suffering from blindness or impaired vision. Although the photoreceptor cells in some cases undergo complete degeneration, the remains of the retinal circuitry and especially the ganglion cells, which generate the output of the retina to the brain are often not affected and are still functionally intact. In order to restore light sensitivity to blind retinae, optochemical tools targeting receptors natively expressed in retinal cells provide a new photopharmacological strategy without the need of any genetic manipulation. We introduced the concept of photochromic ligands (PCL), which takes advantage of photoswitchable azobenzene molecules. These molecules are chemically modified in such a way that they enable optical control of neuronal activity by acting as selective photoswitchable receptor ligands or blockers of voltagegated ion channels (VGIC). The molecule presented here is a photochromic potassium channel blocker and represents a very promising next-generation molecule for restoring light sensitivity in blind retinae.

1 Introduction Recently, we introduced optochemical genetics as a new tool for the light-dependent control of intrinsic receptor function [3]. This photopharmacological approach is based on the application of photochromic ligands (PCLs) that bind non-covalently to their target receptors [1][7], consequentially activating/inactivating them in a reversible fashion (Fig. 1). Generally, PCLs are comprised of a ligand binding domain, a linker region and an azobenzene moiety. The azobenzene, responsible for the light-sensitivity, can exist either in the thermodynamically stable trans- or in the less stable cis-state. Upon irradiation with certain wavelengths one can switch between these two states. Only in one configuration the PCL can bind to the receptor, in the other it is sterically hindered (Fig. 1A). This strategy is not limited to control receptor activation, but can also be applied for blocking and unblocking ion channels [4] (Fig. 1B). Since PCLs usually bind in a similar fashion as ligands to their respective receptors, the photopharmacological approach can easily be applied in-vitro or in-vivo to any tissue expressing the target receptor [4][6]. Polosukhina et al. (2012) could demonstrate that light-switchable open channel blockers applied to the eye (intravitreal injection) of blind mice are capable to restore light sensitivity [5]. One major drawback of this study was the use of the first generation open-channel blocker Acrylamide-Azo-Quaternary ammonium (AAQ) that has to be actively switched with 380 nm and 500 nm. Here, the requirement of UV-light illumination limits its applicability as a drug for vision restoration.

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Fig. 1. (A) Schematic drawing of the photochromic ligand approach (PCL). The ligand carries a light-sensitive moiety, an azobenzene, which changes its conformation between the active and inactive state upon irradiation with specific wavelengths (B) Photochromic open channel blockers represent a special case of PCLs. Only in one conformation the photoswitch blocks the pore of the channel, in this case a potassium channel.

Currently, we are working with a next-generation open-channel blocker ‘red-DAD’. This molecule harbors several features, making it superior to AAQ for an application in vision restoration. First, the compound is not permanently charged and thereby crosses biological barriers more easily. Second, it is redshifted, i.e. one can switch ‘red-DAD’ with blue or white light (440-480 nm, Fig.2). Third, lower light intensities in the range of bright ambient illumination are needed (data not shown). Finally, it relaxes back to the thermodynamically stable trans-configuration within 200 ms after turning of the light (data not shown).



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4 Discussion

Fig. 2. (A) Potassium channel recordings in cortical neurons after application of red-DAD. In darkness channel pore is blocked by redDAD.This block is optimally released with illumination between 420 and 460 nm light (B).

2 Methods For in-vitro studies with completely lightinsensitive murine retinae (cngA3-/-, rho-/-, opn4-/-, similar to the mice used in [2]) retinal explants were shortly incubated with ‘red-DAD’ and mounted on a multi-electrode array (MEA). The detected spiking frequencies of RGCs integrate signals from all retinal cells, which are recorded by the MEA.

Taken together, ‘red-DAD’ is a promising nextgeneration molecule for restoring light-sensitivity in blind retinae. It penetrates easily into retinal cells, can be switch with moderately bright intensities of visible light and has the advantage of acting on bipolar cells. Activating bipolar cells will lead to highly complex RGC signal outputs to the brain due to lateral connections via amacrine cells. The more complex a signal is the more information it contains.

References [1]

[2]

[3] [4]

3 Results By alternating 480 nm light and darkness it can be demonstrated that the pre-incubated retinae induce higher spiking frequency during illumination whereas the control retinae do not respond to light (Fig. 3).

[5]

[6]

[7]

Bartels E, Wassermann NH, Erlanger BF. Photochromic activators of the acetylcholine receptor. Proc Natl Acad Sci U S A. 1971, 68, 1820-3. Claes, E., Seeliger, M., Michalakis, S., Biel, M., Humphries, P. and Haverkamp, S. Morphological characterization of the retina of the cnga3(-/-)rho(-/-) mutant mouse lacking functional cones and rods. Inves Ophthalmol Vis Sci 2004, 45, 2039–48. Fehrentz T, Schönberger M, and Trauner D. Optochemical genetics. Angew Chem Int Ed Engl. 2011, 50, 12156-82. Mourot A, Kienzler MA, Banghart MR, Fehrentz T, Huber FM, Stein M, Kramer RH, Trauner D. Tuning photochromic ion channel blockers. ACS Chem Neurosci. 2011, 2, 536-43. Polosukhina A, Litt J, Tochitsky I, Nemargut J, Sychev Y, De Kouchkovsky I, Huang T, Borges K, Trauner D, Van Gelder RN, Kramer RH. Photochemical restoration of visual responses in blind mice. Neuron. 2012, 75, 271-82. Stawski P, Sumser M, Trauner D. A Photochromic Agonist of AMPA Receptors. Angew Chem Int Ed Engl. 2012, 51, 57485751. Volgraf M, Gorostiza P, Szobota S, Helix MR, Isacoff EY, Trauner D. Reversibly caged glutamate: a photochromic agonist of ionotropic glutamate receptors. J Am Chem Soc. 2007, 129, 260-1.

Fig. 3. (A) Top: Rasterplot of an MEA recording of an untreated cngA3-/-, rho-/-, opn4-/- mouse stimulated with 480nm light (blue bars). Bottom: Average histogram. Firing rate in Hz (spikes/s). (B) Rasterplot and histogram of ’red-DAD’ treated retina stimulated with 480nm light.

By blocking specific synaptic transmission pathways, one can identify, on which cell types ‘red-DAD’ acts in retinae. Carrying out similar pharmacological experiments as in [5] we identified bipolar cells as the primary target of red-DAD (data not shown).


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Finding the Most Effective Site for Extracellular Neuronal Stimulation Milos Radivojevic, David Jäckel, Jan Müller, Vijay Viswam, Ian Lloyd Jones, Andreas Hierlemann, and Douglas Bakkum ETH Zürich, Bio Engineering Laboratory, D-BSSE, Basel, Switzerland * Corresponding author. E-mail address: [email protected]

Abstract Here, we present a method to investigate the possibilities of selective, extracellular stimulation of rat cortical neurons grown over a high-density complementary metal–oxide–semiconductor microelectrode array.

1 Background / Aims Knowledge of effective extracellular stimulation methods optimal stimulation locations and stimulation signal magnitudes to selectively stimulate single neurons is important for designing electrophysiological experiments. However, stimulation artifacts usually compromise the ability to record neuronal responses in close proximity to the stimulation site and to fully evaluate stimulus efficiency at single-cell resolution. To overcome this issue, we used a high-density complementary metal– oxide–semiconductor (CMOS) microelectrode array (HD-MEA), which allows for single-cell electrical activity to be recorded from a large number of electrodes and for stimulus efficacy to be judged by reading out the cell’s somatic and axonal responses.

2 Methods / Statistics We grew primary rat cortical neurons on CMOSbased HD-MEAs developed in our laboratory [1]. The array contains 11,011 densely packed electrodes, each of which can be used as recording or stimulation site. Thanks to an excellent signal-to-noise ratio, the array is sensitive enough to detect both somatic and propagating axonal action potentials [2]. To improve array performance, electrodes were covered with platinum-black. We measured the relative impedance of each electrode to confirm the homogeneity of platinum-black deposition and found negligible variations in impedance among electrodes. In our experiments, we used the CMOS MEA to simultaneously stimulate and sample electrical signals at spatially distant compartments of the same neuron (soma vs. axon) in order to reduce the influence of stimulation artifacts.

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Fig. 1. Averaged electrical signals of a single neuron. Somatic (purple) and axonal (green) “footprints” are reconstructed based on antidromic and orthodromic stimulation-triggered neuronal responses,.

3 Results Somatic and axonal locations of individual neurons were detected and identified according to their spontaneous and/or stimulation-triggered voltage traces (Figure 1) [2]. We used different electrode configurations to investigate somatic and axonal excitability. In a first configuration, “somatic electrodes” were used to apply stimulation and “axonal electrodes” were used for readout of triggered neuronal signals. In a second configuration, electrodes covering preselected areas of the axonal “footprint” were used to apply stimulation, while other available electrodes (axonal and somatic) were used as readout (Figure 2). Stimuli included biphasic voltage pulses



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(between ±10 to 300 mV; 10 mV steps) and were applied 20 times per voltage value at a frequency of 5Hz. Each stimulation electrode was probed with increasing voltages until an evoked signal was detected on the readout electrodes. To get finer resolution excitability profiles, smaller 1 mV steps were used and applied in random order. We found that the most sensitive somatic site lies near the peak amplitude of the somatic voltage trace (8 out of 8 cases). Experiments, in which we probed all electrodes covering axonal locations, revealed higher excitability of the most sensitive somatic over axonal sites (3 out of 4 cases).

spontaneous and/or stimulation-triggered electrical activity. Our preliminary data indicate that highly excitable neuronal sites co-localize with peaks of the reconstructed neuronal “footprint” (Figure 2) and suggest higher sensitivity of somatic over axonal sites to biphasic voltage stimulation. Acknowledgement Supported by the Swiss National Science Foundation Ambizione Grant PZ00P3_132245 and FP7 of the European Community through the ERC Advanced Grant 267351 ‘NeuroCMOS’. References [1]

4 Conclusion/Summary We established a method to reliably investigate somatic and axonal excitability of cortical neurons grown over CMOS HD-MEAs based on their

[2]

Frey, U. et al. (2010) Switch-matrix-based high-density microelectrode array in CMOS technology. IEEE J. Solid-St. Circ. 45, 467–482 Bakkum, D. J. et al. (2013) Tracking axonal action potential propagation on a high-density microelectrode array across hundreds of sites. Nature Commun. 4:2181.

Fig. 2. Responsiveness of somatic (left) and axonal (right) sites to biphasic voltage stimulation.


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Stimulation and Recording from the Same Microelectrode Using Low-Gain High-Resolution Data Acquisition System Hyunjun Jung1, Yoonkey Nam1* 1 Neural Engineering Laboratory, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea * Corresponding author. E-mail address: [email protected]

Abstract Electrical stimulation is able to elicit neural activity by depolarizing the membrane potential. This technique is a powerful tool for clinical and physiological application. During the stimulation, a large artefact, which is thousands of times bigger than the spike, acts as an impulse input to High Pass Filter (HPF) that is essential to remove electrode dc potential. Fluctuating impulse response of HPF is clipped by the narrow input range of high gain amplifier. Because of this interaction between HPF and high gain amplifier, it is impossible to record the direct response on stimulating electrode for several milliseconds. Our goal is to record neural activity directly on a stimulating electrode. We used low gain single stage amplifier and 24-bit ADC to record neural signal without HPF. After stimulus potential from stimulating electrode was recorded totally and we applied revised SALPA algorithm to eliminate stimulus artefact (SA). Consequentially, our system can record neural signals in 2ms after stimulus and we recorded several non-synaptic responses which could not be recorded previously.

1 Introduction Electrical stimulation is one of the essential techniques to modulate neural activity of neural circuits. Measuring neural responses after the electrical stimulation provides important information about the excitability or connectivity of targeted regions [1]. However, it is very difficult to record neural responses immediately after the stimulation due to stimulation artefacts implicated in neural recording system [2]. Despite various approaches (e.g., blanking and discharge circuitry, Wiener filtering, template subtraction method, subtraction of artefacts by local polynomial approximation algorithm)), it is still challenging to recover neural spikes directly from the stimulation electrodes within a few milliseconds [3]. Here we propose a novel neural recoding strategy to recover neural spikes from a microelectrode that was used for both electrical stimulation and recording. We were motivated by the fact that high resolution Analog-Digital Converters (ADC, 16 or 24 bit) are more commonly used in neural instrumentation, thus allowing us to use low-gain wide-bandwidth amplifiers for conditioning microelectrode signals containing large DC drift and small neural spikes. Here, we designed a fast responding data acquisition system and tested for the early recovery of neural spikes from a stimulating electrode.

2 Methods

stimulator output. A non-inverting amplifier was designed with an FET-input OP-AMP (AD823, Anolog Devices, Norwood, USA). The amplifier was operated by 9-V battery to reduce power-line noise. The output of the amplifier stage was connected with 24-bit data acquisition system that was composed of 24-bit ADC and an anti-aliasing filter NI9239 (fs = 25 kHz, National instruments Corp, Austin, USA). To elicit neural responses, biphasic current pulses (amplitude: 1 ~ 20 μA, pulse width: 200 μs) were generated by STG4002 (Multichannel Systems, Reutlingen, Germany). An analog switch was used to connect the stimulator with a microeletrode during the stimulation. We used “SALPA” algorithm [4] that was developed to remove stimulation artefact in real-time neural signal processing. Two parameters (pre-window and post-window length) were tuned to achieve the minimum recovery time and spike conservation ratio. A dissociated hippocampal neuronal culture from E18 rat was used to verify the system performance and a planar-type microelectrode array with platinum black microelectrodes (diameter: 50 μm) was used for electrical stimulation and recording. Continuous or triggered recording of electrode potential, on-line SA suppression were performed by customized LabVIEW program. (National instruments Corp, Austin, USA).

Fig. 1 shows the experimental setup for the proposed neural signal processing system. A microelectrode was connected with both amplifier input and

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(a)

(b)

(b)

Fig. 1. System diagram of the LGHR system (a) and customized LabVIEW program (b)

3 Results The 24-bit ADC provided two advantages in designing the neural recording system: low gain amplification(x10) and wide-bandwidth recording (0 – 12.5 kHz). System noise level was sufficiently small for low-noise neural recordings (input-referred noise: 20 μVpp (1 Hz- 8 kHz filtered)). The input dynamic range was ±875 mV. As there was no analog high-pass filter before the digitization, signal loss from amplifier saturation or clipping was minimized. After the digitization, we attempted to remove slowly recovering transient signals originated from metal-electrolyte double-layer capacitance. Fig 2(a) shows the removal of slowly decaying baseline using digital filters (200 Hz or 500 Hz high pass filter) or SALPA algorithm. We found that an asymmetric local curve fitting window was most effective in obtaining early baseline recovery time (2 ms) and low spike attenuation (64.9 %). Using this system, we were able to record neural spikes from an electrically stimulating microelectrodes reliably. Fig. 2(b) shows a representative recording from a cultured hippocampal neurons. Extracellular spikes as early as 2 ms were clearly recorded. Electrical stimulation can be as large as 30 μA or 500 mV and we can still recover neural spikes after 2 ms. (a)

Fig. 2. Removal of SA and demonstration of direct response from stimulating electrode. Recovered signal through SALPA with 3 parameter sets and 2 HPF (a). Responses from 19 DIV cultured neural network was induced by 200 μs per phase negative first biphasic current pulse. 20 responses were overlaid. (b). Vertical lines near black arrow indicate SALPA artefact. (Black arrow: stimulus onset, Red arrow: spike)

4 Conclusion We designed a simple system which can record neural responses from electrically stimulating electrode in 2 ms after stimulus robustly. Due to wide input dynamic range and omission of HPF, slowly changing after stimulus potential was totally recorded. Also, we revised SALPA for faster recovery and recorded signals in 2ms after stimulus which could not be recorded before. The proposed system can be piggybacked to existing electrical neuro-modulation systems (e.g. deep brain stimulator) to investigate the effect of neural stimulations at the target spot. Acknowledgement This work was supported by National Leading Research Laboratory program (NRF2012R1A2A1A01007327), from National Research Foundation of Korea. References [1]

[2]

[3]

[4]


Y. Jimbo and A. Kawana, “Electrical stimulation and recording from cultured neurons using a planar electrode array,” Bioelectrochemistry Bioenerg., vol. 29, no. 2, pp. 193– 204, Dec. 1992. E. a Brown, J. D. Ross, R. a Blum, Yoonkey Nam, B. C. Wheeler, and S. P. Deweerth, “Stimulus-artifact elimination in a multi-electrode system.,” IEEE Trans. Biomed. Circuits Syst., vol. 2, no. 1, pp. 10–21, Mar. 2008. K. Limnuson, H. Lu, H. J. Chiel, and P. Mohseni, “Real-Time Stimulus Artifact Rejection Via Template Subtraction.,” IEEE Trans. Biomed. Circuits Syst., pp. 1–10, Sep. 2013. D. a Wagenaar and S. M. Potter, “Real-time multi-channel stimulus artifact suppression by local curve fitting.,” J. Neurosci. Methods, vol. 120, no. 2, pp. 113–20, Oct. 2002.

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Combining an Optical Eye Model and MEA Recordings for Testing of Retinal Prostheses John M Barrett1*, Lionel Chaudet2, Mark Neil2, Na Dong3, Xiaohan Sun3, Evelyne Sernagor1, Patrick Degenaar4 1 Institute of Neuroscience, Newcastle University, United Kingdom 2 Imperial College, London, United Kingdom 3 South East University, Nanjing, China 4 School of Electrical and Electronic Engineering, Newcastle University, United Kingdom * Corresponding author. E-mail address: [email protected]

Abstract Typical light stimulation setups used in the investigation of optogenetic retinal prostheses do not take into account the modification of the incoming light by the optics of the eye itself. Thus, we developed an experimental setup that allows multielectrode array recordings from ex-vivo retinas while projecting light from an array of highintensity Gallium Nitride microLEDs through an optical model of the human eye onto the retina. We were able to focus the LED array image onto the retina successfully and evoked channelrhodpsin-2 mediated light responses in healthy and in genetically blind retinas. We will use this system to explore improved strategies for optogenetic retinal prostheses.

1 Background Retinal prostheses seek to restore vision to patients affected by retinal dystrophies (characterised by photoreceptor degeneration) by stimulating the surviving neurons. Current devices return only limited vision, still within the range of legal blindness [1]. One promising approach is optogenetics – photosensitizing the surviving neurons with light-sensitive ion channels or pumps. In our lab, we investigate this approach in-vitro using retinas from transgenic mice with photoreceptor degeneration and retinal ganglion cells (RGCs) expressing the light-sensitive cation channel, channelrhodopsin-2 (ChR2). As ChR2 requires high radiance stimulation, we aimed to demonstrate scalability of microLED (μLED) optical stimulator arrays [2,3] to head-mounted prostheses. In in-vitro studies of optogenetic RGC stimulation, light is typically delivered onto the retina using microscope objectives (e.g. [4]), but this does not take into account how the image is altered by the optics of the eye itself and the virtual reality optics required to deliver it. We therefore designed and built a system for projecting μLED array light through a realistic optical model of the human eye onto isolated mouse retinas, while recording RGC responses using microelectrode arrays (MEAs).

2 Methods The OEMI-7 (Ocular Instruments, Cordova, USA) is an optical model of the human eye for ophthalmological training. We constructed an adapter to attach the OEMI-7 to an MEA1060-Inv amplifier (MultiChannel Systems (MCS), Reutlingen, Germany). We used 60-channel ITO MEAs (MCS) to record

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from isolated B6.Cg-Tg(Thy1-ChR2/EYFP)9Gfng/J (ChR2) mouse retinas with channelrhodopsinexpressing RGCs and retinas from a cross between ChR2 mice and the C3H/HeNHsd mouse model of retinitis pigmentosa (ChR2rd1 mice).

Fig.1. View of the artificial eye and virtual reality lens from below. Light is projected from the LEDs (not pictured), through microlenses and focussing optics onto a mirror (bottom right), then reflected and refracted through the virtual reality lens into the artificial eye (top), which focuses the light on the retina. The blue line shows the approximate path taken by the LED light. The metal plate at the top holds the eye and the MEA amplifier

We provided light stimulation using a 256-pixel microLED array, custom fabricated using 0.35µm CMOS and GaN LED technologies [2,3]. We project-



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ed the LED light onto the MEA holding the retina through a microlens array and projection optics [5] (to improve efficiency and fill factor), an eMagin z800 virtual reality prism lens (eMagin, Bellevue, USA), and the OEMI-7 (Fig 1).

3 Results We successfully focussed the LED array image through the OEMI-7 and the translucent MEA onto the RGC layer (Fig 2) and recorded ChR2-mediated spiking responses. The transmission efficiency was approximately 2.5%, a 3-fold improvement over the 0.86% efficiency of focussing similar LEDs through a 2X objective via the camera port of an inverted microscope (Olympus IX-71; Olympus, Tokyo, Japan).

4 Conclusion We developed an optical system for stimulating retinas in a way that mimics how light would realistically stimulate optogenetically engineered RGCs in retinal dystrophy patients. Our results indicate that microLED arrays are scalable to wearable optogenetic prostheses. We will use this system to explore improved stimulator technology and signal processing strategies for optogenetic retinal prosthetics.

Fig.2. Raster plots and peristimulus time histograms showing responses of an example cell from a ChR2rd1 retina to μLED stimulation at 82.6μW/mm2. Each plot represents responses to a 5, 10, 25, 50, 75 or 100ms full-field light flash.

Fig.2. Photomicrograph of an isolated ChR2rd1 retina mounted on a 60-channel ITO MEA. An image projected by the LEDs is focussed onto the retina by the optical system.

Acknowledgement This work was supported by the Wellcome Trust [096975/Z/11/Z] (JB, ES, PD) and EU FP7 research project OPTONEURO [249867] (LC, PD). References [1]

We were able to evoke light-driven spikes with a minimal irradiance on the retina of 14.3 μW/mm2 (3.4×1013 photons/mm2/s at 470nm) in ChR2 retinas and 82.6μW/mm2 (2.0×1014 photons/mm2/s) in ChR2rd1 retinas (Fig 3). The median threshold light flash duration (i.e. the duration required to evoke a response on 50% of trials) at the stated intensities was 23.3ms and 63.7ms, respectively. The energy supplied to the LEDs for successful full-field stimulation in the ChR2rd1 retinas was approximately 1J, but future improvements in both optoelectronic and optical efficiencies, in addition to improved channelrhodopsin sensitivities, are likely to reduce this. Furthermore, this 1J is equivalent to an all-white image, whereas real world image transfer only requires 10-15% of the scene to be illuminated after retinal processing [6]. As such, the total power requirement, including 0.5-1W for processing, can be kept below the 500 ms) in immature two-week old cultures (Fig. 1B).


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Signal analysis and statistics

Fig. 1. The spontaneous initiation of bursts in dissociated hippocampal cultures remains stochastic during development and can be considered as Poisson process. A. Dramatic changes of spiking and bursting rates during third and fourth weeks in vitro are followed by steady-state stabilization after DIV28. B. Despite large-scale fluctuations of the MBR during development, both the coefficient of variation of IBIs and the Fano factor for bursting rate are ≈1 and remain relatively stable in developing and mature hippocampal cultures grown on MEAs.

3 Conclusion Several properties of bursting activity are subject of strong developmental regulation reflecting the maturation of synaptic connectivity and circuitry formation. However, the initiation of bursts in developing neuronal cultures is stochastic Poisson process, and remains as such upon maturation. Our data suggest that bursts represent a rather complex phenomenon with distinct probabilities of the first spike and the following ones in each individual bursting episode.

Acknowledgement This work was supported by the Federal State Sachsen-Anhalt (grant LSA RG Molecular Physiology) and the DFG (grant HE-3604/2-1). References [1]

[2] [3]


Corner, M.A. (2008). Spontaneous neuronal burst discharges as dependent and independent variables in the maturation of cerebral cortex tissue cultured in vitro: A review of activitydependent studies in live ‘model’ systems for the development of intrinsically generated bioelectric slow-wave sleep patterns. Brain Research Reviews, 59, 221-244. Kaech, S., Banker, G. (2006). Culturing hippocampal neurons. Nature Protocols, 1, 2406-2415. Marom, S., Shahaf, G. (2002). Development, learning and memory in large random networks of cortical neurons: lessons beyond anatomy. Quarterly Reviews of Biophysics, 35, 63-87.

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Selective Laser Ablation of Interconnections Between Neuronal Sub-Populations: A Test-Bed for Novel Neuroprosthetic Applications Alberto Averna1#, Marta Bisio1#, Valentina Pasquale1, Paolo Bonifazi2, Francesco Difato1 and Michela Chiappalone1* 1 Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genova, Italy 2 School of Physics and Astronomy, Tel Aviv University (TAU), Israel # Equal Contribution * Corresponding author. E-mail address: [email protected]

Abstract Cortical and hippocampal patterned networks, composed of inter-connected modules of different size, were grown on commercially available Micro-Electrode Arrays to investigate neurodynamics. Such patterned networks, realized by means of multi-compartmental polydimethylsiloxane structures, represent an in vitro model to study electrophysiological activity of damaged neuronal networks in the central nervous system. A laser microdissector has been used to selectively cut the neural connections between different modules, thus mimicking a pathological condition. Spontaneous and evoked activity of modular neural networks has been characterized in both healthy and damaged cultures.

1 Background In the last decades, great efforts have been made to focus on studying neuro-prostheses targeting lesions at the level of the CNS and aimed at restoring lost cognitive functions [1, 2]. Simplified in vitro models of cell assemblies can provide useful insights for the design of future cognitive brain prostheses. In particular, studies of healthy and lesioned in vitro neuronal networks can be exploited as a test-bed for the development of innovative neuro-prostheses [3]. To investigate the inherent properties of neuronal cell assemblies as a complement to artificial computational models, engineered networks of increasing structural complexity, from isolated finite-size networks up to inter-connected modules, were grown on Multi Electrode Arrays (MEAs). Here, we characterized the spontaneous and evoked activity of 2D neuronal cultures, before and after selective laser dissection of physical connections between neuronal subpopulations [4]. The quantitative characterization of the lesion-induced changes in activity represents a reproducible injury model to possibly test innovative ‘brain-prostheses‘.

2 Methods All experimental procedures and animal care were conducted in conformity with institutional guidelines, in accordance with the European legislation (European Communities Directive of 24 November 1986, 86/609/EEC). Primary neuronal cultures were obtained from cerebral cortices and hippocampi of rat embryos at gestational day E18. In order to provide the physical 216

confinement of neuronal cultures, a polymeric structure in polydimethylsiloxane (PDMS), composed of different modules, has been realized through soft lithography [5]. The PDMS mask is positioned on the MEA substrate before the coating procedure, performed by putting a 100-µl drop of laminin and polyD-lysine solution on the mask and leaving it in the vacuum chamber for 20 minutes. Then, the mask is removed and cells plated afterwards. The used MEAs (Multi Channel Systems, Reutlingen, Germany) can present three different geometrical layouts: “4Q”, where 60 electrodes are organized in 4 separate quadrants; “8x8”, where electrodes are placed according to a square grid; 6x10, rectangular grid. The nominal cell density is around 500 cells/μl (~100 cells per network module). Experiments on both cortical and hippocampal modular networks, recorded between 20 and 25 Days In Vitro (DIVs), have been carried out. Results have been obtained from a dataset of 17 modular networks (7 hippocampal and 10 cortical). The used experimental protocol consisted of 5 consecutive phases: i) One-hour recording of spontaneous activity; ii) Stimulation session I, which consists of stimulating at least two electrodes per cluster using a train of 50 positivethen-negative pulses (1.5 V peak-to-peak, duration 500 µs, duty cycle 50%) at 0.2 Hz; iii) Laser ablation of inter-cluster neural connections, whose aim is to isolate a cluster which is physically and functionally connected to at least another one; iv) One-hour recording of spontaneous activity after performing the lesion; v) Stimulation session II, from the same electrodes of phase ii.


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The normal distribution of experimental data was assessed using the Kolmogorov-Smirnov normality test. According to the distribution of the data, we performed either parametric or non-parametric tests and p-values < 0.05 were considered significant.


tive neuro-prostheses aimed at restoring lost neuronal functionality between distinct brain areas, at the level of the central nervous system.

Fig. 1. Optical micrograph of a patterned culture (hippocampal neurons, 20 DIV), composed of 3 cell clusters (or modules) over a 4Q MEA. The same culture was monitored before (left) and after (right) the optical lesion. It is visible that neural connections between the upper left cluster and all the others were cut by the laser. Screen bar 200 μm.

3 Results Depending on the distance between modules, the module networks are able or not to establish spontaneous neuronal connections among them. Fig. 1 reports optical images of a representative culture before (left) and after the optical lesion (right). We analysed both spontaneous and evoked activity of modular networks before and after lesion. Regarding spontaneous dynamics, the strong synchronization of activity observed among the clusters before the lesion (Fig. 2A), disappears almost completely after it (Fig. 2B). Moreover, a global decrease in the network mean firing rate is observed after the lesion, both inside the isolated cluster, and in all the other clusters that were previously connected to the isolated one. Before performing the lesion, electrical stimulation was able to evoke activity both within the cluster hosting the stimulating channel and in the connected modules (Fig. 2C, black profiles). After laser dissection, the evoked activity remains confined within the isolated cluster without spreading towards the other ones (Fig. 2C, red profiles). Figure 2D shows that a global decrease in the evoked activity after the laser cut is present in both local and distal responses. Since lesion is aimed at reducing the amount of connections among clusters, inter-cluster responses are more affected than intra-cluster ones.

4 Summary We showed that selective laser dissection of interconnections among neural assemblies affects both spontaneous and evoked activity of modular networks, by inducing de-synchronization between the different modules during spontaneous activity, and preventing propagation of evoked responses among modules. Those preliminary results present a reproducible experimental model, which can be used as a test-bed for the design and development of innova-

Fig. 2. Comparison of spontaneous and evoked activity of healthy and damaged modular cultures. A, B. 5-minute raster plot of spontaneous activity of a representative patterned network before (A) and after (B) lesion. The activity of different clusters (i.e. C1, C2 and C3) has been highlighted in different colors. The blue one (C1) is the isolated cluster. C. Post-stimulus time histograms (PSTH) obtained when stimulating a channel belonging to the isolated cluster. The electrodes labelled in blue belong to the isolated cluster, while the black cross indicates the stimulated channel. Black curves report the evoked activity before the lesion, while the red ones refer to the evoked activity after it. D. Comparison between the statistical distributions of normalized PSTH areas (i.e. ratio between post-lesion PSTH areas over pre-lesion ones), either within the stimulated cluster (Intra Cluster) or in all the other ones (Inter Cluster). Statistical analysis has been performed by using the Mann-Whitney test [*p95%) is obtained bubbling air inside a bottle filled with water, with temperature regulated by the RH reading from a 306

miniaturized sensor in the chamber (Sensirion Inc.). The CO2 is maintained to 5% by the use of a 5% CO2 gas mixture and monitored by an infra-red sensor (Gas Sensing Solution Ltd). MEA signals are collected by custom pre-amplifiers inside the chamber and external filter-amplifiers (gain:1100, bandwidth:300Hz-3kHz) and acquired by a commercial device (Multichannel Systems GmbH). The internal boards are sealed to avoid damaged due to the high RH% level. Hippocampal cultures on 60 channel MEAs (Multichannel Systems) were positioned in the chamber between the second and the third week in vitro and monitored thereafter for periods ranging from 12 hours to several days in a row. If necessary, the medium exchange was performed from outside with PTFE tubes (330μl every 48h) (ALA-MEA-MEM-PL caps, MultiChannel Systems). Spikes were detected by comparison with a threshold (-5*standard deviation of noise), updated every 12 hours, and analysed with McRack (Multichannel Systems).

3 Results The maintenance of adequate environmental conditions in the chamber allowed to monitor the spontaneous behaviour of neuronal networks, i.e. not influenced by unforeseen environmental fluctuations. Figure 1 reports a 60 hours snapshot of activity (13-14 DIV), where an activity alteration following the culture medium change (i.e., an induced perturbation to network activity) is clearly visible.


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Fig. 1. Mean firing rate (Hz) and mean burst rate (burst/min) profiles exhibited by an hippocampal culture recorded in the climatecontrolled chamber. It is shown that the spontaneous activity is perturbed by the medium change procedure (orange arrow).

Figure 2 reports raster plots showing the occurrence of spiking activity and the mean firing rate exhibited by a culture maintained in the setup for six days (15-20 DIV). Differences over time reflecting the maturation of the network (i.e., re-organization of patterns of synchronous activity) were observed.

MEA technology: Fabrication and instrumentation

cultured networks activity over days. The possibility to maintain cultures on MEAs in a controlled environment and constantly connected to the recording stage avoids any stress to cells, differently from repeated short-term recordings, which imposes to move cells from the incubator to the recording stage repeatedly over the culturing period [7]. Applications such as maturation studies, functional degeneration or effects of chronic neuropharmacological treatments are examples of experiments which can benefit from this device. Acknowledgement We thank Alembic facility for its support and Dr. De Ceglia for the dissection of hippocampi. We are very grateful to Karl-Heinz Boven, Andreas Grall and all the people from Multi Channel Systems GmbH who supported us in the production of custom printed circuit boards. References [1]

[2]

[3]

[4]

[5]

[6]

[7]

Lignani G. et al (2013). Long-term optical stimulation of channel rhodopsin-expressing neurons to study network plasticity. Front Molecular Neuroscience, 6(22). Novellino A. et al (2011). Development of micro-electrode array based tests for neurotoxicity: assessment of interlaboratory reproducibility with neuroactive chemicals, Front. Neuroeng, 4(4). Potter S.M. and DeMarse T. (2001). A new approach to neural cell culture for long-term studies. J Neurosci Methods, 110, 17-24. Martiniuc A.V. et al. (2012). Tracking the evolution of neural network activity in uninterrupted long-term MEA recordings. Proceedings of MEAMeeting 2012, Reutlingen. Biffi E. et al. (2012). A novel environmental chamber for neuronal network multisite recordings. Biotechnol Bioeng, 109, 2553-2566. Regalia G. et al. (2013). An experimental platform aimed at long lasting electrophysiological multichannel recordings of neuronal cultures. Proceed of the 5th International Joint Conference on Computational Intelligence, doi: 10.5220/0004659005510557, 2013. E. Biffi, G. Regalia, et al. (2013). The influence of neuronal density and maturation on network activity of hippocampal cell cultures: a methodological study. Plos One. 8(12).

Fig. 2. Raster plots showing the occurrence of spiking activity in the same hippocampal culture maintained in the controlled environmental chamber over different days in vitro (DIVs). Each panel represents the activity of 60 channels over a 10 minute snapshot and was extracted every 24 hours of recordings. The mean firing rate (Hz) is reported in the top right corner of each raster plot.

4 Conclusion/Summary The chamber presented here constitutes an effective experimental platform to obtain environmental stability during MEA experiments lasting more than few hours. We presented here preliminary data demonstrating that it allows to follow the evolution of


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Development of a Stand-alone Integrated MEA Biochip System for Chronic Recordings Paolo De Pasquale1,2; Antonin Sandoz 1; Igor Charvet 1; Philippe Passeraub 1; Michela Chiappalone 2; Luc Stoppini 1 1 Hepia, HES-SO Geneva, Switzerland; 2 Istituto Italiano di Tecnologia (IIT), Genova, Italy * Corresponding author. E-mail address: [email protected]; [email protected]

Abstract Stand-alone integrated MEA system were developed and tested to investigate the effect of toxic compounds on network neural activities during chronic recordings. The system, consisting of specifically designed MEA aim to receive microfluidic organic substances and to communicate using wireless technology with the computer device. The system has been tested with several types of 2D and 3D neuronal cultures. The electrophysiological data are processed by NeuroSpy, a modify version of SpyCode. The software developed is able to analyze the basal activity, drug and stimulation evoked response.

1 Background "Cell biochips" containing engineered tissue interconnected by a microfluidic network allows the control of microfluidic flows for dynamic cultures by continuously feeding of nutrients and waste removal. Thus, these types of systems can enhance functionality of cells by mimicking the tissue architecture complexities when compared to in vitro analysis, but at the same time present a more rapid and simple process when compared to in vivo testing procedures. To date, one of the most promising tools for neuropharmacological functional tests is the Micro-Electrode Array (MEA). MEAs have been applied to study multiple aspects of electrically excitable cells and to explore the pharmacological and toxicological effects of numerous compounds on spontaneous activity of electrogenic cells (A. Novellino, T. J. Shafer et al. 2011). As a complementary tool to standard MEA platforms, we are developing a novel stand-alone system able to acquire, stimulate and feed the cultures. This new approach will keep the cultures inside the incubator providing a useful tool for chronic long-term recordings, with the possibility to continuously submit exposure to drugs or chemicals.

tems, Reutlingen, Germany) and adapted to this particular in vitro application. The wireless headstage is directly linked to the chip and integrated into the main body of the system. A specific micro-peristaltic pump, developed in order to feed the culture during the incubating period, is controlled by a dedicated software to operate it via Bluetooth or wireless mode. We developed a modified version of the Spycode software (Bologna et al. 2010), called NeuroSpy (Fig. 2), to perform the analysis of electrophysiological data of neuronal cultures, acquired by MEAs. NeuroSpy allows performing basic and high precision offline analyses . Moreover, the software is able to perform a specific type of analysis aim to study the behaviour of the 2D or 3D neural cultures, as a result of both chemical and electrical stimulation.

2 Methods and Statistics We have developed a small-volume in vitro system in which neural cells/tissues can be cultivated in four separate porous membrane microchambers. These are connected by microchannels with the presence of a MEA in each well (Fig. 1). Each MEA, in this configuration, is composed by 8 recording electrodes plus two additional electrodes which can be used for electrically stimulating the cultures through the STG4002 stimulator (MCS). Electrophysiological activities recorded from the 4 integrated MEAs are transmitted through the Wireless System W32 (developed for in vivo applications by Multi Channel Sys-

Fig. 1. Complete system: MEA chip, wifi system, perfusion pump.

Fig. 2. Analysis software: Neurospy interface.

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3 Results In the first series of experiments, we could record the electrophysiological signals from several types of cells, with the aim at testing the system performance. Basically, the behaviour of the system has been studied with 2D and 3D cells structure, derived from human sphere stem cells, rat brain cortex, rat hippocampus slices and dissociated cells. Fig.3 shows a network burst activity, recorded from a 3D neural network (derived from H9 or iPs stem cells), through the raw data and a raster plot. Fig.4 Shows the Mean Firing Rate and the Mean Bursting Rate from dissociating cells from rat brain cortex. Fig.5 shows the ISI histogram relative to the same experiment of Fig.4 and Fig.5.


test more thoroughly new drugs or chemicals. It will also help to understand the process that causes an organism to react and adapt when exposed to drugs over a long timescales. Moreover, the system will provide scientists with a new independent device able to reduce contaminations factors, due to interactions between the organic components and external agents, thanks to the feeding, recording and stimulating remote wireless control. Scientists will be able to monitor the culture since the first day in vitro, without the risk of expose them to an excessive stress due to the behaviours changing. Statement of financial support: Supported by the HES-SO, CTI/KTI and the SCAHT. References [1]

[2]

[3]

[4] Fig. 3. Electrophysiological activity from a representative experiment of a neural network derived from H9 cells. Top panel, raw data recorded from one electrode. Bottom panel, raster plot depicting the activity from the 32 channels of the array.

[5]

[6]

[7]

Fig. 4. MFR and MBR from a representative experiment of a neural network derived from rat brain cortex. Left panel, Mean Firing Rate (spikes/sec). Right panel, Mean Bursting Rate (Bursts/Min).

Tieng V, Stoppini L, Villy S, Fathi M, Dubois-Dauphin M, Krause KH. Engineering of Midbrain Organoids Containing Long-Lived Dopaminergic Neurons. Stem Cells Dev. 2014 Apr 3. Kern I, Xu R, Julien S, Suter DM, Preynat-Seauve O, Baquié M, Poncet A, Combescure C, Stoppini L, Thriel CV, Krause KH. Embryonic stem cell-based screen for small molecules: cluster analysis reveals four response patterns in developing neural cells. Curr Med Chem. 2013;20(5):710-23. Krug AK, et al. A. Human embryonic stemcell-derived test systems for developmental neurotoxicity: a transcriptomics approach. Arch Toxicol. 2013 Jan; 87(1):123-43. doi: 10.1007/s00204-012-0967-3. Epub 2012 Nov 21. Sundstrom L, Biggs T, Laskowski A, Stoppini L. OrganDots-an organotypic 3D tissue culture platform for drug development. Expert Opin Drug Discov. 2012 Jun;7(6):525-34. doi: 10.1517/17460441.2012.686488. Epub 2012 May 19. Bologna, L. L.; Pasquale, V.; Garofalo, M.; Gandolfo, M.; Baljon, P. L.; Maccione, A.; Martinoia, S. and Chiappalone, M.Investigating neuronal activity by SPYCODE multichannel data analayze.Neural Networks, 23 (6): 685-697, 2010. Chiappalone, M., Novellino, A., Vajda, I., Vato, A., Martinoia, S. & van Pelt, J. (2005) Burst detection algorithms for the analysis of spatio-temporal patterns in cortical networks of neurons. Neuro-computing, 65-66, 653-662. A. Novellino, Bibiana Scelfo, T. Palosaari, A. Price, Tomasz Sobanski, T. J. Shafer, A. F. M. Johnstone, G. W. Gross, A. Gramowski, O. Schroeder, K. Jügelt, M. Chiappalone, F. Benfenati, S. Martinoia, M. T. Tedesco, E. Defranchi, P. D’Angelo and M. Whelan. (2011) Development of microelectrode array based tests for neurotoxicity: assessment of interlaboratory reproducibility with neuroactive chemicals. Front. Neuroeng., 27 April 2011

Fig. 5. ISI histogram from a representative experiment of a neural network derived from rat brain cortex

4 Conclusion These human surrogate integrated Biochips will enable the determination of toxicological profiles of new drug candidates. In addition, the developed system will provide scientists with alternative ways to 9th Int. Meeting on Substrate-Integrated Microelectrode Arrays, 2014

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3D Multi Electrode Arrays Martin Baca1*, Heike Bartsch2, , Tom Porzig2, Adam Williamson1, Andreas Schober1 1 FG Nanobiosystemtechnik, Technische Universität Ilmenau (TUI), Germany 2 FG Elektroniktechnologie, Technische Universität Ilmenau (TUI), Germany * Corresponding author. E-mail address: [email protected]

Abstract Neurobiological concepts derived from measurements provided by the current generation of MEA technology have so far lacked the complexity of actual high-level neurobiological systems. Two key advances are needed to improve our understanding of such systems: in vivo 3D-neuronal cell cultures (including multiple types of neural cells), and 3D MEA systems for measuring this 3D-cultures [1][2][3]. We have constructed an in vitro measurement system for exactly such 3D-neuronal cell cultures. The principal cultures to be investigated with this system will be neural cells derived from human stem cells.

1 Requirements

2 MEA design and manufacturing

Neurobiological concepts derived from measurements provided by the current generation of MEA technology have so far lacked the complexity of actual high-level neurobiological systems. Two key advances are needed to improve our understanding of such systems: in vivo 3D-neuronal cell cultures (including multiple types of neural cells), and 3D MEA systems for measuring this 3D-cultures [1][2][3]. Two major technical specifications have been taken into account for the 3D-MEA design: 1.Culture Thickness - The standard method of measuring the electroactivity of cell cultures and slices is the use of a 2D-MEA beneath the culture with a typical electrode diameter of 30-100 µm and a typical distance between electrodes of 100 µm. However, the typical thickness of a 3D culture can exceed a few hundred micrometers. Clearly for better 3D spatial and temporal resolution of cultures, one would ideally use a 3D-MEA capable of reaching at least a few hundred micrometers into the cultures. 2. Incubator Compatibility - For measuring standard electroactivity in 2D, cultures are grown on the 2D MEA itself. The MEA on which the cells are growing is moved from the incubator to the amplifier system each time a measurement is desired. The culture is enclosed in a housing to prevent contamination. Standard commercially available 3D-MEA systems use sets of prongs inserted into a 3D culture from above. This requires the removal of the housing lid, making placement of the culture back into the incubator impossible and also disregards the cellular damage caused to the 3D culture during MEA insertion. The ideal situation for 3D cultures is a 3D-MEA which is compatible with the incubator conditions.

The above mentioned requirements call for smart multilayer and packaging technology. Low temperature cofired ceramics (LTCC) are chosen as material system for the MEA design because those ceramics enable complex multilayer assemblies and feature excellent temperature and solvent stability. In our case we decided to use Green Tape TM 951 form DuPont Nemours. The compatibility of the material and available metallisation with cell cultures was already proven [5]. An LTCC multilayer board with gold electrodes is the base of the 3D MEA. Fehler! Verweisquelle konnte nicht gefunden werden. depicts a schematic cross section of the assembly. LTCC vias (material DP 5738 from DuPont) serve as electrodes in cell contact. They are interconnected via the buried wiring and a 90° solder contact with printed gold pads (material DP 5742 from DuPont). The layout of these gold pads is designed to fit the MEA2100-System for in vitro recording from Multi Channel Systems and enable thus a comparable data processing to established 2D MEAs

Fig. 1. Schematic cross section of the LTCC assembly

Slots with dimensions of 450 µm are laser cut into the base plate for the assembly of three finger electrode bars, which carry 3 electrode fingers with 3 gold

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contacts each. These bars are manufactured separately using LTCC technology. The outer contour of the fingers is laser cut. There exist no reliable data for useful electrode pitches and dimensions applied to 3D cultures so far. Hence, two different finger electrode layouts were manufactured, one with small fingers and one with tall fingers. Their dimensions and electrode pitches are presented in Fig. 2. The fingers have a thickness of 400 µm and the diameter of the gold electrodes in cell contact is 85 µm. In addition to the 3D electrodes on the fingers each MEA is equipped with 3x10 2D electrodes, which are situated in the near vicinity of the bars and one 2D reference electrode


3 Conclusion Future work will now increase the number of sensors from 9 per microtower up to 80. Due to the small signal amplitude the amplification should be as close as possible to the sensor. An ASIC (Application Specific Integrated Circuit) has been designed which is able to manage 8 sensors per finger, and 10 fingers per microtower. An arbitrary number of ASIC amplified microtowers can be placed in parallel. Additionally a version of the MEA presented here, with a transparent base plate is in production, to include imaging with electrophysiological measurement of 3D-cultes [4]. Acknowledgement This research has been supported by the 3DNeuroN project in the European Union's Seventh Framework Programme, Future and Emerging Technologies, grant agreement n°296590. References

a)

[1] [2]

[3]

[4] [5]

b) Fig. 2. Finger dimension for both types

http://www.3dneuron.eu/ Klefenz F, Williamson A (2013): Modeling the formation process of grouping stimuli sets through cortical columns and microcircuits to feature neurons, Computational Intelligence and Neuroscience Volume 2013, Article ID 290358, 10 pages http://dx.doi.org/10.1155/2013/290358 Husar P, Laqua D, Fischer M, Lilienthal, K, Hampl, J, Fernekorn U, Schober A (2010): Hybrid three-dimensional sensor array, in particular for measuring electrogenic cell assemblies, and measuring assembly. DE 10 2010 000 565.7 // PCT/EP2011/052638 Just T, Husar P (2014) Amplifier ASIC for multichannel and real 3D-MEA Bartsch de Torres H, Rensch C, Fischer M, Schober A, Hoffmann M, Müller J (2009). Thick film flow sensor for biological microsystems. Sensors and Actuators A: Physical, 160, 109-115 http://dx.doi.org/10.1016/j.sna.2010.04.010

After the finger assembly the MEA is equipped with a ring, which serves as a culture chamber. The ring consists of fused silica and it is bonded on the LTCC surface using silicone adhesive (LOCTITE® SI 5366™). Fehler! Verweisquelle konnte nicht gefunden werden. depicts a complete MEA with tall fingers and details of both LTCC electrode fingers. In a next step the MEAs are prepared for the use with cell medium.

Fig. 3 MEA with cell culture ring and finger types


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The Influence of Medium Conductivity on Cellular Impedance Measurements with Field-Effect Transistor Arrays Felix Hempel, Nathalie Bernhard, Jessica Ka-Yan Law, Anna Susloparova, Dieter Koppenhöfer, Xuan-Thang Vu and Sven Ingebrandt* Department of Informatics & Microsystem Technology, University of Applied Sciences Kaiserslautern, Zweibruecken, Germany * Corresponding author. E-mail address: [email protected]

Abstract In the present study the influence of measurement media with different conductivities on cell impedance measurements was investigated. HEK 293 cells were cultured on the field-effect transistor surfaces and their transistor transfer function was measured in different buffer solutions. Changing the conductivities of the measurement buffers is strongly influencing the measured spectra. By fitting the data to an established model, the change of the seal resistance and the membrane capacitance due to the conductivity of the media can be calculated. The derived cell-related data advance our understanding in cell-transistor interface modelling.

1 Introduction Impedance spectroscopy with field-effect transistors (FETs) has been used for several biosensing applications, including DNA detection [1] and cell-substrate adhesion studies [2]. Unlike the commercially available Electric Cell-Substrate Impedance Sensing (ECIS) system, our FETs have smaller sensor sizes, which enable the detection of single cell-substrate adhesion events. With our in house-fabricated system we specialized in such single cell impedance measurements [3], [4] and the interpretation of the measured data [5]. The essential, cell-related parameters for our sensing platform are the membrane capacitance (CM), which reflects the morphology of the attached cell and the seal resistance (Rseal), which represents the adhesion strength between cell and transistor gate. The seal resistance should scale with the conductivity of the solution [5] resulting in a stronger electronic coupling between cell and transistor gate. In this study we conducted experiments to validate our model for cell impedance measurements and to study the influence of the conductivity of the solutions on the transistor-transfer function (TTF). Data fitting can be performed to optimize cell impedance measurements with FET devices by providing values of Rseal and CM for different experimental configurations using an electrically equivalent circuit (EEC). The EEC mainly uses the well-known point-contact model for FET devices in contact with adherent cells [5].

2 Methods HEK 293 cells were cultured on our FET devices (gate width: 8 µm and length: 5 µm) without additional coating. The devices were fabricated

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similar to the protocol described in a previous publication [6]. The electronic readout was performed with our TTF amplifier. A 10 mV signal with a frequency ranging from 1 Hz to 1 MHz was applied to the Ag/AgCl pseudo reference electrode to measure the TTF spectra. The measurement solutions were prepared as following:  Sodium containing buffer: NaCl 135mM, CaCl2 1.8 mM, MgCl2 1 mM with a conductivity of 25.8 mS/cm.  Sodium free buffer: KCl 5mM, CaCl2 1.8 mM, MgCl2 1 mM with a conductivity of 2.21 mS/cm. Both solutions were adjusted to pH 7.4 and to the same osmolarity of 340 mOsm/kg by adding glucose.

Fig. 1. Schematic of a cell-covered field-effect transistor to explain the configuration for transistor-transfer function measurements [5].

3 Results The TTF measurements were performed with cell-covered gates and with cell-free gates, after trypsinization. The FETs were characterized before each measurement and operated in a working point with highest transconductance value (gm). In this configuration the sodium-containing solution mimics


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the standard culture medium, while the sodium-free solution drastically reduces the conductivity. The measurements of the transfer function show significant differences for cell-covered and cell-free gates, for both measurement solutions (Fig. 1). A drop of the transfer function can be observed when cells are adherent on top of the transistor gate (Fig. 2). The same result can be seen when the bathing solution was changed to the sodium-free solution. The graphs show that the drop in transfer function for sodium-containing solution generally happens at higher frequencies. This drop is shifted to lower frequencies for the sodium-free solution, where the drop can be seen at 10 kHz already.

Fig. 3. Fitted curves for a) sodium containing buffer and b) in sodium-free buffer.

Acknowledgements This work was supported by the Federal Ministry of Education and Research in Germany under projects 17008X10 and 17042X11. References [1]

[2] Fig. 2. Transfer function measurements with a) sodium containing buffer and b) in sodium-free buffer.

By fitting the data of the measurements the values of Rseal and CM can be obtained [5]. Figure 3 shows an exemplary measurement with the fitted curve for the adherent cells. The data were fitted such that Rseal and the resistance of the reference electrode (Rel) change with changing conductivity of the solution. Following values resulted for the sodium-containing buffer Rseal: 1.34106, CM: 1.8610-12 and Rel: 1.5103 and for the sodium-free buffer Rseal: 1.47107, CM: 1.7210-12 and Rel: 1.9103. The fitted curves show an increase of Rseal by by one order of magnitude when changing to measurement buffers with lower conductivity.

[3]

[4]

[5]

[6]

S. Ingebrandt, Y. Han, F. Nakamura, A. Poghossian, M. J. Schöning, and A. Offenhäusser (2007), Label-free detection of single nucleotide polymorphisms utilizing the differential transfer function of field-effect transistors, Biosensors and Bioelectronics 22, 2834–2840. S. Schäfer, S. Eick, B. Hofmann, T. Dufaux, R. Stockmann, G. Wrobel, A. Offenhäusser, and S. Ingebrandt (2009), Timedependent observation of individual cellular binding events to field-effect transistors, Biosensors and Bioelectronics, 24, 1201–1208. A. Susloparova, D. Koppenhöfer, X. T. Vu, M. Weil, and S. Ingebrandt (2013), Impedance spectroscopy with field-effect transistor arrays for the analysis of anti-cancer drug action on individual cells, Biosensors and Bioelectronics, 40, 50–56. D. Koppenhöfer, A. Susloparova, D. Docter, R. H. Stauber, and S. Ingebrandt (2013), Monitoring nanoparticle induced cell death in H441 cells using field-effect transistors, Biosensors and Bioelectronics, 40, 89–95. A. Susloparova, D. Koppenhöfer, J. K. Y. Law, X. T. Vu, and S. Ingebrandt, Electrical Cell-substrate Impedance Sensing with field-effect transistors is able to unravel cellular adhesion and detachment processes on a single cell level, submitted. A. Offenhäusser, C. Sprössler, M. Matsuzawa, and W. Knoll (1997), Field-effect transistor array for monitoring electrical activity from mammalian neurons in culture, Biosensors and Bioelectronics, 12,819–826.

4 Conclusion The seal resistance is a crucial factor for cellsubstrate adhesion measurements with FETs. We observed clear differences in measured impedance signals with different conductivity of the measurement medium. By fitting the data we are able to determine the relations between measurement medium and cell adhesion spectra of our system.


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Organic Field-Effect Transistor Arrays as a Possible Low-Cost Alternative for In-Vitro Monitoring of Different Cell Cultures Lotta Emilia Delle1, Anna Susloparova1, Xuan Thang Vu1,2, Olga Fominov1, Felix Hempel1, Jessica Ka-Yan Law1, Sven Ingebrandt1* 1 Department of Informatics & Microsystem Technology, University of Applied Sciences Kaiserslautern, Zweibruecken (Germany) 2 Present address: Department of Physics IA, RWTH Aachen University, Aachen (Germany) * Corresponding author. E-mail address: [email protected]

Abstract In this work micro-scale, organic field-effect transistor (OFET) devices based on polymeric semiconductor materials as an active channel are presented and used to study the cell-substrate adhesion by impedance spectroscopy and to record extracellular signals from cardiac myocyte cultures. As an organic semiconductor poly(3,4ethylenedioxythiophene) doped with poly(styrene sulfonate) (PEDOT:PSS) was used. The electronic bandwidth of the micro-scale devices is mainly limited by the channel length and the carrier mobility in the material. In order to improve this performance a fabrication process for nano-sized OFET devices was developed with the aim to compare the nano-scale devices to their micro-scale counterparts.

1

Introduction

Biosensors based on silicon field-effect devices offer a wide range of possible applications. Main advantages as high sensitivity to changes in the surface potential and a possible miniaturization in size favor the detection of biomolecules [1], enzymatic activities [2] and cellular signals [3]. Disadvantages are high production costs and the typically opaque substrates, which interfere with standard cell culture microscopy. Devices based on organic polymers provide a cheap and transparent alternative. The conductive polymer PEDOT:PSS is a common material for applications in chemical and biological sensing [4].

Fig. 1. Schematics of the OFET device cross-section and of the measurement setup [6].

In the present work the fabrication process of organic field-effect transistor (OFET) devices based on PEDOT:PSS is presented. In order to increase the electrical perfomance of the devices, gold interdigitated electrodes were used as a transistor gate for cell adhesion studies. The measurements show a significant difference between cell-covered and cell-free transistor gates, when sensors are read out in the socalled transistor transfer function (TTF) method typically used in our studies [5,6,7]. The TTF spectrum represents the combined bandwidth limiting effect of

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reference electrode, electrolyte solution, attached cell, transistor and first amplifier stage. The frequency dependent response of the attached cell to an ac voltage can be used to extract the characteristics of the attached cell (Fig. 1).

2

Materials and Methods

The OFET devices used in this study were fabricated on glass substrates. Micro-sized gold interdigitated source and drain electrodes (IDEs) were fabricated by a lift-off process on glass substrates and PEDOT:PSS was used as an active layer. Nano-sized interdigitated electrodes (IDE) with spacings of 5 or 10 µm between electrode fingers were fabricated by nanoimprint lithography in combination with optical lithography for the source and drain contacts of the OFETs. After electrical passivation of the gold contact lines, the IDE areas were opened and used as electric contact to the polymer layer. The PEDOT:PSS solution was inkjet-printed by means of a material printer (Dimatix Material Printer 2831, Fujifilm Inc., USA). After printing, ethylene glycol was spin-coated and devices were subsequently annealed. The use of ethylene glycol leads to an increase in conductivity improving the carrier mobility and the electronic performance of the devices [8]. Most importantly annealing enhances the durability of PEDOT:PSS in aqueous media enabling long-term cell cultures. Chips were connected to a carrier via wire bonding and encapsulated for cell culture. HEK293 cells were cultured for adhesion assays and HL1 cardiac cells for electrical recordings of action potentials under standard conditions on the OFET arrays.


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3 Results and Discussion The micro-sized OFET devices were developed with the objective to investigate Electric CellSubstrate Impedance (ECIS) [9] or to record electrical activity from cardiac myocytes [10]. For the ECIS assays HEK293 were cultured on the OFET devices and after 2 days in vitro (DIV) TTF spectra with cells and after cell removal using trypsin were compared (Fig. 2). For recording from cardiac myocyte cultures, HL1 cells were cultured and electrical activity was monitored after 2-3 DIV. Changes in impedance spectra were clearly monitored in case of the ECIS assays. In order to confirm that the measured effect is influenced by the attachment of the cells to the transistor surface and not by the degradation of the device, control measurements were performed. For the electrical recordings from cardiac myocyte cultures the bandwidth of the micro-scale devices is still too narrow so that only slow components of the extracellular signal shapes were visible (Fig. 3). Nevertheless, first action potentials were recorded demonstrating the general possibility to apply such devices for cell culture monitoring.

Fig. 4. Nano-sized IDE imprinted in an intermediate polymer stamp.

4

Conclusion

In this study, we demonstrate a straightforward fabrication process for OFETs suitable for different in vitro assays. It can be stated that the OFET devices can be used to analyze cell adhesion. Bandwidth of micro-scale OFETs is wide enough to record slow components of action potentials from cardiac myocyte cultures. The performance is influenced by the channel length of the devices, which can further be downscaled by fabrication with nanoimprint lithography. In general, OFETs offer a low-cost alternative to the already commercialized ECIS systems based on gold electrodes and eventually as single-use alternative to the silicon-based FET arrays for action potential detection. References [1]

Fig. 2. ECIS assays with OFET devices. In general, the bandwidth of the micro-scale devices is too narrow to enable high-performance extracellular recordings. However, differences of cell-adhered to cell-detached situation are clearly visible.

Fig. 3: Recording of action potentials from beating HL1 cell cultures after 3 DIV. Four channels were selected out of the 16-channel OFET array. Due to the limited bandwidth of the micro-scale OFETs, only slow frequency components of the field-potentials are visible.

To optimize the performance of the OFET devices, nano-sized IDE gate structures were fabricated (Fig. 4) by nanoimprint lithography in order to characterize them in terms of sensitivity and bandwidth compared to the micro-sized OFET devices.

Ingebrandt S., Yeung C.K., Krause M., Offenhäuser A. (2007). Label-free detection of single nucleotide polymorphisms utilizing the differential transfer function of field-effect transistors. Biosens. Bioelectron., 22, 2834–2840 [2] Katz E., Willner I. (2003). Probing Biomolecular Interactions at Conductive and Semiconductive Surfaces by Impedance Spectroscopy: Routes to Impedimetric Immunosensors, DNASensors and Enzyme Biosensors. Electroanalysis, 15, 913–947 [3] Ingebrandt S., Yeung C.K., Krause M., Offenhäusser A. (2001). Cardiomyocyte-transistor-hybrids for sensor application. Biosens. Bioelectron., 16, 565 – 570 [4] Khodagholy D., Rivnay J., Sessolo M., Gurfinkel M., Leleux P., Jimison L. H., Stavrinidou E., Herve T., Sanaur S., Owens R. M., and Malliaras G. G. (2013). High transconductance organic electrochemical transistors. Nat. Commun., 4, 2133 [5] Schäfer S., Eick S., Hofmann B., Dufaux T., Stockmann R., Wrobel G., Offenhäusser A., Ingebrandt S. (2009). Timedependent observation of individual cellular binding events to field-effect transistors. Biosens. Bioelectron., 24, 1201-1208 [6] Susloparova A., Koppenhöfer D., Vu X.T., Weil M., Ingebrandt S. (2013). Impedance spectroscopy with field-effect transistor arrays for the analysis of anti-cancer drug action on individual cells. Bios. Bioelectron., 40, 50-56 [7] Koppenhöfer D., Susloparova A., Docter D., Stauber R.H., Ingebrandt S. (2013). Monitoring nanoparticle induced cell death in H441 cells using field-effect transistors. Bios. Bioelectron., 40, 89-95 [8] Yamashita M., Otani C., Shimizu M., Okuzaki H. (2011). Effect of solvent on carrier transport in poly(3,4ethylenedioxythiophene)/poly(4-styrenesulfonate) studied by terahertz and infrared-ultraviolet spectroscopy. Appl. Phys. Lett., 99, 143307 [9] Giaever I. and Keese C. R. (1993). A morphological biosensor for mammalian cells. Nature, 366, 591–592 [10] Stett A., Egert U., Guenther E., Hofmann F., Meyer T., Nisch W., and Haemmerle H. (2003). Biological application of microelectrode arrays in drug discovery and basic research. Anal. Bioanal. Chem., 377, 486–495


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Oriented Cortical Networks in Polydimethylsiloxane Microchannel Scaffolds for Recordings from Network Modules and Individual Connections Rouhollah Habibey1*, Asiyeh Golabchi1, Anilkrishna Konduri1, Marina Nanni1, Francesco Difato1, Axel Blau1 1 Fondazione Istituto Italiano di Tecnologia (IIT), Dept. of Neuroscience and Brain Technologies (NBT), Via Morego 30, 16163, Genoa, Italy, www.iit.it * Corresponding author. E-mail address: [email protected]

Abstract Compartmentalized PDMS devices are used for producing cortical or hippocampal networks with defined connections of neurites growing through microchannels. Using miniaturized PDMS microchannel devices, we generated two-compartment cortical networks with unidirectional connectivity. The two network modules and their interconnecting axons were accessible for extracellular recording and optical observation or manipulation. Seeding different types of neurons in each compartment may help in understanding the functional properties of oriented networks similar to in vivo models.

1 Introduction

2 Methods

Separate modules of small neuronal networks can be connected by chemical patterning and physical confinements through one or more neurite bundles to examine how sub-networks of neurons interact with each other [1]. Others have previously demonstrated that oriented connectivity between two networks can be created on microelectrode array (MEAs) by sequential seeding of neurons in two PDMS wells connected by microchannels [2]. However, the size of the wells made it almost impossible to record from all parts of each network. Here, we present a new miniaturized and thin PDMS tile for decreasing the network size, thereby making network modules and axons available for recording and optical observation or manipulation. The device is compatible with commercial MEAs and the design can easily be adapted to different electrode configurations.

2.1 PDMS device fabrication The fine PDMS structures were molded by softlithography on SU-8 templates, including two small reservoirs for cell somata, which were connected by eight channels (widths: 30 µm, heights: 7 µm, lengths: 800 µm) (Fig .1). PDMS pre-polymer and catalyst were mixed (10:1), poured on the SU-8 master and a thin film of plastic foil placed on the surface to reduce the overall device thickness to ˂ 200 μm. Big seeding reservoirs were punched on the two far edges of each small reservoir (Fig .2).

A

B Fig. 2. PDMS microdevice with 4 big (A) and 2 small (B) reservoirs which are connected through 8 microchannels (C; h=7 μm, w=30 μm, l=800 μm). The capacity of each big reservoir (r = 1 mm) is equal to 0.628 µl, each small reservoir (h=100 μm, w=2000 μm, l=1400 μm) is equal to 0.28 µl.

C

2.2 Cell culture in microchannel tiles

D

Fig. 1. Fabrication of the PDMS microchannel tile (A-B) and seeding of cortical neurons (C-D). A) Molding of the PDMS device from an SU-8 template, B) punching out of big reservoirs, C) device alignment on a MEA and seeding of the first network module, D) seeding of the second module.

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Cured PDMS tiles were baked for 12h at 100 oC to crosslink any remaining oligomers. MEAs (electrode diameters = 30 μm, pitch = 200 μm) were coated with 0.1 mg/ml poly-D-lysine (PDL) and 0.05 mg/ml laminin after aligning the PDMS microchannels over the electrodes. Two rows of electrodes were located in


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each reservoir while the rest of the electrodes were recording from microchannels (4 electrodes in each microchannel; Fig .1). After adding 1.5 ml cell culture medium (Neurobasal (NBM), B27 2%, Glutamax 1%, penicillin/streptomycin 1%) to each MEA and overnight incubation (5% CO2, 37 oC, 95% RH), the medium was drained and rat cortical neurons were added through the supporting reservoirs (3 μl, 8000 cell/μl). A second neural network module was formed by adding new cells to the opposite compartment after 14 days in vitro (DIV) (Fig.3).


signals from axons inside the microchannels were recorded after 8 DIV, while no activity could be recorded from the first cortical module in the first reservoir. Only after 13 DIV, first signals could be recorded from the first cortical module. Signals from the second cortical module in the opposite reservoir could be recorded already after 6 DIV after plating (first module was 20 DIV). It seems that the presence of axons from the first module accelerates the differentiation and activity onset in the second module.

100 μV 100 ms

500 μV 100 ms

Fig. 3. Before and after seeding the cells in the second module. A) Axons from the first module had reached the second reservoir at 14 DIV before seeding the new cells. B) 3 DIV after adding new cells to the second reservoir, these started to get in contact with axons from neurons in the first reservoir (17 DIV). Electrode pitches are 200 µm.

3 Results Adding cells through the open reservoirs automatically let them enter into the small reservoir and settle there. The small reservoirs produced almost closed chambers for small network formation on top of the electrodes. Cells were distributed homogenously in the small reservoirs with a maximum number of 2240 cells per module (reservoir). Neurites grew into the microchannels after 3 DIV and reached the opposite reservoir after 10 DIV. Freshly prepared cortical neurons were added after 14 DIV into the counterpart reservoir (Fig.3). Because of its thinness, the device was sufficiently transparent to track axonal growth and network formation by bright field or phase contrast up-right or inverted microscopy (Fig.4).

Fig. 5. An example of activity between two cortical modules and one of their connecting channels. Electrode 28 records from module 1, electrode 78 records from module 2 and electrodes 38, 48, 58 and 68 record from connecting axons. Recording window = 500 ms. Signal amplitudes on electrodes 28 and 78 are ≃ 250 µV. Maximum signal amplitudes on electrodes 38, 48, 58 and 68 are ≃ 1500 µV. Recording and microscopy pictures were prepared from the culture at 25 DIV (first module) and 9 DIV (second module).

Acknowledgement We thank the Fondazione Istituto Italiano di Tecnologia for intramural funds in support of all parts of this study. References [1]

Shein Idelson M, Ben-Jacob E, Hanein Y. (2010): Innate synchronous oscillations in freely-organized small neuronal circuits. PLoS One 5(12):e14443 [2] Pan L, Alagapan S, Franca E, Brewer GJ, Wheeler BC. (2011): Propagation of action potential activity in a predefined microtunnel neural network. J Neural Eng. 8(4):046031.

Fig. 4. Growing axons inside the microchannel at 10 DIV. Electrode pitch: 200 µm.

In spite of the small network sizes (≃ 2500 cells per reservoir), normal activity and amplified signals could be recorded from reservoirs and microchannels (250 µV and 1.2 mV, respectively; Fig.5). The first


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A Microfluidic Device for Selective Positioning of Invertebrate Neurons Katharina Krumpholz1*/**, Akram El Hasni2*, Andreas Neuhof1, Rudolf Degen1, Uwe Schnakenberg2, Peter Bräunig1, Katrin Bui-Göbbels1 1 RWTH Aachen University, Institute of Biology II, 52074 Aachen (Germany) 2 RWTH Aachen University, Institute of Materials in Electrical Engineering (IWE 1), 52074 Aachen (Germany) * Authors contributed equally ** Corresponding author. E-mail address: [email protected]

Abstract We present microfluidic devices for selective positioning of locust and pond snail neurons. Fluid flows containing the neurons were generated by a syringe pump. The influence of different channel geometries as well as differences in flow rates were investigated to achieve appropriate conditions for handling the neurons gently and achieve efficiency in positioning.

1 Background /Aims We are interested in the construction and investigation of small defined neuronal networks to gain detailed insight in network activity. Our aim is the control of neuronal activity inside the network and the unambiguous assignment of signals to individual cells. This becomes possible if every cell of the network grows on just one electrode of a MEA surface. In this context the use of invertebrate neurons provides the advantage that - in contrast to vertebrate cells - they can be cultured at extremely low cell density (Fig.1). Furthermore many neurons of invertebrates are very large. So they cover individual electrodes completely resulting in a better signal to noise ratio. The construction of such networks requires the positioning of neurons on the electrode surfaces, but positioning of cells onto single electrodes by manual handling without stressing them is still a problem. In our project directed positioning should be achieved by a closed hydrodynamically working microfluidic device. Our far goal is the development of a cost-efficient, automatic biohybrid system. A combination of microfluidic device and MEA shall enable the selective positioning of individual neurons on the electrodes and the long term electrophysiological characterisation of single neurons as well as their interactions in the network.

2 Methods 2.1 Animal dissection Insect (Locusta migratoria) and molluscan (Lymnaea stagnalis) neurons were extracted from thoracic ganglia and from the central ganglionic ring respectively, and affiliated in particular media (Locusta: modified Leibovitz L-15 cell culture medium [1]; Lymnaea: modified L-15 defined medium (DM) [2].

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Fig. 1. Cultured neurons at low density sprouting numerous neurites. A: Lymnaea neurons. Scale bar: 50µm B: Locusta neuron. Scale bar: 20µm

2.2 Microfluidic devices The microfluidic devices were fabricated in polydimethylsiloxane (PDMS) on glass using standard soft lithography and O2 plasma bonding techniques. Design variations of the cell trap device are shown schematically in Figure 2A/B. A fluid stream of defined velocity generated by a syringe pump, containing the pre-processed neurons, was guided into the device. The system is based on the principle of differential fluidic resistance. The fluid tends to flow along the path of least fluidic resistance. Here the meandering main channel has a higher fluidic resistance than the small transverse channels, which causes a higher flow rate through the transverse channels. This principle leads to a guiding of the neurons into the depressions upstream the transverse channels (Fig. 2A), where they can adhere. The cell trap design shown in Figure 2A and 3A has the advantage that the integration of a MEA can be achieved most easily. Other design channel geometries and trap arrangements are improvements with respect to optimized flow proportions between main and transverse channel to increase efficiency of cell trapping. An example is shown in Fig. 2B and 3B.


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Fig. 2. Schematics of microfluidic cell traps. A/B: Meandering microchannel systems (dark blue) with transverse channels (light blue) for trapping neurons.

Fig. 4.A: Captured Polystyrene beads. Scale bar: 50µm B: Captured Locusta neurons. Scale bar: 50µm C: Cultured Locusta neuron in a microfluidic channel with numerous neurites. Scale bar: 25µm

4 Conclusion/Summary Fig. 3. Photographs of microfluidic cell traps. A: Device with 16 semi-circular trapping depressions and funnel shaped transverse channels. The white arrows indicate the direction of the fluid stream. B: Cut-out of device with four square trapping depressions in every other main channel section. Scale bars: 50µm

3 Results The functionality of the cell traps with respect to different parameter variations like channel heights and widths as well as different flow rates was tested. Therefor we used polystyrene beads (Fig. 4A), Locusta and Lymnaea neurons. It was possible to position invertebrate neurons in the depressions of the microfluidic cell trap devices (Fig. 4B). It turned out that very slow flow rates for the neurons (1 µl/min) are necessary otherwise the cells would simply become squeezed through the transverse channels because they deform quite readily. Channel widths wider than 75µm do not lead to an adequate deflection of the neurons when passing the depressions. Also the height of the transverse channel as well as the shape of the depression are important for trapping success. The transverse channels should be of the same height as the main channel otherwise the flow rate through the transverse channels would not be strong enough to deflect the cells. In addition, deeper trapping depressions prevent the neurons from coming adrift. Preliminary results show that we are able to culture neurons in the closed microfluidic devices over a period of several days where they build numerous neurites (Fig.4C).

Our tests show that the cell trap is suitable in capturing individual cells. We observed that the successful use of the devices predominantly depends on the channel geometry as well as on the fluid velocity. In general it turned out that beads tolerate parameter variations in channel sizes and flow rates much better than neurons. Our results indicate that neurons react sensitively to the shear stress they are subjected to. We observed that the neurons were deformed in the trapping depressions by the pressure of the stream. This effect was not described previously with cells of different cell lines [3]. So far the microfluidic devices represent a promising approach to handle the problem of positioning neurons selectively on electrodes on a MEA surface. References [1]

[2] [3]


Ridgway, R. L. (1991): Nerve Growth Factor (NGF) Induces Sprouting of Specific Neurons of the Snail, Lymnaea stagnalis. J. Neurobiol., 22, 377-390. Göbbels, K. (2010): Neuronal cell growth on iridium oxide. Biomaterials, 25, 1055-1067 Frimat, J.-P. (2011): A microfluidic array with cellular valving for single cell co-culture. Lab on a Chip, 11, 231-237

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Amplifier ASIC for Real 3D-Multichannel-MEA Thomas Just1*, Peter Husar1 1 Technische Universität Ilmenau, Germany * Corresponding author. E-mail address: [email protected]

Abstract In this paper we will show the first results of a multichannel amplifier for 3D-MEAs with 800 capacitive sensors. One chip has included two amplification stages and sample-and-hold gate which will acquire the value of every sensor at the same time. 10 ASICs will be parallelized to read out in summary the 800 capacitive sensors of the 3D-MEA. The first simulation show a generated action potential of a spiking cell and the integrated signal at the output for further offline analyses.

1 Aims

2.2 Concept of the ASIC

For standard measurements of neuronal cell activity commercial 2D-MEAs are used. To get information in the third dimension it is necessary cultivate cells in additional layers. A 2D-MEA records only the activity on the bottom layer. To get signals from cells of the higher layers we will build towers with sensors. Because of the signal properties and the high number of sensors an ASIC will amplify, filter and multiplex the signals.

Every chip can synchronize itself with each other. The ASIC can be configured by the SPI interface, however, it can work autonomously by default settings. Fig. 3 shows the equivalent network of neuron, sensor and amplifier. The cell produces potentials of about 100 mV across the membrane, the extracellular potential amounts only up to -80 μV – 40 μV [2]. The most signal power lies in the frequency range from 100 Hz to 10 kHz [3]. The activity of the cells is captured through the sensors capacity into the chip. 80 input signals are pre-amplified and filtered with low noise amplifiers. In the next step the signals are sampled in 160 sample-and-hold units which realize the snapshot function (reading from 80 sensors at one time and saving 80 samples from last timestamp). Several internal MUXs provide the signals to 4 output channels. The chip has a maximal amplification of 1000.

2 Methods 2.1 Concept of 3D-MEA Standard MEAs are having sensor arrays on a two dimensional plate. To crow up into the third dimension sensors have to be placed on micro towers (Fig. 1). Every tower has 8 sensors, 10 towers per shape and one 3D-MEA will have 10 shapes (max. 800 sensors). Due to the small signal amplitude [1] the amplifier should be as near as possible at the sensor. The chip is designed to work as signal amplifier and stimulator simultaneously at 80 bidirectional channels on one shape.

3 Results Fig. 4 shows the simulated signal at the sensor (b) which was evoked by the intracellular signal (a). At the output we will get an inverted amplified signal for further analysis.

Fig. 1. Tower design; 8 sensors per finger, 10 fingers per shape, 10 shape build one 3D-MEA (in summary 800 sensors). Fig. 2. ASIC-Layout. a) first amplification stage, b) output

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4 Summary +

Fig. 3. Equivalent circuit diagram. Extracellular (blue), capacitive sensor (red), amplifier (green) a)

To get more information about 3D cell structures a novel kind of 3D-MEAs was developed. Additionally an ASIC was produced which will amplify measured cell activity as near as possible to the sensor. In this paper the concept of an amplifier ASIC for 3DMEAs and first tests of the simulation results was shown. Acknowledgement This research has been supported by the 3DNeuroN project in the European Union's Seventh Framework Programme, Future and Emerging Technologies, grant agreement n°296590. We will also thank the Institut für Mikroelektronik- und Mechatronik-Systeme (IMMS, Erfurt) for producing the ASIC.

U in mV

References [1] t in ms

[2] [3]

b)

Silbernagl, S.; Despopoulos, A.: Taschenatlas Physiologie. Thieme Verlag. Carter M. (2010): Guide to research techniques in neuroscience. Academic Press, 1-376. Just T. et al (2013): Spike Sorting Algorithm for Multichannel MEA Recordings Based on Cross Correlation. Biomedizinische Technik/Biomedical Engineering (Biomed Eng )

U in µV

t in ms

c)

U in mV

t in ms

Fig. 4. Simulations; a) intracellular, b) extracellular, c) output of the ASIC


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Simultaneous Electrical Investigation of Isolated Neurites Using a Neurite-Isolation Device as Neurite Regeneration Model Johann K. Mika1, Karin Schwarz2, Heinz Wanzenböck1*, Petra Scholze2, Emmerich Bertagnolli1 1 Institute of Solid State Electronics, Vienna University of Technology, Vienna, Austria 2 Center for Brain Research, Medical University of Vienna, Vienna, Austria * Corresponding author. E-mail address: [email protected]

Abstract The understanding of how neurites are guided to each other plays an important role in neurobiology inter alia in regrowth and repair of disconnected nerve cells. In the last decades several neurotrophic factors were identified to promote neurite out-growth and guidance to target links. While the effects of neurotrophins on the molecular properties of neurites are quite well examined, the impact on the electrical functioning was nearly neglected due to the limited possibility for simultaneous recordings with patch clamp technique. Microelectrode Arrays offer a method to overcome this limitation but do not guarantee for a small electrode to neurite distance. In order to investigate the influence of neurotrophins on the electrical activity of growing neurites we developed a device that separates the neurites from the whole culture and provide microelectrodes near them. Our approach consists of a microfluidic device aligned on top of a multielectrode array. This setup enables the simultaneous recording of neuronal activity of growing neurites through microchannels and optical investigation of the whole growing cell population at the same time. The proof-of-concept was demonstrated with the growth of sympathetic neurons from the superior cervical ganglion of P5 WT mice responding to different levels of nerve growth factor (NGF), one of the most well-studied neurotrophic factors. Due to the microchannels which act as guidance tubes for neurites the device is capable to act as neurite regeneration model. The approach to combine the guidance tubes with a multielectrode array further helps to verify the effect of neurite growth promoters on metabolical and electrical characteristics. It also enables a universal applicable device for future studies where neurites and somata need to be treated independently of each other.

1 Introduction The guidance and reconnection of disconnected nerve cells of the central and peripheral nervous system to a target link is currently a strong research topic in neurobiology. In the last decades, several neurotrophins were identified as proteins which induce the survival, development, and function of neurons [1]. In detail, they also promote neurite outgrowth and guidance to target cells [2]. While the effects of neurotrophins on the growth rate and molecular properties were quite well examined in the past, the impact on the electrical functioning of a whole network of neurons was nearly neglected due to the limitation of patch clamp technology. The development of multielectrode arrays by Thomas et al. 1972 [1] and finally the rise of this technology enabled neurobiologists to record nerve signals from a whole culture simultaneously.

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Fig. 1. Schematic drawing of the 30-2-Neurite Isolation-MEA. The NI-part consists of 4 supply reservoirs and a sink and source macrochannel (left, blue). The cells will be seeded either in the source or in both macrochannels. The 30 microchannels (blue) allow only neurites to enter whereas 2 electrodes per channel enable electrical activity recordings of the neurite.

While MEAs broke through the limitation of few electrodes, they did not guarantee a small, defined contact zone between electrode and neurite. To overcome this issue and to investigate electrical signals of multiple neurites our approach consists of a microfluidic neurite-isolation (NI-) device aligned on top of a multielectrode array. Figure 1 shows a schematic view of our device. This setup enables the simultaneous recording of neuronal activity of growing neurites through microchannels and optical investigation of the whole growing cell population at the same time. We


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demonstrated the proof-of-concept with neurons from the superior cervical ganglion of P5 WT mice responding to different levels of nerve growth factor (NGF).


ingrowth of neurites into microchannels and electrical activity was determined with 93.6%.

Fig. 2. Neurite-Isolation MEA with a closer look on the inner microelectrode-microchannel area.

2 Material and Methods The NI-MEA device is composed of two components, the neurite-isolation device (NI-device, upper part) and a custom designed multielectrode array (MEA, lower part). The NI-device consists of two culture chambers (source and sink) with reservoirs at their ends as cell inlets and media supply. Source and sink chambers are connected together by 30 microchannels (diameter < 10 µm) which act as physical barriers for neuronal somata allowing only neurites to enter. The MEA with its electrodes is precisely aligned within the microchannels so that each microchannel is equipped with two microelectrodes (electrode material either gold or ITO with TiN coating). Figure 2 shows a produced NIMEA and a closer look on the microchannel/microelectrode area. Cells of the superior cervical ganglion of P5 WT mice were loaded into the source chamber via the cylindrical reservoirs. The cells were grown in NIMEAs with different nerve growth factor concentrations for 14 days. Electrical measurements on days 4, 7 and 14 using a BC1060 recording setup (MultiChannelSystems, Reutlingen, Germany) were taken. The data was then analyzed using a developed MATLAB program using a combination of threshold analysis and principal component analysis [4]. After 14 days the cells were fixed and stained using different antibodies to acquire parameters like cell distribution, neurite growth and type. The data was also compared with the images captured regularly after each electrical measurement.

3 Results The optical and electrical results were correlated and the overall results analyzed. Electrical activity maps showed different patterns in accordance to chemical stimulation of the growing cells. The optical transparency of the NI-MEA allowed tracing cultures by optical light microscopy as well as fluorescence microscopy using immunocytochemistry (Figure 3). As proof-of-concept and also to show the sensitivity of the device different nerve growth factor gradients in the macrochannels were analyzed (Figure 4). A degree of consistence between the

Fig. 3. Optical light microscopy of the same area (DIV 7 and 10).

Fig. 4. Different concentrations of neurite growth factor result in different recorded electrical activity.

4 Conclusion and Discussion We designed a new microfluidic-microelectrode device that combines a neurite isolation device with a MEA. This platform for (re-)generating neurites enables both microscopic observations of and electrophysiological recordings from neurites. The proof of concept of the device was shown by recording spontaneous as well as nicotine-induced electrical activity of mouse sympathetic neurons for two weeks with two different concentrations of NGF. References [1]

[2]

[3]

[4]


Bradbury EJ & McMahon SB. (2006). Spinal cord repair strategies: why do they work? Nature reviews Neuroscience 7, 644-653. Sofroniew M.V., Howe C.L. & Mobley W.C. (2001). Nerve growth factor signaling, neuroprotection, and neural repair. Annu Rev Neurosci 24, 1217-1281. Thomas C.A., Springer P.A., Loeb G.W., Berwald-Netter Y., Okun L.M. (1972) A miniature microelectrode array to monitor the bioelectric activity of cultured cells. Exp Cell Res, 74: 61–66 Schuller P (2014) Investigation of Neuronal Activity recorded with Multi-Electrode Arrays using Principal Component Analysis. 9th International. Meeting on Substrate-Integrated Microelectrode Arrays, Reutlingen, Germany.

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Ultra-Low Cost Arrays of Printed Carbon Electrodes for Extracellular Recordings Jan Schnitker1, Alexey Yakushenko1, Francesca Santoro1, Fabian Brings1, Bernhard Wolfrum1, Andreas Offenhäusser1 1 ICS-8/PGI-8 Institute of Bioelectronics, Forschungszentrum Jülich, Germany

* Corresponding author. Email address: [email protected]

Abstract Multielectrode arrays are a commonly used tool in neuroscience for the investigation of cellular networks and their communications processes by recording extracellular signals. In particular, advanced MEAs featuring 3D electrode can achieve very high signal noise ratios and a high durability for extracellular recordings. The fabrication of MEAs requires typically a cleanroom environment and uses expensive materials for the fabrication process. We investigated a new approach based on printed carbon. We fabricated by hand printed carbon electrode MEAs in very few and steps and used extremely cost-efficient materials for the fabrication. These printed carbon chips were successfully used for extracellular recordings from cardiomyocyte-like cells showing a high signal-noise ratio.

1 Background Modern chip-based electrophysiological investigations can rely on a variety of different chip types ranging from planar metal microelectrodes to 3D nanoelectrodes [1]. These biosensor devices can feature excellent cell-chip coupling for recording and stimulation of electrogenic cells. Moreover, they can reach subcellular spatial resolution. They require elaborated fabrication methods such as cleanroom facilities and, therefore, are expensive and potentially labor intense in fabrication. It is not only for high-throughput oriented applications such as drug-screening very desirable to have ultra-low cost devices which are even disposable. We demonstrated the successful use of individual printed carbon electrodes for neurotransmitter release measurements [2]. We extended this ultralow cost fabrication approach to carbon-based multi electrodes arrays (MEAs) and applied them for extracellular recordings from electrogenic cells. Arrays of such printed carbon electrodes can be manufactured in very few simple steps.

Fig. 1. SEM micrographs of two printed carbon inks with (A) commercial ink with high roughness, (B) selfsynthesized ink with smooth surface (scale bar 5 µm).

A fabricated 6 channels MEA with different sized electrode the stripes on printed carbon is shown in Fig. 2. We fabricated printed carbon electrodes on three different substrates: glass, polyimide (Kapton ™, Dupont) and polyethylene terephthalate (PET, Dupont).

2 Methods We applied different kinds of carbon pastes, commercially available as well as self-synthetized inks on flexible polymer substrates and rigid glass surfaces. Then, we covered them with Polydimethylsiloxane (PDMS, Sylgard 184, Dow Corning). The electrode apertures were manually pinched with a needle. The fabrication process was completed with the connection of the electrode feedlines to a carrier adapting to the electronic socket of the amplifier system for later readout. The surface of a commercial and self-synthesized ink is shown in Fig 1 showing large differences in roughness.

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Fig. 2. (A)Stripes of inks printed on flexible polyimide subsrtate, (B) six stripes of printed carbon with 6 different sized electrodes on a encapsulated MEA (scale bar 1 mm).

3 Results We cultured cardiomyocyte-like cells (HL-1) on the chips and measured the electrical activity after 3 DIV with a self-developed low-noise amplifier system. An example trace of an extracellular voltage recording is shown in Fig. 3. The extracellular recording exhibits a low


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background noise-level and a high signal-to-noise ratio, comparable to the recording quality of e.g. Au or Pt-MEAs produced by means of standard cleanroom fabrication (data not shown).

Fig. 3. Extracellular recording of action potentials with printed carbon electrodes from HL-1 cells.


4 Conclusion We demonstrated a novel fabrication method for printed carbon electrode MEAs which can be used for extracellular voltage recordings. The printed carbon electrodes have shown a similar performance in terms signal-noise ratio compared to typical recordings metal-based multi electrode arrays in the regime of few hundred microvolts. The manufacturing process of the disposable printed carbon chips employs very cheap commercial or homemade carbon pastes for the electrodes and PDMS as passivation material. This keeps the overall fabrication costs extremely low. Printed carbon electrodes can be applied for a wide range of applications in chip-based life-sciences ranging from amperometric measurements to extracellular voltage recordings of action potentials. References [1]

[2]


Spira ME, Hai A. Multi-electrode array technologies for neuroscience and cardiology. Nat. Nanotechnol. 2013 Feb;8(2):83–94. Yakushenko A., Schnitker J., B. Wolfrum. Printed Carbon Microelectrodes for Electrochemical Detection of Single Vesicle Release from PC12 Cells. Analytical Chemistry. 2012 May 15;84 (10):4613–4617.

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Stimulation Artifact Suppression Techniques for High-Density Microelectrode Arrays A.Shadmani1, J. Müller1, P. Livi1, M. Ballini1, Y Chen1, A. Hierlemann1 1 ETH Zürich, Department of Biosystems Science and Engineering, Basel, Switzerland

Abstract Stimulation protocols used with Microelectrode arrays (MEAs) cause residual charges and artifacts on the stimulation electrodes or on the immediately neighboring electrodes. Here, we report on two techniques that help to suppress the generated artifacts on the neighboring electrodes of the stimulation site. The first technique is to disconnect the electrodes from the recording channels during stimulation. The second technique includes to measure and then store the respective resting potential values of the electrodes and to then restore those potentials on the electrodes after stimulation.

1 Background/Aims The possibility to stimulate individual neurons is an essential requirement to answer many questions in neuroscience. Electrical stimulation is required for triggering action potentials and analyzing neuronal network dynamics or investigating neuronal plasticity. It is important to have the possibility to investigate the neuronal activity immediately after stimulation pulses in close vicinity to the stimulation sites. Therefore, efforts have been undertaken, e.g. [1], to enable recording from the same electrode that has been used for stimulation. These efforts mainly focus on removing of the residual stimulation signal, i.e., stimulation artifact on the stimulation electrode to have it ready for immediate recording after stimulation. While this is an interesting solution for low-density MEAs, highdensity MEAs allow for achieving high-resolution recordings without the need to acquire the signal on the stimulation electrode by simply using the recordings from the closely spaced neighboring electrodes. Since the gain of recording circuits in the high density MEA is high, and the distance between the electrodes is small, a stimulation signal will affect the recording circuits of the surrounding electrodes and potentially prohibits recording for a few seconds before those channel circuits get back to their operating state.

2 Methods/Statistics One solution to suppress the artifacts is to disconnect the neighboring electrodes from the recording circuits during the stimulation phase and then immediately reconnect them once the stimulation has been performed. The second solution is to measure and store the resting potential of the neighboring electrodes before stimulation, and then keep the electrodes at this stored potential during stimulation. Immediately after stimulation the potential application is stopped, and

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the electrodes will be available to record neural activities.

3 Results Fig. 1a, shows a set of electrodes on the highdensity MEA, configured for stimulation (red) and recording (blue) with a gain of 512. Fig. 1b shows the stimulation signal and the resulting artifact on the neighboring recording channel. It takes about 2 seconds to get back to the operating state. Fig. 2 shows the schematic and measurement results of the circuit used for the disconnection technique. In this case, the recording channel is ready for recording around 1 ms after stimulation. Fig. 3 shows the schematic and measurement results of a test circuit fabricated in 0.18 µm CMOS technology, which can apply the initially measured potential of the recording electrode during the stimulation phase. This circuit is connected to a CMOS-based high-density microelectrode array (HDMEA), featuring 26,400 electrodes at 17.5 µm pitch, 1024 recording channels, and 32 stimulation units for bidirectional communication with electrogenic cells [2,3]. In this case the recording channel will be ready to record in less than 1 ms after the stimulation pulse.

4 Conclusion/Summary Two techniques were presented for suppressing the stimulation artifacts on neighboring electrodes of the stimulation site. Both techniques show a significant improvement for suppressing the artifacts. The recording channel disconnection technique does not require any additional circuitry. Acknowledgement This work was supported by the Advanced ERC Grant “NeuroCMOS” under contract number AdG 267351. Amir Shadmani received individual support through the Marie Curie Research Training Network “EngCaBra” (grant agreement no. 264417)


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10 µm (a)

(b)

(c)

Fig. 1.. (a) Pt-microelectrodes, 17.5 µm pitch (red: stimulation electrode, blue: recording electrode), (b) Stimulation and recording voltages without artifact suppression (recording amplification is 512 times), (c) Zoom-in around stimulation pulse

(a) (b) (c) Fig. 2. (a) Circuit schematic for stimulation artifact suppression by using the recording channel disconnection technique. (b) Improved stimulation artifact by using this technique. (c) Zoom-in around stimulation pulse

Chold (a) (b) (c) Fig. 3. (a) Simplified circuit schematic for stimulation artifact suppression using the technique of first storing the electrode potential to Chold, then maintaining this potential during the stimulation phase, (b) Improved stimulation artifact by using this technique, (c) Zoom-in around the stimulation pulse

References [1] [2] [3]

E. A. Brown et al., Stimulation Artifact Elimination in a Multi-Electrode System, IEEE Transactions on Biomedical Circuits and Systems, Vol. 2, No. 1, March 2008 M. Ballini, A 1024-Channel CMOS Microelectrode-Array System with 26’400 Electrodes for In-vitro Recording and Stimulation of Electro-active Cells In-vitro, VLSI Symposium 2013, Kobe Japan, 2013. J. Müller et al., Conferring Flexibility and Reconfigurability to a 26'400 Microelectrode CMOS Array for High Throughput Neural Recordings", in Proc. Transducers ’13, Barcelona, Spain, pp. 744-747, 2013.


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Large-Area, High-Density Arrays of Micro-Needles for Acute Slice Electrophysiology D.E. Gunning1*, P. Hottowy2*, W. Dabrowski2, A. Sher3, J.M. Beggs4, C.J. Kenney5, D.A. Young1, A.M. Litke3 and K. Mathieson1 1 Institute of Photonics, SUPA, University of Strathclyde, Glasgow, UK 2 AGH University of Science and Technology, Krakow, Poland 3 SCIPP, University of California Santa Cruz, Santa Cruz, California, USA 4 Biocomplexity Institute, Indiana University, Bloomington, Indiana, USA 5 SLAC National Accelerator Laboratory, Menlo Park, California, USA * Corresponding author. E-mail address: [email protected]

Abstract This paper introduces a highly dense, large-area array of micro-needles developed specifically for in vitro, acute slice electrophysiology. The array incorporates 512 needles with 60 m inter-needle spacing. Each needle measures up to 250 m in height and at the tip has a small (5-10 m diameter) conducting platinum electrode. The electrode impedance is measured to be ~300 k at 1 kHz and the system noise is ~7 V allowing action potentials to be recorded from individual neurons. The needles can penetrate in to neural tissue and with the potential to record from hundreds of neurons simultaneously, this technology will be used in high-resolution connectivity studies of local networks of neurons in acute slices.

1 Introduction

2 Method

For many years, the use of planar microelectrode arrays has been a widely adopted electrophysiological approach to studying connected neural activity. Whilst these types of arrays work very well to record the simultaneous activity of hundreds of neurons in both retina and culture [1,2,3], it is widely accepted that, as a consequence of the planar nature of the electrodes, their usefulness in vitro is limited to studying neurons on the surface of a tissue preparation. For the study of acute slices, where the surface neurons and their connections can be damaged by the slicing procedure, arrays of 3-d microelectrodes address this limitation by penetrating in to the slice to bypass the damaged surface layer and record from the intact interior volume of the slice [4,5,6,7]. Here, the fabrication and electrical characterization of a large-area, high-density array of microneedles is presented. The array is fabricated in silicon and incorporates 512 needles with a needle density of 256 needles/mm2. The needles can measure up to 250 m in height and at the tip of each is a small, 5-10 m diameter platinum electrode. The device has been electrically characterized to show that the platinum electrode impedance at 1 kHz is ~300 k and the complete readout system operates with an RMS noise of ~7 V. The array covers an area of 1 x 2 mm2 allowing the connectivity of neurons between cortical layers and multiple cortical columns in the mouse brain to be recorded simultaneously. The small electrode size combined with low impedance enables single-cell resolution recordings with an excellent signal to noise ratio (~15:1).

Each micro-needle array is a 32 x 32 mm2 chip, four of which are fabricated on to a 100 mm diameter silicon wafer using a novel sequence of semiconductor fabrication techniques. The process relies on the high-aspect ratio etch capability of the Bosch process and the conformality of a tungsten film deposited by low pressure CVD. In Figure 1, the major fabrication steps of a single needle are illustrated.

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Fig. 1. A schematic of the main steps in the micro-needle array fabrication process. The fabrication of a single micro-needle is illustrated. The needles (etched hole, silicon dioxide passivation, metallization and polysilicon filler) are ‘embedded’ in to the silicon (ivi) and in the final process steps, the silicon is removed to expose the array of needles and the silicon dioxide insulation is removed from the tips of the needles to expose a tungsten electrode (vii-viii) which is subsequently electroplated with platinum black.

The fabrication process for the 512-channel micro-needle array has been adapted from that described in [7] for the fabrication of a 61-channel micro-needle array. The most significant modifications include using an SF6/Ar etch chemistry for the high-resolution


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(2 m minimum feature size) tungsten readout pattern (to improve the etch selectivity to photoresist). Also the deep silicon etch (step viii of Figure 1) has been optimised to use a combination of the Bosch process and wet chemical (TMAH) etching to expose the micro-needles. This results in a faster and more anisotropic silicon etch. The completed chip is wire-bonded in to a 512-channel system of low-noise pre- and postamplification readout circuitry [8].

3 Results An array of 512 micro-needles was successfully fabricated and electrically characterized. A 100% of needles were shown to be mechanically viable (no broken needles) across an array. The platinum electrode impedance was measured to be ~300 k at 1 kHz, a suitable value to allow recordings of neuronal action potentials (duration ~1 ms).


4 Conclusions A 512-channel micro-needle array with a needle density of 256 needles/mm2 was successfully fabricated to have dimensions suitable for a large-area, highresolution study of connectivity in local neural networks. The small, low-impedance platinum recordings enable recordings of action potentials from individual neurons. The designed fabrication process is robust and versatile, lending itself to future arrays with longer needles, multiple needle lengths across a single array, higher densities and larger area coverage. Ultimately, this innovative new technology could allow neuroscientists to look, with unprecedented detail, at the connected electrical behaviour of neural circuits in vitro. Acknowledgement The work was funded by RAEng fellowship EP/H044639/1 (DEG), NSF grants IIS-0904413, PHY-0750525 (AML) and IIS-0904912 (JMB), the Pew Charitable Trust’s Scholarship in the Biomedical Sciences (AS) and Polish Ministry of Science and Higher Education grants (WD, PH). The device fabrication was done at the SNF, Stanford University, the JWNC, University of Glasgow and microfabrication labs at the Queen’s University, Belfast and the University of Strathclyde.

References [1] [2]

[3] Fig. 2. (top) Photo of front- and back-side of a 512-channel microneedle array showing tungsten readout wires leading to aluminium bond pads. (bottom left) SEM image of ~75 m tall 60 m spaced micro-needles. (bottom right) SEM image of ~75 m tall 30 m spaced micro-needles.

The readout system operates with ~7 V RMS noise, making biological recordings with an excellent signal to noise ratio (15:1) possible. Images of the front- and back-side of an individual chip and SEMs of the micro-needles spaced at 60 and 30 m (i.e. respective densities of 256 and 512 needles/mm2) are shown in Figure 2.

[4]

[5]

[6]

[7]

[8]


Field G.D. et al (2010). Functional connectivity in the retina at the resolution of photoreceptors. Nature, 467, 673-677. Jimbo Y. and Kawana A. (1992). Electrical stimulation and recording from cultured neurons using a planar electrode array. Bioelectrochem. Bioenergy, 29, 193-204. Tang A. et al (2008). A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro. J. Neurosci., 28, 505. Nordhausen C.T., Rousche P.J. and Normann R.A. (1994). Optimising recording capabilities of the Utah intracortical electrode array. Exp. Brain Res., 637, 27-36. Charvet G. et al (2010). BioMEA: A versatile high-density 3D microelectrode array system using integrated electronics. Biosens. Bioelectron., 25, 1889-1896. Heuschkel M.O. et al (2002). A three-dimension multielectrode arrays for multi-site stimulation and recording in acute brain slices. J. Neurosci. Methods, 114, 135-148. Gunning et al (2013). Dense arrays of micro-needles for recording and electrical stimulation of neural acute brain slices. J. Neural Eng., 10, 016007 (11pp) Litke A.M. et al (2004) What does the eye tell the brain?: Development of a system for the large-scale recording of retinal output activity. IEEE Trans. On Nucl. Science, 51(4), 14341440.

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A Hybrid Multi Optrode Electrode Array for Optogenetics Luis Hoffman1, Alexandru Andrei1, Robert Puers1,2, George Gielen1,2, Dries Braeken1* 1 Imec, Leuven, Belgium 2 ESAT, KULeuven, Leuven, Belgium * Corresponding author. E-mail address: [email protected]

Abstract Today, optogenetic tools play an important role in the unraveling of neuronal circuits in the brain. Here, we present a novel hybrid multi optrode electrode array (MOEA) for optogenetic applications in vitro. The MOEA comprises of an arrangement of 64 titanium nitride electrodes and 64 optical outputs distributed over two different wavelengths (470 nm and 590 nm); both functionalities are integrated monolithically into a silicon substrate using CMOS compatible technology. This enables the device to optically stimulate a single cell and simultaenously record its electrical activity.

1 Background Optogenetics has emerged as an invaluable tool to study brain connectivity by optically controlling welldefined events in living cells. To ensure continuous progress in this field, new tools with improved capabilities should be developed. Such devices are ideally cheaper, offer better reproducibility and feature multiple independent optical outputs. Additionally, full integration of optical stimulation of different wavelengths combined with electrical recording would increase the degrees of freedom of experimental design in optogenetics.

fabrication of neural probes and the other for the creation of optical interconnects in telecommunication systems. All this ensures that the resulting devices are highly reproducible, reliable and scalable.

2 The proposed device MOEA The presented in vitro device contains 64 TiN electrodes and 64 optical outputs, located closely together to allow for optical stimulation and recording of the same cell. The electrodes are 30 µm in diameter and the optical outputs are approximately 6 by 20 µm in size, which resembles the area of a single cell (see Fehler! Verweisquelle konnte nicht gefunden werden.). Light is carried from external sources (light emitting diodes or lasers) into silicon nitride waveguides by optical input gratings. Two different grating designs were incorporated; one is capable of coupling light of 470 nm into one set of waveguides and the other of 590 nm in another set of waveguides. At the electrode/optrode site, the light is extracted from the waveguides perpendicular to the electrode plane by optical output couplers (see Fig 2). Fabrication The device is fabricated in imec’s clean room facilities. It is manufactured on silicon wafers using a CMOS compatible process and combines two well-established techniques: one used for the 330

Fig. 1. (a) Complete view of the MOEA. (b) Detail section where the different components are highlighted


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4 Conclusion This novel hybrid multi optrode electrode array will stimulate progress in optogenetic applications by allowing for multi-wavelength stimulation and subsequent recording of the same cell. Acknowledgements Funding was provided by the IWT (grant 110068). The authors would like to thank Olga Krylychkina, Katrien Mols and Marleen Welkenhuysen.

Fig. 2. Blue light (470 nm) out-coupled from a waveguide at a grating site.

3 Results The optical outputs were tested using light coupled in by a miniature laser diode with a wavelength of 470 nm. The optical power density (OPD) was measured using an off-the-shelf optical power meter. The smallest OPD measured on a single grating out-coupler, which is in direct contact with the cells, was 70 mW/mm²: more than sufficient to activate commonly used optogenetic ion channels (ranging from 1 – 20 mW/mm²).


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Image Processing Based Quantitative Analysis of MEA Cultured Neurons João Fernando Mari1,2, José Hiroki Saito2,3, Amanda Ferreira Neves5, Celina Monteiro da Cruz Lotufo4, João-Batista Destro-Filho4* Alberto Pasquarelli6 1 Universidade Federal de Viçosa – Campus Rio Paranaíba – Brazil 2 Department of Computer Science – Federal University of São Carlos – Brazil 3 Faculty of Campo Limpo Paulista – Brazil 4 Federal University of Uberlândia – Campus Santa Mônica – Brazil 5 Department of Structural and Functional Biology – University of Campinas – Brazil 6 University of Ulm – Germany * Corresponding author. E-mail address: [email protected]

Abstract In this work we propose image processing methods that enable quantitative studies on MEA cultured neurons by means of fluorescence microscopy. The neurons are segmented from the fluorescence channel images and the positioning of microelectrodes is obtained from the transmitted light channel images. The proposed method is applied to images from dissociated cultures of rat dorsal root ganglion (DRG) neurons. Our method efficiently identifies the neurons and microelectrodes in the images. The quantitative analysis results in information about the state of the culture, such as neuron’s morphology and spatial distribution. Microelectrodes positioning enables to describe the interface between neurons and microelectrodes and improve the analysis of the electrophysiological signals recorded by MEA.

1 Backround Microelectrode Arrays (MEAs) allow studying the distributed patterns of electrical activities in dissociated cultures of brain tissues, composed by neurons and glia [1]. Fluorescence microscopy imaging allows monitoring the cell morphology and even the cell culture topology, it still permits to investigate the relation between cell and electrode, critical to recording a good-quality signal [2]. The objective of this work is to perform quantitative analyses about DRG neurons cultured in MEA from confocal fluorescence microscopy images.

2 Methods Culture Preparation and Image Acquisition: The cell cultures were performed using dorsal root ganglia (DRG) taken from male Wistar rats. The rats were anesthetized with CO2 and sacrificed by decapitation in accordance with the IASP and approved by the local Ethic Committee of Animal Experiments (CEUA/UFU). Dissociated cells were then plated on 60-channel MEAs (30 μm diameter, 200 μm spacing; Multichannel Systems). Cells were incubated with DiBAC4(3), and then imaged using an inverted fluorescence laser scanning confocal microscopy (Zeiss LSM 510 META). Volumetric 3D images were acquired in two channels, a fluorescence channel and a transmitted light one. Images were acquired in 3D to enable other studies, such as [3]. This work only considers 2D analysis, and maximum and minimum intensity project

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tions are applied over fluorescence and transmitted light channels in order to build 2D images. Microelectrodes identification: We pre-process the projected image by a median filter to reduce the noise and by a botton-hat transform to suppress the background and to highlight the microelectrodes. The circular Hough transform [4] is then applied to automatically detect the microelectrodes. Neurons segmentation: The median filter is applied in order to reduce the noise and top-hat transform is applied to suppress the background and to highlight the neurons. After the pre-processing step, we tile the image into 8x8 small images, and apply Otsu thresholding algorithm to segment each gray level tile. The area opening operation is applied to eliminate the connected components and morphological reconstruction is applied to fill holes inside the objects. We apply the morphological closing in order to smooth the object borders. Marker based Watershed transform is then applied to separate under segmented cells. Inner distance transform and extended maxima transform are applied to obtain markers for each neuron [5]. The skeleton by influence zone (SKIZ) of the binary image is considered as a marker for the image background. The objects and background markers are imposed as regional minima over a topographic surface defined by the morphological gradient of the binary image. After watershed transform it is still possible to observe a large number of segmentation errors, mainly artifacts. We propose a simple classification step to


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remove most of these objects based on three shape features: area, roundness, and eccentricity. Quantitative Analysis: Remaining errors are interactively corrected and the segmented neurons, with the information about microelectrodes positioning, are employed to obtain quantitative measures, such as: number of cells; perimeter, area, diameter, roundness and eccentricity of the cells; distances between each pair of cells and distances between cells and microelectrodes. Neurons in the image are then classified according to its diameter as: small neurons (≤ 20); intermediate neurons (≤ 40 μm); and large neuron (> 40 μm). Neurons located inside the microelectrode recording area are classified in accordance with the distances between neurons and the microelectrodes as: potentially connected (≤ 30 μm); neighboring (≤ 60 μm); and distant from microelectrodes (< 100 μm). Other neurons are considered outside any microelectrode recording area.

3 Results The methods were applied over the top-right MEA quadrant of a confocal microscopy image. Fig. 1(a) shows the automatically identified microelectrodes, (b) the automatically segmented neurons. In this experiment 88% of the neurons had been correctly segmented by the method. Fig. 1(c) shows the neurons classified in accordance with the diameter and distance to the microelectrodes after remaining error interactive correction. Charts in Fig. 2 show diameter (a) and distance (b) based neuron classifications.


(a)

(b)

(c)

Fig. 1. (a) Automated Microelectrode Identification. (b) Automated Neuron Segmentation. (c) Neurons classified in accordance to the distance to the nearest microelectrode.

(a)

(b)

Fig. 2. Quantitative analysis results. (a) Diameter based neurons classification and (b) distance to the nearest microelectrode neurons classifications.

4 Conclusion Microelectrode identification and neurons segmentation methods show to be capable to identify most of the microelectrode and the neurons. The measurements enable to obtain important information about the neuronal culture that can be used for the study and interpretation of the electrophysiological signal recorded by MEA. The neuron measurements, such as area, diameter, and distribution, are related to the soma membrane, and give the microelectrode recording probability. The distances between neurons and microelectrodes provide a tool for studies regarding to the relationship between the neuron electrophysiological activity and the signal recorded by the MEA system [5].

References [1]

[2]

[3]

[4]

[5]

Acknowledgement We thank Genoa Univ. and Multi Channel Systems Inc., DE, for providing us with MEA devices, and Mariane Silva and Prof. M.E. Beletti (Histology Dept. – UFU) for technical support.


Potter, S.M. (2001). Distributed processing in cultured neuronal networks. In: Nicolelis, M.A.L. (Ed) Progress in Brain Research, 130, 49-62. Van Pelt, J., Wolters, P.S., Corner, M.A., Rutten, W.L., Ramakers, G.J. (2004). Long-term characterization of firing dynamics of spontaneous bursts in cultured neural networks. IEEE T. Bio-Med Eng., 51(11), 2051-2062. Mari, J.F., Neves A. F., Saito, J.H., Lotufo, C.M.C., DestroFilho, J.B., Oliveira, A.A.B. (2012). Three-dimensional quantitative analysis of the dorsal-root-ganglia neuron-electrode interface. Proc. MEA Meeting 2012, 43-44. Peng T, Balijepalli A, Gupta SK, LeBrun T (2007) Algorithms for On-Line Monitoring of Micro Spheres in an Optical Tweezers-Based Assembly Cell. J. Comput. Inf. Sci. Eng. 7:330– 338. Ross J (2008) Microstimulation and multicellular analysis: A neural interfacing system for spatiotemporal stimulation. Georgia Institute of Technology, USA.

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MEA technology: Culture techniques

Long-Term Cultivation and Recording from Organotypic Brain Slices on High-density Micro-electrode Arrays Wei Gong1*, David Jäckel1, Jan Müller1, Michele Fiscella1, Milos Radivojevic1, Douglas Bakkum1, Felix Franke1, Frederic Knoflach2, Beat Gähwiler3, Branka Roscic1, Thomas Russell1, Andreas Hierlemann1 1 Bio Engineering Laboratory, D-BSSE, ETH Zürich, Basel, Switzerland 2 F. Hoffmann-La Roche, Basel, Switzerland 3 University of Zürich, Brain Research Institute, Zürich, Switzerland * Corresponding author. E-mail address: [email protected]

Abstract Organotypic brain slices are a standard preparation in neuroscience research. Here we present a method to study hippocampal neuronal networks in vitro by cultivating the respective slices directly on top of high-density multielectrode array (HD-MEA) chips for more than 30 days. Directly cultivating brain slices on the HD-MEA chips provides close neuron-to-electrode contacts and the possibility to maintain a register of cell identities, with respect to the electrode locations and recorded activity, over weeks. Moreover, very thin cell layers can be obtained in later stages of the cultivation procedure. The high resolution of the HD-MEA chips, with over 10,000 electrodes, allows for single cell resolution signal acquisition and mapping of the network activities.

1 Introduction This project aims at cultivating hippocampal slices on high-density multi-electrode array (HD-MEA) chips for long-term extracellular electrophysiology recordings. Organotypic brain slices constitute a preparation in between dissociated cell cultures and in vivo electrophysiological experiments with respect to the representation of brain-like morphology. Organotypic brain slice cultures preserve the local neuronal network architectures, cell-to-cell connections, and characteristics of the original neuronal network. Longterm brain slice cultivation allows for studying network development and dynamics. The culturing of organotypic brain slices directly on HD-MEAs preserves, to a large extent, the slice morphology and enables to precisely allocate the cells in close vicinity to the underlying electrodes. Spontaneous neuronal activity can be assessed at any time point, and the slice becomes very thin at an advanced stage of cultivation. The latter property, combined with the HD-MEA chips, containing over 11’000 electrodes, allows for obtaining an overview of network activities at singlecell resolution thanks to easier spike sorting. The brain slice cultivation method presented here relies on the Gähwiler method [1]. Slice cultures on HD-MEAs have been maintained for over 30 days, while daily recordings have been conducted.

2

Material and Methods

Newborn mice (C57Bl/6 mouse, age post natal day 6) were sacrificed for brain slice preparation. 8

sagittal hippocampal slices (300 µm thickness) were obtained per mouse and affixed to HD-MEA chips with chicken plasma and thrombin. The slice cultures on the HD-MEAs were maintained in custom culturing chambers that allowed for air exchange. The culturing chambers were continuously rotated, inside an incubator with stable temperature (36°C), gas atmosphere composition (5% CO2), and humidity (100%). HD-MEAs featuring 11’011 platinum electrodes were used for slice recordings [2, 3], 126 recording channels can be simultaneously used for readout. Sequences of 147 high-density block configurations were used to record from the whole electrode array area with overlaps. The recordings were performed daily inside an incubator under defined and sustained culturing conditions. The slice culture recordings were analyzed and yielded activity maps displaying varying spike amplitudes. Single-neuron activity was extracted by manual inspection of multichannel spike waveforms based on the principle component analysis (PCA) spike sorting (UltraMegaSort 2000, Hill D, 2011).

3 Results Organotypic hippocampus slices have been directly cultivated on HD-MEA chips for over 30 days. Hippocampus CA3 neuronal network activities and single cell footprints have been extracted during the cultivation period (Fig.1).


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Fig. 1. Activity map of one hippocampal CA3 slice culture after 10 days in vitro (DIV 10, left panel). The spike amplitudes are indicated by the color bar. The box region indicates the whole MEA array surface. The spike-triggered average spatial distribution (footprints) of one neuron is shown in the right panel

4

Conclusions

The method presented here provides an opportunity to follow single-neuron activities and related network dynamics in slices over extended time periods, while the relative positions of the preparation and electrodes remain stable. Acknowledgement This work was supported by the ERC Advanced Grant “NeuroCMOS” under contract number AdG 267351. Wei Gong received individual funding through the EU Marie Curie Initial Training Networks (ITN) EngCaBra. Contract number 2010-264417.

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References [1] [2]

[3]

Gähwiler BH. 1981 Organotypic monolayer cultures of nervous tissue. J Neurosci Methods. 1981 Dec; 4(4):329-42. Frey U, Egert U, Heer F, Hafizovic S, Hierlemann A. 2009, Microelectronic system for high-resolution mapping of extracellular electric fields applied to brain slices. Biosens Bioelectron. 2009 Mar 15;24(7):2191-8. Frey, U., Sedivy, J. , Heer, F. , Pedron, R. , Ballini, M. , Mueller, J. , Bakkum, D. , Hafizovic, S. , Faraci, F.D. , Greve, F. , Kirstein, K.-U. , Hierlemann A., 2010, Switch-MatrixBased High-Density Microelectrode Array in CMOS Technology. IEEE Journal of Solid-State Circuits. 45, 467-482.


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Four-Layer Closed-Loop Neuronal Networks Gregory J. Brewer1*, Liangbin Pan2, Sankaraleengam Alagapan2, Eric Franca2, Thomas DeMarse2, Bruce C. Wheeler2 1 University of California Irvine, USA 2 University of Florida, Gainesville, FL USA *Corresponding author. E-mail address: [email protected]

Abstract Our knowledge of the collection of interconnected networks in the brain would benefit from a greater understanding of the computational ability of these networks and their information transfer principles. To better understand the self-assembly of neuron networks and their communication, we created a four-compartment interconnected MEMS device over an electrode array and seeded it with live rat cortical neurons to be able to monitor electrical activity between sub-networks. We used tapered microtunnels to direct activity in a sequential direction around a four- compartment loop. During the spontaneous formation and propagation of bursts along this closed loop system one of the four chambers tended to dominate initiation of these events. We discuss potential mechanisms for this phenomenon. This type of design will facilitate construction of models for communication within and between subregions of the brain to decode information transfer

1 Background / Aims

3 Results

The brain is now widely appreciated as a collection of interconnected networks, but our understanding of the computational ability of these networks and their information transfer principles are poorly understood. To better understand the self-assembly of neuron networks and their communication we created a four-compartment interconnected device over an electrode array and seeded it with live neurons to be able to monitor electrical activity between sub-networks.

Four well devices, each containing a small population of cortical neurons, quickly extended neurites into the tunnels. However, due to differences in the size of the opening at each end of the tunnel (i.e., the taper) the direction of growth was biased. Neurites located on the wall of the chambers with large 10 um tunnel entrances quickly extended into those openings and eventually filled the tunnels between chambers. Neurites that originated near tunnel entrances with the small 3 um opening were less likely to enter the tunnels. Because of this bias, connectivity tended to extend from chamber to chamber in a unidirectional pattern that formed a closed loop. The effect was apparent in the pattern of spontaneous bursting that emerged and propagated between chambers (Fig. 1). First, the timing of these bursts indicated unidirectional propagation of activity in a sequential order around the loop reflecting the unidirectional bias in connectivity. Second, while any of the four chambers could initiate a burst, one of the four chambers tended to dominate. Bursts evoked by electrical stimulation in each compartment propagated in the same unidirectional manner, demonstrating preference for a closed, one-way loop.

2 Methods A four-chambered device was constructed in PDMS with microtunnel connections between the chambers. The 51 microtunnels that interconnected each chamber were tapered from widths of 10 to 3 um wide to guide the direction of axon growth. This design was intended to promote a unidirectional closedloop system of interconnected chambers where activity propagates along this loop. Each device was positioned onto the surface of a planar electrode array. Neurons from rat cortical hemispheres were seeded into each chamber (3000 cells/mm2 per chamber). After 3 weeks in culture, neural activity (spiking) was recorded to determine whether activity propagated in a preferred direction around the loop.


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4 Conclusion/Summary Tapered microtunnels were effective in directing activity in a sequential direction around a four- compartment loop. During the spontaneous formation and propagation of bursts along this closed loop system one of the four chambers tended to dominate initiation of these events. We discuss potential mechanisms for this phenomenon. This type of design will facilitate construction of models for communication within and between subregions of the brain to decode information transfer. Fig. 1. A burst of spiking activity recorded in sub-network A appeared to propagate sequentially to compartments B, C and D to form a closed loop with delays of 70-500 ms between subnetworks

Acknowledgement We thank NIH for their support (R01 NS052233). .

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3D Neural Networks Coupled to Planar Microtransducer Arrays: An Innovative In-Vitro Experimental Model for Investigating Network Dynamics Monica Frega1, Mariateresa Tedesco1, Paolo Massobrio1, Mattia Pesce2, Sergio Martinoia1* 1 Neuroengineering and Bio-nano Technology Group (NBT), Department of Informatics Bioengineering Robotics and System Engineering, University of Genova, Genova Italy 2 Department of Neuroscience and Brain Technologies, Italian Institute of Technology (IIT), Genova Italy * Corresponding author. E-mail address: [email protected]

Abstract Despite the extensive use of in-vitro models for neuroscientific investigations, all studies on cultured cells devoted to elucidate neurophysiological mechanisms and computational properties, are based on 2D neuronal networks. In this work, we present a new experimental paradigm constituted by 3D hippocampal networks coupled to MicroElectrode Arrays (MEAs). We show how the features of the recorded network dynamics differ from the corresponding 2D network model suggesting new avenues for the use of such 3D models for studies or for the development of bio-hybrid microsystems for in-vivo neural repair.

1 Introduction Large networks of cultured neurons chronically coupled to micro-transducer arrays are nowadays considered a valid experimental model for studying basic neurophysiological mechanisms governing the formation and conservation of neuronal cell assemblies. Despite the extensive use of such in-vitro models, studies on cultured cells devoted to network electrophysiology are still exclusively based on 2D neuronal networks. In recent years, scientists have used different methods in attempts to create 3D neuronal cultures [1]. It has shown that cells cultured in a 3D environment better represent in-vivo behaviour than cellular populations cultured in a monolayer [2]. However, no attempt has been made to collectively record the electrophysiological activity of such 3D neuronal networks. In this work, we present a novel experimental model in which 3D networks are coupled to MEAs to form a structurally and functionally connected 3D neural network. The introduction of this new in-vitro model systems will complement and further exploit the possibility offered by in-vitro model system for studying network dynamics and for addressing specific issue such as the relationships between structure and functions.

2 Methods 2.1 Cell culture and MEA setup Dissociated neuronal cultures are obtained from hippocampus of embryonic rats (E18) and plated onto 60-channels MEAs (Multi Channel Systems - MCS, Reutlingen, Germany). In order to construct a 3D neuronal network that resembles in-vivo conditions, we took inspiration from the Pautot’s method [3]. 3D neu-

ronal networks are constructed by seeding neurons onto a scaffold constituted by micrometric glass beads on which they are able to grow. The resulting 3D structure is composed on average by 5 layers of micro-beads and cells obtaining a physically connected 3D neuronal network with a cell density of about 80’000 cells/mm3. The developed set-up allowed us to record the network activity from planar MEAs. Additionally, we were able to deliver an electrical stimulation both from the bottom and from the top layers (in the latter case, by using a conventional tungsten electrode).

Fig. 1. Multi-layers of microbeads and neurons confined by a PDMS structure onto the active area of the MEA (blue shaded circle) and extensive encapsulation by neuritic processes (exposed to Map-2) around the micro-beads (green shaded circle).

3 Results 3.1 Spontaneous activity characterization Spontaneous activities of 2D (n=28) and 3D (n=39) neuronal networks were recorded after four weeks in culture by means of TiN 60 channels MEAs. Figure 2a shows a raster plot of the spontaneous activ-


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ity of a representative experiment performed on a 2D hippocampal culture. We can observe a quasisynchronous activity and network dynamics composed mainly of network bursts. Similarly, Figure 2b shows a raster plot of the spontaneous activity of a representative experiment of a 3D network. It can be observed that the signature of the network dynamics presents a wider repertoire of activities with less global synchrony and more pronounced spiking at a single channel-neuron level.

Fig. 2. 3 min of spontaneous activity of a. 2D neuronal network and b. 3D neuronal network.

The previous observations were quantified by evaluating the percentage of random spikes (i.e., the fraction of spikes outside bursts) in all the performed experiments. 2D network displays a low value of percentage of random spikes (15.9 ± 2% (mean ± standard error of the mean)), while 3D neuronal networks show a significant higher level of random spiking (33.8 ± 3 %; P-value =10-4, Mann-Whitney U-test). Even if network bursting remains a common signature for both preparations, the shift from 2D to 3D does also affect the bursting behavior. In 2D neuronal networks, the number of detected network bursts is high, the network burst duration seams similar for all the detected network burst. On the contrary, in 3D neuronal network we observed that the number of network bursts is lower and their duration is more variable. To examine the level of synchronization of the network activity, the cross-correlation (CC) function was evaluated for the 2D and 3D neuronal networks. CC function for 2D networks underlines the high level of correlation among all the recording channels. On the contrary, 3D neuronal networks exhibits a low level of correlation among the active channels in the random spike activity region; within the burst activity region, instead, the level of correlation among channels is high. To demonstrate whether the layers are effectively connected, we studied the propagation of the signal. In 2D neuronal network, the propagation of the signal is continuous, the signal starts from one source and propagates rapidly and in a continuous way in all the network. The estimated speed of propagation comput340

ed for all the experiment is 42 ± 3.5 mm/s. In 3D neuronal network, the signal that starts from one site propagates slowly in the network in a discontinuous way.

3.2 Stimulus-evoked activity characterization To further prove that the layers are functionally connected, we performed experiments by delivering electrical stimulating from the bottom and top layers. To quantify and compare the neuronal network responses to electrical stimulation we evaluated the percentage of Post-Stimulus Time Histogram (PSTH) area generated after electrical stimulation in different time windows. The response to stimulation of 2D networks is relatively fast as about 90% of the PSTH area is reached within the first 500 ms. 3D neuronal networks stimulated from the bottom layer exhibit a similar response to stimulation within all the time windows: we could observe a fast response (35% of PSTH area reached within the first 100 ms) and a slow response (among 30% of the response to stimulation is detected between 1s and 2s). In the case of 3D networks stimulated from the top layer, the fast response was practically not detected (only about 10% of PSTH area is within the first 500 ms) suggesting that no direct pathways are present. The responses generated from this type of stimulation are slow as about 90% of PSTH area is reached between 500 ms and 2 s.

4 Conclusion The obtained results demonstrate that we constructed a physically connected 3D neuronal network that exhibits a much richer repertoire of activity that cannot be found in uniform (i.e., non-patterned) 2D neuronal networks. This electrophysiological activity resembles more the one detected in in-vivo measurements in freely behaving rats [4]. References [1]

[2]

[3]

[4]

Cullen, D. K., Wolf, J. A., Vernekar, V. N., Vukasinovic, J. and LaPlaca, M. C. (2011). Neural tissue engineering and biohybridized microsystems for neurobiological investigation in vitro. Crit. Rev. Biomed. Eng. 39, 201-240. Irons H. R., Cullen, D. K., Shapiro, N. P., Lambert, N. A., Lee, R. H. and LaPlaca, M. C. (2008).Three-dimensional neural constructs: a novel platform for neurophysiological investigation. J Neural Eng, 5, 333-41. Pautot, S., Wyart, C., and Isacoff, E. Y. (2008). Colloidguided assembly of oriented 3D neuronal networks. Nature Methods, 5, 735-740. Leinekugel, X., Khazipov, R., Cannon, R., Hirase, H., BenAri, Y. and Buzsaki, G. (2002). Correlated bursts of activity in the neonatal hippocampus in vivo. Science, 296, 2049-2052.


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Directed Neuronal Networks on Multi-Electrode Arrays László Demkó1*, Csaba Forró1, Harald Dermutz1, János Vörös1 1 Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, ETH Zurich, CH-8092, Switzerland * Corresponding author. E-mail address: [email protected]

Abstract The use of cultured neuronal networks as a model for their in vivo counterparts allows researchers to study the central nervous system, especially the brain, in a controlled environment. Specific neuronal connectivity patterns in the brain are implicated to play a role in the perception, processing and storage of information, so the building of small neuronal networks with controlled topology is a promising approach to investigate the basic circuits of the nervous system. The present work focuses on the different techniques developed in our group to realize small neuronal networks with well-defined topology, directly on MEA chips.

1 Aims

3 Results

One of the grand challenges of this century is to learn how our brain functions. Despite of the vast amount of technical possibilities we still have very little insight (and especially consensus) into e.g. how memory is stored. One reason for this might be that our current knowledge in neurosciences mostly derives from single cell technologies such as patch clamping, or system level studies (e.g. MRI). Multielectrode arrays (MEA) try to fill in this gap by providing information about neural slices and cultures, however, even though several attempts have been made to build more controlled neuron networks (microcontact printing, microfluidics, etc.) there is still no reliable technology that could provide welldefined in vitro networks with oriented connections on MEA chips. Due to the lack of such “study networks”, the field is forced to perform experiments on highly complex systems which makes a complete understanding of the fundamental aspects very difficult.

We demonstrate the applicability of the developed methods on MEA chips with different electrode arrangements. The results show the proof of concept by realizing directed connections between rat hippocampal neurons sitting on neighbouring electrodes, and the analysis of the resulted neuronal activity from the measured extracellular signals.

2 Methods In this work we demonstrate different methods to realize small neuronal networks on MEA chips in a controlled and reproducible manner. The type, number, position of the neurons, as well as the connections and their directionalities can be defined and kept under control by combining PDMS sheets and electrically driven axonal outgrowth. The role of the PDMS sheets are multifold: first they provide control over the seeding concentration and cell positions, then they confine neurite outgrowth into the predefined directions and make closed compartments for improved electrical recordings.

4 Summary By creating reproducible neuronal networks with well-defined topology, our vision is to bridge the gap between micro and macro, the molecular or few cell level and whole brain experiments. With the results, we aim to provide insights into the role of neuronal and synaptic properties such as synapse development, plasticity and connectivity, and extract the algorithms that support information processing and neurocomputation in brain tissue. Acknowledgement This research has been supported by the 3DNeuroN project in the European Union's Seventh Framework Programme, Future and Emerging Technologies, grant agreement n°296590.


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Human Pluripotent Stem Cell Derived Neural Cells Obtained Either Using Adherent or Neurosphere Based Differentiation Have Ability to Form Spontaneously Active Neuronal Networks Tiina Joki1*, Pauliina Salonen2, Tarja Toimela2, Riikka Äänismaa1, Timo Ylikomi2, Susanna Narkilahti1 1 NeuroGroup, BioMediTech, University of Tampere, Tampere, Finland 2 Finnish Centre for Alternative Methods (FICAM), University of Tampere, Tampere, Finland * Corresponding author. E-mail address: [email protected]

Abstract Human pluripotent stem cells derived neural cells are considered to be a promising tool for both basic research and clinically orientated purposes. The ability to from functional interconnected networks is innate to neurons in vivo. Thus it is important that this ability is also retained in vitro differentiated neurons. In this work we used two types of human stem cell derived neural cultures, differentiated using either adherent or suspension based method, to study their emerging neuronal network properties. According to our results, both culture types were able to maturate into functional neuronal networks within the similar time frame.

1 Background / Aims Neural differentiation of hPSCs is mainly performed using two growth factor based technics a) suspension culture of floating cell aggregates (embryoid bodies or neurospheres) or b) adherent culture as a cell monolayer or combination of both [1, 2]. Both methodologies have their lacks and advantages. Suspension methods require longer differentiation times and produce more heterogeneous cell populations containing also glial cells. More laborious adherent methods are faster and produce more homogenous neural stem cell populations which can be further used for creation of neural precursor cell banks. In the brain, the main function of neurons is to transmit and process electrical signals. When the neural differentiation is performed in vitro, it is important to show that the produced neuronal networks are able to form in vivo-like electrophysiological properties. The micro electrode array (MEA) is an ideal tool for long term follow up studies of network level functionality. Although the MEA technology has been available for many decades [3, 4], only few articles about hPSC derived neurons on MEAs exist [5–12]. Previously, suspension method has been shown to produce active neuronal networks [8–12]. The aim of this work was to study whether commercially available neural precursor cells differentiated via adherent method have similar ability to form spontaneously active neuronal networks compared to suspension method.

2 Methods / Statistics Cells used in this experiment were: 1) Group1: hNP1 (ArunA Biomedical, Inc. Athens, GA, USA), commercial neural precursors manufactured from human embryonic stem cell (hESC) line WA09 using an adherent method

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2) Group2: Regea 08/023 hESC line derived neural cells, differentiated using a suspension based method The hNP1 neural precursors (LOT: 7003-001, p0) were maintained according to manufacturer’s instructions and for experiments maturated according to the published protocol [13]. The hESCs (Regea 08/023) were differentiated into neural cells according to previously published method [2] and used for experiments at 8 week differentiation point as previously described [8]. Cell maturation was studied with immunocytochemistry and with network level spontaneous activity measurements using 6-well MEAs (MultiChannel Systems GmbH, MCS, Reutlingen, Germany) with inhouse build silicone cell culture chambers [12]. The electrophysiological MEA measurements were performed in BioMediTech’s electrophysiology laboratory after culturing cells 22, 28, 31 and 35 days in vitro (DIV) on MEA dishes (parallel samples: hNP1 n=18 and Regea 08/023 n= 12 ). Spike detection was performed online with MC_Rack (MCS), spikes and bursts were analyzed using Neuroexplorer (NEX Technologies, AL, USA) and previously published burst detection algorithm [11] for MATLAB (MathWorks, MA, USA). Immunocytochemical studies were performed at DIV 28 with cell plated on 48 well culture plates (Nunc, NY, USA) according previously published protocol [2]. Statistical analysis was performed with GraphPad Prism 5 (GraphPad Software, Inc. CA, USA) using Kruskal-Wallis test followed by Dunn’s multiple comparison test (post hoc test).

3 Results According to immunocytochemical analysis, both groups were positive for neuronal and astrocytic markers after differentiation and maturation. The hNP1 and Regea 08/023 cultures contained MAP-2 positive neurons. Both studied groups were also posi-


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tive for GFAP, an astrocytic marker, however, the morphology of GFAP+ cells was very different between the cultures (representative image, Fig.1).

Fig. 1. Immunocytochemical staining of hNP1 derived (A) and Regea 08/023 derived (B) neuronal networks. Staining with neural markers MAP-2 (green, neurons) and GFAP (red, astrocytes) and nuclear staining with DAPI (blue).


The activity developed from single spikes into training and even bursting during the 5 weeks of culturing as previously described for hESC derived neuronal cultures [8]. Although the amount of active channel was increasing in time in hNP1 networks and decreasing in 08/023 networks, the spiking frequency was unchanged between cultures (Fig. 2 C, D). The burst analysis suggested differences in burst behavior but no statistically significant differences were found (Fig 2 E, F). Synchronous activity was seen in both groups (Fig. 3.)

Both groups were able to form neuronal networks that expressed spontaneous activity on MEA (Fig. 2 A, B). Fig. 3. Representative images of the raw data at DIV 35 from recordings showing synchronous electrical activity of the hNP1 derived (A) and Regea 08/023 derived (B) neuronal networks.

4 Conclusion/Summary Here we show that neural cultures derived from hPSCs either using adherent or suspension based method have the ability to form functional neuronal networks. The activity development was similar between the two studied groups and also comparable to earlier studies [8–12]. However, for verifying the effect of different differentiation methods, larger experimental set-up is required. Acknowledgement We acknowledge for financial support: Academy of Finland (SpareBrain project, 139345), Finnish Funding Agency for Innovation (Human Spare Parts project), The Pirkanmaan Reginal Fund, and FPDP-T.

Fig. 2. Representative 3D plot of spontaneous activity during 10 min measurement of one hNP1 MEA (A) and one Regea 08/023 MEA (B). The percentage of active channels compared to all channels (C), spiking frequency, spikes per second (D), the amount of detected bursts per well (E), and the average amount of spikes in bursts (F). n= amount of parallel wells, in D and C (active channel: more than 5 spikes per measurement) and E and F, n = active wells defined by burst detection algorithm [11]. [8]

References [1] [2]

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[7]

Dhara, S. K., & Stice, S. L. (2008). Neural differentiation of human embryonic stem cells. Journal of cellular biochemistry, 105(3), 633–40. doi:10.1002/jcb.21891 Lappalainen, R., Salomäki, M., Ylä-Outinen, Heikkilä, T., Hyttinen, J., Pihlajamäki, H., … Narkilahti, S. (2010). Similarly derived and cultured hESC lines show variation in their developmental potential towards neuronal cells in long-term culture. Regen. Med. (2010), 5(5), 749– 762. Retrieved from http://www.futuremedicine.com/doi/abs/10.2217/rme.10.58 Pine, J. (1980). Recording action potentials from cultured neurons with extracellular microcircuit electrodes. J Neurosci Methods, 2(1), 19–31. Retrieved from http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_ui ds=7329089 Gross, G. W., Rieske, E., Kreutzberg, G. W., & Meyer, A. (1977). A new fixed-array multimicroelectrode system designed for long-term monitoring of extracellular single unit neuronal activity in vitro. Neurosci Lett, 6(2-3), 101–105. Retrieved from http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_ui ds=19605037 Odawara, a, Saitoh, Y., Alhebshi, a H., Gotoh, M., & Suzuki, I. (2014). Long-term electrophysiological activity and pharmacological response of a human induced pluripotent stem cell-derived neuron and astrocyte co-culture. Biochemical and biophysical research communications, 443(4), 1176–81. doi:10.1016/j.bbrc.2013.12.142 Gonzalez, R., Garitaonandia, I., Abramihina, T., Wambua, G. K., Ostrowska, A., Brock, M., … Semechkin, R. a. (2013). Deriving dopaminergic neurons for clinical use. A practical approach. Scientific reports, 3, 1463. doi:10.1038/srep01463 Muratore, C. R., Rice, H. C., Srikanth, P., Callahan, D. G., Shin, T., Benjamin, L. N. P., … YoungPearse, T. L. (2014). The familial Alzheimer’s disease APPV717I mutation alters APP processing and Tau expression in iPSC-derived neurons. Human molecular genetics. doi:10.1093/hmg/ddu064

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Heikkilä, T. J., Ylä-Outinen, L., Tanskanen, J. M., Lappalainen, R. S., Skottman, H., Suuronen, R., … Narkilahti, S. (2009). Human embryonic stem cell-derived neuronal cells form spontaneously active neuronal networks in vitro. Exp Neurol, 218(1), 109–116. Retrieved from http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_ui ds=19393237 Ylä-Outinen, L., Heikkilä, J., Skottman, H., Suuronen, R., Äänismaa, R., & Narkilahti, S. (2010). Human cell-based micro electrode array platform for studying neurotoxicity. Front Neuroeng, 3(pii: 111). Mäkinen, M., Joki, T., Ylä-Outinen, L., Skottman, H., Narkilahti, S., & Äänismaa, R. (2013). Fluorescent probes as a tool for cell population tracking in spontaneously active neural networks derived from human pluripotent stem cells. Journal of Neuroscience Methods, 215, 88–96. doi:10.1016/j.jneumeth.2013.02.019 Kapucu, F. E., Tanskanen, J. M. A., Mikkonen, J. E., Ylä-Outinen, L., Narkilahti, S., & Hyttinen, J. A. K. (2012). Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics. Frontiers in Computational Neuroscience. doi:10.3389/fncom.2012.00038 Kreutzer, J., Ylä-Outinen, L., Kärnä, P., Kaarela, T., Mikkonen, J. E., Skottman, H., … Kallio, P. (2012). Structured PDMS Chambers for Enhanced Human Neuronal Cell Activity on MEA Platforms. Journal of Bionic Engineering, 9, 1–10. Retrieved from http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_ui ds=19243222 Young, a, Machacek, D. W., Dhara, S. K., Macleish, P. R., Benveniste, M., Dodla, M. C., … Stice, S. L. (2011). Ion channels and ionotropic receptors in human embryonic stem cell derived neural progenitors. Neuroscience, 192, 793–805. doi:10.1016/j.neuroscience.2011.04.039

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Design and Fabrication of a Second-Generation Perfusion Cap for Enhanced Optical Access and Manipulation of Cell Cultures Anil Krishna Konduri1, Dirk Saalfrank2, Rouhollah Habibey1, Asiyeh Golabchi1, Marina Nanni1, Axel Blau1* 1. Dept. of Neuroscience and Brain Technologies (NBT), the Italian Institute of Technology (IIT), Via Morego 30, 16163 Genoa, Italy, www.iit.it 2. Dept. of Informatics and Microsystem Technology, University of Applied Sciences Kaiserslautern, 66482 Zweibrücken, Germany, www.fh-kl.de *Corresponding author: [email protected]

Abstract This work describes a novel neural cell culture perfusion system compatible with a commercial multielectrode array (MEA) recording setup. A controlled and continuous flow of cell culture medium and the long-term monitoring of cell physiology are two advantages of perfusion systems. However, both can be significantly disturbed by air bubble entry through the external tubing. This study targeted at the improvement of the internal perfusion cap design to not only trap and guide bubbles to the medium outlet, but improve its overall tightness and suitability for short-working distance microscopy. For the latter reason, we integrated a glass cover slip window at the bottom of the PDMS perfusion cap to improve the optical clarity and an internal wall groove for guiding air bubbles. The flow and leakage properties of this cap design were compared to a cap featuring a PDMS membrane instead of a glass coverslip.

1 Introduction

2 Materials & Methods

Perfusion systems made of PDMS have been widely used in cell culture-based studies. They show advantages over traditional cell culture containers i.e. by offering the possibility of mimicking an in vivo environment. Control over continuous medium flow through micro tubes could be useful for extending long-term survival on MEAs previously shown for static cultures [1] [2]. Long-term cell culture is useful in many case studies, like continuous time-lapse imaging and extracellular electrophysiology at ambient conditions [3]. PDMS is an advantageous polymer for various cell culture applications because of its transparency, gas permeability, bio-compatibility and flexibility. However, some of these features could be considered as a disadvantage for a PDMS perfusion system. For instance, the flexibility of a PDMS cap could lead to leakage and air bubble formation, which are problematic for optical imaging and culture viability in longterm electrophysiology studies on MEAs [3]. We designed a second-generation perfusion cap, which can be easily fabricated using a PDMS scaffold with an integrated PDMS/glass coverslip window. It has the advantages of (1) having a higher resistance against pumping pressure fluctuations, (2) featuring mechanical stability paired with high transparency for optical imaging, and (3) including a bubble trap guidance mechanism towards the medium outlet. It can be fabricated either by additive manufacturing (3D printing) or replica-molding and its implementation is lowcost. 344

2.1 Perfusion cap fabrication We present a perfusion concept based on a replica-molding strategy from a 3D-printed acrylonitrile butadiene styrene (ABS) moulding template, which consisted of three removable parts (Fig.1 A). Their combination determined the height as well as the outer and inner diameters of the PDMS cap. The two-part PDMS material (Sylgard 184, Dow Corning) was mixed in the ratio of 10:1 (v/v), and, after degassing, was poured into the assembled moulding template and cured at 85 °C for 30 min (Fig. 1 B). Two holes (Ø 1.2 mm) were centered at opposite sides of the upper edge (Fig 1. C) [2]. They served as holders for polytetrafluorethylene (PTFE) inlet and outlet tubes. Each cap consumed 6-7 ml of the PDMS precursor mix. Perfusion cap templates were designed (Creo CAD, PTC) to fit the 30 mm standard outer diameter (OD) of commercially available MEA rings.


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Fig.2. Fluctuation tolerance test in two types of perfusion caps. (A) PDMS cap with glass coverslip and desired fluid flow, (B) PDMS cap with PDMS window, which showed leakage of fluid.

3.2 Bubble guidance mechanism

Fig.1 . Perfusion cap assembly. Overview of the cap production out of PDMS: (A) Template components, (B) assembled template, (C) molding PDMS perfusion cap from template, (D) cross-section of PDMS perfusion cap with a glass coverslip or PDMS window (*), (E) 3D cross-section of an experimental PDMS perfusion cap design currently being under production. (1. Slope for microscope objective movement, 2. groove for laser dissection objective movement, 3. glass coverslip/PDMS membrane, 4. space for tubing, 5. groove for bubble trap/guidance mechanism, 6. double-walled jacket)

3 Results Two types of PDMS caps have been developed and compared, namely a PDMS perfusion cap with the central window composed of a PDMS membrane (number 3 in Fig 1. D) and a cap with a glass coverslip instead of the PDMS membrane. Both types of the caps were made from the same template (Fig.1 A). The glass slide was bonded with PDMS to the perfusion cap. Resistance to pump-induced fluctuations in the fluid flow and bubble trapping was compared for these two caps.

Air may penetrate the cap or tubing and enter the device. In the presented PDMS cap, bubbles were trapped in a groove (number 5 in Fig. 1E) at a minimum flow rate of 20 µl/day. Due to the continuous fluid flow, the bubbles tended to move slowly toward the outlet (Fig. 2A). This prevented them from accumulating underneath the central window thereby causing subsequent image distortion during long-term imaging. A minimum flow rate of 20 µl/day was sufficient for guiding bubbles to the outlet.

4 Conclusions The second generation PDMS perfusion cap with a glass cover slip window minimized fluid related problems and leackage. In addition, the device successfully guided bubbles toward the outlet. The glass coverslip window and bubble guidance will be useful in improving the optical visibility of samples in a perfusion system. Acknowledgement This work was supported by intramural funds at the Italian Institute of Technology.

References [1]

3.1 Fluctuation tolerance test The tolerance of each cap against deformation was tested by monitoring the fluid pressure created by a stepper motor-driven syringe pump (flow rate: 20 µl/day). In both prototypes we observed that the cap with the glass coverslip was more stable to deformation. The cap made with PDMS showed signs of leakage between the glass ring and the perfusion cap, while the PDMS cap with the glass cover slip showed a homogenous fluid distribution without leakage (Fig.2).

[2]

[3]


Blau A., Neumann T., Ziegler C., Benfenati F. (2009) Replicamolded poly(dimethylsiloxane) culture vessel lids attenuate osmotic drift in long-term cell culturing, J. Biosci., 34, 59-69. Potter, S.M., DeMarse. T.B. (2001) "A new approach to neural cell culture for long term studies." J. Neurosci Methods 110: 17-24. Saalfrank D., Vasile Martiniuc A., Konduri AK., Habibey R., Golabchi A., Latifi Sh., Knoll A., Ingebrandt S., and BlauA; Incubator-independent cell culture perfusion platform for continuous long-term electrophysiology and time-lapse imaging reveals fluctuations in neural network activity and local architecture over the months paper submitted.

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Primary and Stem-Cell Derived Cardiac Myocyte Cultures

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Primary and stem-cell derived cardiac myocyte cultures

Combined MEA Recording and Ca-Ratiometry in hiPSC-Derived Cardiomyocytes for Cardiac Safety Assessment Georg Rast Abteilung Drug Discovery Support Boehringer Ingelheim Pharma GmbH & Co. KG Birkendorfer Straße 65 D-88397 Biberach an der Riss [email protected]

Abstract Multi-Electrode arrays (MEA) enable the recording of multiple field potentials from various electrically active surfaces. This presentation focuses on the acquisition of ECG-like signals from two-dimensional cultures of human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes in combination with Fura-based Ca-ratiometry. Fura-2 is a Ca-sensitive dye which is used to record intracellular Ca levels with medium temporal resolution. The nine well, 256 channel MEA platform provided by Multichannel Systems was extended to accommodate nine independent fiber optic channels for composite Ca transient recording from the cell population in each well. The wells were equipped with an individual perfusion for sequential and escalating application of test compound or solvent (control). Experiments were designed in a way that on each nine well chip, three wells treated with solvent control can be tested against six wells treated with test compound at up to five concentrations. Profiling of reference compounds demonstrates that the combination of the read-out parameters from field potential and Ca transient analysis enables a detailed characterization of the pharmacology of the respective compound. In particular, inhibition of ion channels, intracellular calcium handling, and changes in cAMP may be detected. Furthermore, specific physiological properties of the cells in use may be characterized. In the present case using hiPSC-derived cardiomyocytes, it was found that they deviate substantially from primary cells. An in-depth analysis of the base-line properties of the cellular systems used is essential for the interpretation of potential compound effects.


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Human Stem-Cell Derived Neuronal Cell Based In Vitro Platforms for Neurotoxicity and Disease Modelling Susanna Narkilahti1* 1 NeuroGroup, BioMediTech, University of Tampere, Tampere, Finland * Corresponding author. E-mail address: [email protected]

Abstract Human pluripotent stem cells and their neuronal and glial derivatives have raised high hopes in the field of regenerative medicine. Furthermore, these cells are useful in the fields of human development studies, disease modelling, drug screening, and neurotoxicology. Currently, most of the analyses are based on in vitro and in vivo animal models or cells. Moreover, most of the neurotoxicological analyses are based on molecular biology end point analyses which do not take into account the cell type specific functionality at all. For neurons, their most crucial characteristic is the electrical signalling. The most optimal method to measure and monitor the electrical functionality of neuronal networks in vitro is the microelectrode array (MEA) setup. This method has been implemented by both academic research parties and industry for neurotoxicological screening to some extent using both animal and human derived neurons. Due to their close resemblance to the brains of developing human foetuses, human pluripotent stem cells derived neuronal cultures in MEA platform offer unique tool to study the aspects of neurotoxicity in humans and to model certain diseases. However, many aspects of these kinds of in vitro models still require optimization. The several aspects of model optimization will be discussed in this presentation.

1 Background The discovery on human embryonic stem cells (hESC) in 1998 [1] opened a door for totally new area for both clinical treatment development and for in vitro research. It was hoped that these cells, able to differentiate into every cell type in human body (excluding placental structures), would provide source of making needed cells types in great amounts. And surely, it is possible for example to make various types on neurons and glial cells from hESCs in vitro [2]. Even though the first great expectation of utilizing hESC derivatives was in regenerative medicine, it was acknowledged that these cells would be beneficial also in other areas in vitro such as in drug development and screening, neurotoxicological analysis and in the developmental studies of human brain. As hESC possesses ethical challenges, the discovery of human induced pluripotent stem cells (hIPSC) in 2007 [3] has broaden up the use of these human pluripotent stem cells (hPSC = hESC + hiPSC) for multiple purposes as hiPSCs can be produced from any individual. One of the greatest advantages of hiPSC is that when producing them from a patient bearing disease with genetic background it is possible to create particular cells in a laboratory dish that should have the same defect than the patient. It has already been shown making hiPSC derived neurons from patients with neurological diseases bearing genetic background is possible [4]. Thus, hiPSC derivatives have brought up new areas such as development of particular disease models and development of per-

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sonalized medicine strategies where hPSC can further benefit us.

2 Methods and results 2.1 Neural differentiation and characterization of human pluripotent stem cells After 2005 we have developed a relative simple cell suspension differentiation method for hPSCs [2]. With this method, we have tested the neuronal differentiation capacity of 15 hESC and 13 hiPSC lines derived and produced with different methods, obtained either from research laboratories or commercially. We have showed that all of these hiPSC lines can be differentiated into neuronal cells but there is a relatively huge line, but not origin, depended variation in the efficiency [2, 5, unpublished data]. The neuronal characteristics were studied in gene and protein expression levels (RT-PCR, qRT-PCR, immunocytochemistry, flow cytometry). Importantly, all produced neuronal cultures were able to form spontaneously functional neuronal networks when studied using micro electrode arrays (MEA, Multichannel Systems, MCS, Germany) in a setting developed by us [6]. The development of spontaneous activity and maturation was depended on the initial neuronal differentiation capacity of a particular hiPSC line. In next step, we have taken both hESC and hiPSC lines which have good neuronal differentiation capacity into more detailed analysis to see if there are differences between neuronal properties. This is an ongoing


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work. Preliminary results suggest that both electrophysiological properties of single neurons (patch clamp) and neuronal networks are very similar between cells derived either from hESCs of hiPSCs.

2.2 MEA measurements of hPSC derived neuronal networks The MEA environment differs from cell culture plastic or glass were neuronal cells are typically cultured and thus it does not provide optimal culturing conditions. Also, as the electrodes on MEA’s are embedded in the middle of the MEA chamber it is challenging to get the neurons grow on that area and not surrounding areas. To optimize the MEA culturing environment, we developed a silicone chamber that restricts the culturing area only to the area of electrodes in MEA [7]. This has provided us a way to be more successful with neuronal culturing in MEA’s and also resulted in direct cost saving as less coating materials and cells are needed per MEA. The hPSC derived neuronal networks represent developmentally the earliest types of networks existing. It is not known yet how far these networks can be maturated and thus what developmental stage they mimic in vivo (fetal, childhood or adult human neuronal networks). It is, however, quite clear that when comparing functional properties of hPSC and primary rodent neuronal networks in vitro, there are differences in network characteristics (maturation time, activity parameters, level of synchronous activity, ect.). Therefore, we developed an algorithm to analyse bursts in these highly variable hPSC derived neuronal networks [8]. One of the application areas of hPSC derived neurons is neurotoxicology. Thus, we tested if hPSC derived functional neuronal networks could be used for this purpose. We showed that methyl mercury, a known neurotoxicant, influences functionality of these networks in concentration level that does not cause visible toxic effects when measured with standard molecular biology methods [9]. The disease modelling in vitro has become very active research area especially after the discovery of hiPSC cells. In case neurological disorders, it is quite obvious that development of models were the functional properties of the created neuronal networks can be measured are needed. This reply both in developing disease models bearing genetic background or more general, for example, injury related models. In order to develop this area further we tested how the human cerebrospinal fluid affects functionality of neuronal networks [10]. As part of development of in vitro models certain type of platforms with additional features may be needed including culturing of mixed cell populations, restrictions between different cells areas or cell parts ect. This may also lead to need to design different kinds of MEA electrode arrays with different patterns. As part of developing our own in vitro models, we have tested different electrode mate-


rials and manufacturing process in order to made inhouse MEA’s [11, 12].

3 Discussion and conclusions Human pluripotent stem cells derived neurons provide us tool to develop many application areas such as neurotoxicological screening and disease modelling. As the main function of neurons is to create functional neuronal networks, those networks should be used as principle component in the developed platforms. Thus, MEA provides basis for these platform development. There are, however, many aspects that need to take into account: 1) Although there are hundreds of hESC and hiPSC lines available today they do not differentiate similarly towards neurons. In addition, there are multiple differentiation methods resulting in different neuronal amounts and neuronal subtypes. Further, as hiPSC are modified cells from the beginning there may be some abnormalities or changes in the derived neuronal networks. Thus, one needs to think carefully what kind of hPSC line to use for production of the neuronal networks for particular use. 2) MEA measurements are feasible to conduct with hPSC derived neuronal networks. Those contain, however, relatively big network to network variations and differ from rodent primary neuronal networks that are more or less the standard for neurotoxicological screening today. Thus, there should be standardization of producing these hPSC derived neuronal networks and also more detailed characterization in multiple levels. 3) The development of MEA technology to the HTP systems will of course to help to tackle the variation seen in functional properties as sample sizes can be increased. That does not remove the need to analyse the properties of hPSC derived neuronal networks more carefully and compare those to the rodent primary neuronal networks. On the other hand, as the differentiation methods are optimized further that can result in more homogenous neuronal networks. 4) The disease modelling may require more complicated in vitro platforms. In these platforms both structural components and cellular composition of that particular brain area may need to be recapitulated as well as possible. Whether that leads even to 3D in vitro models remains to be seen. Of course that would add on requirements also for used MEA technology. Acknowledgement I like to thank previous and present persons in NeuroGroup, BioMediTech for all of their hard work and commitment. These studies have been funded by Academy of Finland (SpareBrain project, 139345), Finnish Funding Agency for Innovation (Human Spare Parts project) and FP7 Future and Emerging Technologies 3DNeuroN project (296590).


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References [1] Thomson, J. A., Itskovitz-Eldor, J., Shapiro, S. S., Waknitz, M. A., Swiergiel, J. J., Marshall, V. S., and Jones, J. M. (1998). Embryonic stem cell lines derived from human blastocysts. Science 282, 1145-1147. [2] Lappalainen, R. S., Salomäki, M., Ylä-Outinen, L., Heikkilä, T. J., Hyttinen, J. A., Pihlajamäki, H., Suuronen, R., Skottman, H. and Narkilahti, S. (2010). Similarly derived and cultured hESC lines show variation in their developmental potential towards neuronal cells in long-term culture. Regen Med. 5, 749-762. [3] Takahashi, K., Okita, K., Nakagawa, M. and Yamanaka, S. (2007). Induction of pluripotent stem cells from fibroblast cultures. Nat Protoc. 2, 3081-3089. [4] Han S.S., Williams L.A., Eggan K.C. (2011) Constructing and deconstructing stem cell models of neurological disease. Neuron 70, 226;70(4):626-644. [5] Toivonen S., Ojala M., Hyysalo A., Ilmarinen T., Rajala K., Pekkanen-Mattila M., Äänismaa R., Lundin K., Palgi J., Weltner J., Trokovic R., Silvennoinen O., Skottman H., Narkilahti S., Aalto-Setälä K., Otonkoski T. (2013) Comparative analysis of targeted differentiation of human induced pluripotent stem cells (hiPSCs) and human embryonic stem cells reveals variability associated with incomplete transgene silencing in retrovirally derived hiPSC lines. Stem Cells Transl Med. 2(2)83-93. [6] Heikkilä, T. J., Ylä-Outinen, L., Tanskanen, J. M., Lappalainen, R. S., Skottman, H., Suuronen, R., Mikkonen, J. E., Hyttinen, J. A. and Narkilahti, S. (2009). Human embryonic stem cell-derived neuronal cells form spontaneously active neuronal networks in vitro. Exp Neurol. 218, 109-116. [7] Kreutzer, J., Ylä-Outinen, L., Kärnä, P., Kaarela, T., Mikkonen, J. E., Skottman, H., et al. (2012). Structured PDMS Chambers for Enhanced Human Neuronal Cell Activity on MEA Platforms. Journal of Bionic Engineering, 9, 1–10 [8] Kapucu F.E., Tanskanen J.M.A., Mikkonen J.E., Ylä-Outinen L., Narkilahti S., Hyttinen J.A.K. (2012) Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics. Front Comput Neurosci. 6:38 [9] Ylä-Outinen L., Heikkilä J., Skottman H., Suuronen R., Äänismaa R., Narkilahti S (2010) Human cell-based micro electrode array platform for studying neurotoxicity. Frontiers in Neuroengineering 3(111):1-9 [10] Kiiski H., Äänismaa R., Ylä-Outinen L., Aho A., Yli-Hankala A., Bendel S., Skottman H., Kreutzer J., Kallio P., Tenhunen J., Narkilahti S. (2012) Human cerebrospinal fluid promotes glial differentiation of human embryonic stem cell-derived neural cells but does not prevent spontaneous activity in preexisting neuronal networks. Biol Open. May 13;2(6):605-612. [11] Ryynänen T., Ylä-Outinen L., Narkilahti S., Tanskanen J.M.A., Hyttinen J., Hämäläinen J., Leskelä M., Lekkala J. (2012) ALD IrOx Thin Film as Microelectrode Coating in Stem Cell Application. Journal of Vacuum Science and Technology A 30(4), Jul-Aug, 2012. [12] Ryynänen T., Kujala V., Ylä-Outinen L., Korhonen I., Tanskanen J., Aalto-Setälä K., Hyttinen J., Kerkelä E., Narkilahti S., Lekkala J. (2011) All-Titanium Microelectrode Array for Field Potential Measurements from Neurons and Cardiomyocytes - a Feasibility Study. Micromachines 2, 394-409

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Comparative Assessment of Different Types of Human Stem Cell Derived Cardiomyocytes for Predictive Electrophysiological Safety Screening Using Microelectrode Arrays (MEA) Udo Kraushaar1*, Dietmar Hess3, Hua Rong Lu2, David Gallacher2 & Elke Guenther1 1 NMI Natural and Medical Sciences Institute, Reutlingen, Germany 2 Janssen Pharmaceutica NV, Beerse, Belgium 3 NMI-TT GmbH, Reutlingen, Germany *corresponding author. Email adress: [email protected]

The need for new strategies in preclinical compound testing is intensively debated by the pharmaceutical industry. In this respect, cardiomyocytes generated from human stem cells (hSCs) are regarded as a promising source to develop meaningful in vitro test models of adverse cardiovascular effects, including electrophysiological safety screening. Our study is the first to compare the pharmacological profile between different types of hSCs by means of MEA recordings and to relate it to existing electrophysiological preclinical cardiac safety models.

Commercially available human embryonic or induced pluripotent stem-cell derived cardiomyocytes (hiPSCs, hESCs) from three providers were electrophysiologically validated against ten compounds with different modes of action. Spontaneous field action potentials (fAP) were recorded from the cells directly seeded on the recording electrodes of 6-well MEAs.

Based on these findings, we conclude that hSCderived cardiomyocytes in principle are a promising cell source for electrophysiological cardiac safety assays but still need to be improved towards the expression of a mature electrophysiological phenotype to avoid false-negative or -positive responses.

Compound effects comprised changes of the initial phase of the fAP (Na+ component), fAP duration as well as changes of the spontaneous beating frequency and regularity. For most of the reference compounds, all cell types investigated expressed the same alterations in the parameters analyzed in response to the compounds, but with different sensitivities. Comparison of our results with literature data from other preclinical cardiac safety models revealed in most but not all cases a good pharmacological correlation for all cell types tested.


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On-Chip Measuring of Drug-Induced Cardiac Action Potential Elongation of Single Cells with Patch Clamp Resolution Danny Jans, Ben Huybrechts, Simon Schaerlaeken, Andim Stassen, Luis Hoffman, Jordi Cools, Geert Callewaert, Dries Braeken1 1 imec, Kapeldreef 75, 3001 Leuven, Belgium

Abstract Drug development is hampered by the awareness of drug-induced cardiotoxicity resulting in the rejection of pharmaceutically active compounds. To a certain level cardiac malfunction can be understood from the electrical activity of single cardiac cells. In this study we present a novel assay applied on a multi-electrode array (MEA) built on top of complementary metal-oxide-semiconductor (CMOS) electrical circuitry to address the electrical activity of individual cardiac cells in a network. The electrodes (diameter ± 1 µm) are subcellular sized and allow for both electrical stimulation and recording. We used primary cultures of rat neonatal ventricular cardiac cells after 5 to 7 days of culture. Application of different doses of cisapride demonstrated the potential of the system to measure dose-dependent increases of the action potential duration (APD) of ventricular cardiac cells.

1 The Open-Cell State Drug-induced cardiotoxicity of pharmaceutically active compounds is often recognized as an elongation of the QT segment in the electrocardiogram. Current in vitro tools fail either the versatility of using appropriate cell types or the ability to report detailed electrical activity. The in vitro screening system needs to involve the use of cardiac ventricular cells, since the APD of these cells coincides with the QT interval. Our CMOS-MEA presents the electrical activity of a single cardiac cell as individual spikes on a noise band with amplitudes of about 3 mV (SNR ± 100) (panels A, B). Electrical stimulation of the cell creates the socalled open-cell state shaping an electrical signal that displays the full time course of the action potential of the cardiac cell. The observed amplitudes range from 3 to 90 mV (SNR up to 300) (panels C, D). The open cell state is restricted in time and the recorded signal spontaneously returns to single spikes [1].

Fig. 1. The open-cell model. (A) Schematic depiction of a single cell (blue) on top of an electrode (grey). (B) Representative extracellular recording from a single cardiac cell. (C) Schematics of the cell upon electrical stimulation. (D) Representative recording of the cardiac electrical activity during the open-cell mode.

2 Dose-dependent drug-induced APD elongation In this study, we evaluated the potential of the imec MEA in detecting drug-induced changes in APD. An open-cell assay was developed in which a cell was stimulated twice and the APDs of both situations were subtracted, resulting in a APD value. The first stimulation for each cell always occurred in the absence of the drug, whereas the second stimulation was performed both in the absence and in the presence of increasing concentrations of cisapride, a drug known to prolong the APD. In the absence of the drug, APD amounted 1.9 ± 1.2 ms. With increasing concentrations of cisapride, i.e., 10, 100, and 1000 nM, APD

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increased to 33.5 ± 4.4, 53.6 ± 4.0 and 78.4 ± 5.6 ms, respectively (n = 30). Figure 2 shows the dosedependent relationship between APD and the drug concentration.


In conclusion, the imec CMOS-MEA shows to be a powerful tool for detailed analysis of the electrical activity of single cardiac cells and demonstrates potential to drastically improve predictability and accuracy of future drug screening assays. References [1]

Braeken D, Jans D, Huys R, Stassen A, Collaert N, Hoffman L, Eberle W, Peumans P, Callewaert G. (2012). Open-cell recording of action potentials using active electrode arrays. Lab Chip.7;12(21):4397-4402.

Fig. 2. Quantitative data of the effect of increasing concentrations of cisapride on APD. See text for explanation.


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Pharmacological Response of a Human Induced Pluripotent Stem Cell-Derived Neuron and Astrocyte Co-Culture Aoi Odawara1,3, Masao Gotoh1, Ikuro Suzuki2* 1 School of Bioscience and Biotechnology, Tokyo University of Technology, 1404-1Katakura, Hachioji, Tokyo 2 Department of Electronics and Intelligent Systems, Tohoku Institute of Technology, Japan 3 Japan Society for the Promotion of Science, Tokyo, Japan * Corresponding author. E-mail address: [email protected], [email protected]

Abstract Human induced pluripotent stem cell (hiPSC)-derived neurons may be effectively used for drug discovery and cellbased therapy. However, the immaturity of cultured human iPSC-derived neurons and the lack of established functional evaluation methods are problematic. We here used a multi-electrode array (MEA) system to investigate the effects of the co-culture of rat astrocytes with hiPSC-derived neurons. The co-culture facilitated the long-term culture of hiPSC-derived neurons for >3 months and long-term spontaneous firing activity was also observed. After >3 months of culture, we observed synapse transmission. Compared with rat neurons, hiPSC-derived neurons required longer time to mature functionally. Furthermore, addition of the synapse antagonists induced significant changes in the firing rate. In conclusion, we used a MEA system to demonstrate that the co-culture of hiPSCderived neurons with rat astrocytes is an effective method for studying the function of human neuronal cells, which could be used for drug screening.

1 Introduction Human induced pluripotent stem cells (hiPSCs) differentiation techniques and culture methods are important evaluation models and an alternative to animal testing for drug discovery screening, toxicity testing, and elucidating disease mechanisms [1-3]. Neuronal cells can be generated from hiPSCs, providing a very important alternative to studies of humans and model organisms, to facilitate a better understanding of the mechanisms of neurological diseases and identifying novel therapeutic avenues [4, 5]. However, hiPSCderived neurons cultured using traditional twodimensional culture methods lack adequate maturation, and evaluation methods focusing on the function of cultured hiPSC-derived neurons have not been established. Additionally, using glial cell humoral factor in cultured neuronal cells increases the number of living cells [6,7]. We previously demonstrated that achieving long-term single cell culture is possible by adding glial cell humoral factor to rat hippocampal neurons [8]. Here, we used the MEA system to investigate the functional effects of co-culturing rat astrocytes with hiPSC-derived neurons on their long-term spontaneous activity and drug responsiveness.[9]

2 Materials and Methods 2.1 Planal MEA chip To evaluate the long-term culture of hiPSCderived neurons, we used a planar MEA measurement system (Alpha Med Scientific, Japan). The MEA chips

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were produced on a glass slide, comprising 64 electrodes (50 × 50 m) with 150-m spacing in an 8 × 8 grid arrangement. Each recording terminal surface was coated with Pt/Pt-black to reduce impedance.

2.2 hiPSC-derived neurons hiPSC-derived neurons [iCell neurons; CDI Inc., USA] were cultured on MEA chips. The MEA chips were coated with 0.05% polyethylenimine solution for 1 h at room temperature, washed four times with sterilized water, and left to dry overnight. Next, 5 μg/ml of laminin solution was added, and the chips were incubated at 37°C in a 5% CO2 /95% air atmosphere. On the day that culture commenced, the laminin solution was removed and a φ3.4-mm glass ring was placed in the middle of the probes where the electrodes were located. The cells (90 μl) were seeded inside the rings (density, 1.3 × 106 cells/ml) and incubated for 1 h. Culture medium was applied around the rings and were removed carefully. The cultures were grown at 37°C in a 5% CO2/95% air atmosphere.

2.3 Humoral factor derived from rat astrocytes and rat astrocyte co-culture To evaluate the pharmacological response and long-term spontaneous activity characteristics, we tested iCell neurons in three different culture conditions: (i) conventional culture conditions using samples with neurobasal medium culture; (ii) conventional culture conditions with the addition of rat astrocytederived humoral factor; and (iii) with the addition of


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rat astrocyte-derived humoral factor and rat astrocytes. The rat astrocytes were seeded (density, 1 × 103 cells/MEA chip) and co-cultured with hiPSC-derived neurons. The humoral factor was obtained from the same culture medium used to produce the astrocytes.

2.4 Pharmacological tests To investigate pharmacological effects, we administered synaptic antagonists to hiPSC-derived neuronal networks on the MEA chips. The GABAA receptor antagonist bicuculline (Sigma-Aldrich) and the AMPA/kainate receptor antagonist 6-cyano-7nitroquinoxaline-2,3-dione (CNQX) (Sigma-Aldrich) were used at final concentrations of 10 μM and 30 μM in the medium, respectively. Spontaneous recordings were obtained for 60 min before treatment with bicuculline or CNQX and again after administration. A 10min washout was performed in the medium prior to recordings. The cultures were kept in a CO2 incubator between the recordings and washouts. To avoid medium changes and waiting for the effects of drugs, firing analyses were performed during the last 10 min of the 60 min period after drug administration.


culline administration, indicating that the cells were pharmacologically responsive and it also validated the long-term culture of the samples. After CNQX administration, we observed that the firing rate took a long period to recover to its original rate after washing the samples. The relationship between CNQX concentration and recovery time requires further investigation. In conclusion, we demonstrated that the coculture of hiPSC-derived neurons with rat astrocytes facilitated long-term culture and functional measurements were obtained. Moreover, we detected major responses after the addition of synapse antagonist drugs. Therefore, using a MED64 system as a measurement method during hiSPC co-culture with rat astrocytes may be beneficial for clarifying the functions of human neuronal circuits and for drug screening applications

3 Result We applied bicuculline and CNQX to the humoral factor/astrocytes co-cultured samples at 37 and 92 culture DIV, and we observed changes in the spontaneous activity. Figure 1 shows the change in the firing pattern. Bicuculline administration (10 μM) increased the firing frequency, whereas CNQX administration (30 μM) stopped the firing activity completely, although it was restored after washing the samples. Bicuculline administration increased the amplitude and the firing rate. We detected multiple different waveforms, which we attributed to an increase in the number of firing neurons. No significant differences were observed with the conventional culture samples (data not shown). Figure 1B shows the electrodes that detected signals and the firing frequency of the 64 electrodes. At 37 and 92 culture DIV, the number of electrodes that detected signals and the firing rate increased after bicuculline administration, whereas they decreased after CNQX administration, although the firing rate was restored after washing out the samples. After bicuculline administration, among the treated samples, we detected an increase in the number of electrodes with firing activity (four and 11 more electrodes at 37 and 92 culture DIV, respectively). The firing rate in treated samples increased 2.8- and 2.4-fold at 37 and 92 culture DIV, respectively (Fig. 1C). After CNQX administration, there was a 0.54- and 0.17-fold reduction in the firing rate at 37 and 92 culture DIV, respectively.

4 Summary

Figure 1. Effects of drugs in hiPSC-derived neurons with astrocyte co-culture Acknowledgement We thank Alpha Med Scientific company for collaborating in this research. This study was supported by the Japan Society for the Promotion of Science Grant Number 24700485 and by the Japan Science and Technology Agency Adaptable and Seamless Technology transfer Program (A-step) Grant Number AS251Z02961P. References [1]

[2] [3] [4] [5] [6]

[7] [8]

[9]

The firing rate increased greatly at 92 DIV, including the synchronized burst firing rate, after bicu-


K. Takahashi, et al., Induction of pluripotent stem cells from adult human fibroblasts by defined factors, Cell 131 (2007) 861-872. M. Bellin, et al., Induced pluripotent stem cells: the new patient?, Nat Rev Mol Cell Biol 13 (2012) 713-726. A.B. Cherry, et al., Reprogrammed cells for disease modeling and regenerative medicine, Annu Rev Med 64 (2013) 277-290. M. Peitz, et al., Disease-specific iPS cell models in neuroscience, Curr Mol Med 13 (2013) 832-841. Y. Imaizumi, et al., Modeling human neurological disorders with induced pluripotent stem cells, J Neurochem (2013). I. Suzuki, et al., Detection of tetanus-induced effects in linearly lined-up micropatterned neuronal networks: application of a multi-electrode array chip combined with agarose microstructures, BBRC 356 (2007) 470-475. T. Fath, et al., Primary support cultures of hippocampal and substantia nigra neurons, Nat Protoc 4 (2009) 78-85. H.W. Muller, et al., A neurotrophic factor (NTF) released from primary glial cultures supports survival and fiber outgrowth of cultured hippocampal neurons, J Neurosci Res 8 (1982) 195204. A.Odawara, et al., Long-term electrophysiological activity and pharmacological response in human induced pluripotent stem cell derived neuron and astrocyte co-culture, BBRC 443 (2014) 1176-1181.

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Human Embryonic and Induced Pluripotent Stem Cells Form Functional Neuronal Networks in MEA Ylä-Outinen Laura1,*, Hannuksela Eija1, Narkilahti Susanna1 1 NeuroGroup, BioMediTech, University of Tampere, Tampere, Finland * Corresponding author. E-mail address: [email protected]

Abstract Human pluripotent stem cell (hPSC) derived neuronal networks have large potential for creating human cell based in vitro platforms. Electrical signalling corresponding in vivo signalling in neuronal networks is one crucial parameter to evaluate the neuronality of the cells in networks. We show that both human embryonic stem cell (hESC) derived and human induced pluripotent stem cell (hiPSC) derived neuronal cells form functional networks on microelectrode array (MEA). Electrical activity development in hPSC derived neuronal networks show signal development from single spikes to bursting activity. Thus, those networks are potential cell material for MEA based in vitro platforms.

1 Backround / Aims Human pluripotent stem cells (hPSC), including human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs), provide unique cell source for developing in vitro neuronal cell models for neurotoxicity, neural drug development as well as neurologic diseases [1-3]. Nevertheless, only little is known about the functionality and network formation of hPSC derived neuronal cells. The aim of our work was to use microelectrode array (MEA) technology to observe the functional development of both hiPSC and hESC derived neuronal networks.

2 Methods / Statistics Both hiPSCs and hESCs were cultured and differentiated similarly. Shortly, hPSCs were cultured on top of feeder cell layer in colonies. Colonies were mechanically cut to aggregates and cultured as spheres in neuronal differentiation medium for 8 weeks. For MEA measurements, MEA dishes (60-6WellMEA 200/30, Multichannel Systems) with in-house designed silicone cell culture chamber (SpikeBooster, Fig. 1) [4] were used. Maturing neuronal networks were measured with 2100-MEA system (MCS) twice a week for 5 weeks. In addition, control networks were cultured in cell culture dish for gene expression and immunocytochemical analyses. Obtained MEA data was analysed using MATLAB (MathWorks, MA, USA) with in-house-built burst detection code [5] and with Neuroexplorer (NexTechnologies, AL, USA).

Fig. 1. SpikeBooster. Cell culture chamber designed for good cell attachment and fast network formation for 6-well MEA format.

3 Results We detected the functional development that is typical for neuronal cultures in both hESC and hiPSC derived neuronal networks. First, single spiking activity occurred within first week on MEA, and thereafter the functionality matured into training activity, and even bursting (Fig. 2).

Fig. 2. A) Electrical signalling development from hESC derived neuronal networks in 4 weeks on MEA. B) Electrical signalling development from hiPSC derived neuronal networks in 4 weeks on MEA.

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Moreover, both types of networks showed typical neuronal markers in gene expression and immunocytochemical analyses.


References [1]

[2]

[3]

[4]

[5]

Fig. 3. A) Immunocytochemical analysis showed MAP-2 and betatubulin positive neuronal network (green) and synaptophysin (red) in hESC derived neuronal networks. B) Immunocytochemical analysis showed MAP-2 and beta-tubulin positive neuronal network (green) and synaptophysin (red) in hiPSC derived neuronal networks.

Johnstone, AF., Gross, GW., Weiss, DG., Schroeder, OH., Gramowski, A., Shafer, TJ. (2010). Microelectrode arrays: a physiologically based neurotoxicity testing platform for the 21st century. Neurotoxicolog 31(4):331-50. Ylä-Outinen, L., Heikkilä, J., Skottman, H., Suuronen, R., Äänismaa, R., & Narkilahti, S. (2010). Human cell-based micro electrode array platform for studying neurotoxicity. Front Neuroeng, 3(pii: 111). Novellino, A., Scelfo, B., Palosaari, T., Price, A., Sobanski, T., Shafer, TJ., Johnstone, AF., Gross, GW., Gramowski, A., Schroeder, O., Jügelt, K., Chiappalone, M., Benfenati, F., Martinoia, S., Tedesco, MT., Defranchi, E., D'Angelo, P., Whelan, M. (2012). Development of micro-electrode array based tests for neurotoxicity: assessment of interlaboratory reproducibility with neuroactive chemicals. Front Neuroeng. 2011 Apr 27;4:4 Kreutzer, J., Ylä-Outinen, L., Kärnä, P., Kaarela, T., Mikkonen, J. E., Skottman, H., Narkilahti, S., Kallio, P. (2012). Structured PDMS Chambers for Enhanced Human Neuronal Cell Activity on MEA Platforms. Journal of Bionic Engineering, 9, 1–10. Kapucu, F. E., Tanskanen, J. M. A., Mikkonen, J. E., YläOutinen, L., Narkilahti, S., Hyttinen, J. A. K. (2012). Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics. Frontiers in Computational Neuroscience. doi:10.3389/fncom.2012.00038

4 Conclusion/Summary Neuronal cells differentiated from hESCs and hiPSCs form functional neuronal networks which further mature into even bursting networks. This shown functionality makes these cells potential starting material for different in vitro platforms. Acknowledgement We acknowledged the personnel of BioMediTech for support in stem cell culture and MEA technology. This work is funded by TEKES, Finnish Funding Agency for Technology and Innovations, Human Spare Part project.


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A Novel AFM-Based Technique for Measuring Coupled Phenomena in Cardiac Myocytes Caluori Guido1*, Andrade_Caicedo Henry 1,2, Tedesco Mariateresa1, Raiteri Roberto1 1 Department of Informatics Bioengineering and Systems Engineering, Università di Genova, via Opera Pia 11, 16145 Genova 2 Centro de Bioingeniería; Universidad Pontificia Bolivariana; Circular 1 No. 73-76, Bloque 22C, Medellín, Colombia * Corresponding author. E-mail address: [email protected]

Abstract Excitation-contraction coupling (ECC) is the phenomenon through which cardiac myocytes convert electrical action potentials, via calcium ions handling, to a mechanical contraction. In parallel to ECC, cardiac muscle cells possess a feedback mechanism, to overcome environmental changes, which is called mechano-electrical feedback (MEF). Alterations of these phenomena are directly linked to occurrence of contractile dysfunction (pump/heart failure) or ventricular arrhythmias. The main goal of this scientific work is to simultaneously monitor changes of different coupled phenomena in the cardiac mechano-electrical behavior at the subcellular level, combining different tools and experimental techniques: atomic force microscopy (AFM), microelectrode arrays (MEA), and fluorescence microscopy (calcium imaging).We also propose a novel method in which AFM nanoindentation is triggered with respect to the probed EFP with increasing delays at each electric event, thus providing an alternative, more powerful, approach in cardiomyocytes mechanical measurements.

1 Backround / Aims Heart failure is a major pathological condition, which is associated with high mortality. It is directly caused by myocardium contractile dysfunction (pump failure) or ventricular arrhythmias. The phenomenon through which an electrical action potential is converted, via calcium ions handling, to a mechanical contraction goes under the name of excitationcontraction coupling (ECC) [1]. When environmental changes occur, an intracardiac chemical pathway provides adaptation of the cardiac ECC. Such biological control loop is known as mechano-electrical feedback (MEF). Alteration of the MEF has been linked to the occurrence of arrhythmias [2]. In the past decade, the use of the atomic force microscope (AFM) as a tool to study micro and nanomechanical properties of single cells has spread. The use of force spectroscopy techniques allows precise probing of the cell surface and underlying cytoskeleton [3]. Furthermore, AFM can operate in liquid environment; hence single cells can be probed in physiological conditions [4]. Microelectrode arrays (MEAs) is a well-accepted tool that enables the recording and the stimulation of cellular electrical activity, by means of extracellular field potential (EFP). They offer few advantages compared to other electrophysiological techniques, such as multiple experimental sites and long-time monitoring of the electrical behavior. Common cell types addressed by this technology are neurons, cardiac and skeletal muscle fibers [5]. Fluorescence techniques are widely used in cardiac research. Calcium fluorescence imaging allows di-

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rect visualization of Ca2+ fluxes through the cell membrane using specific fluorescent probes. The main goal of this work is to simultaneously monitor changes of different components of the cardiac mechano-electric behavior at the subcellular level, namely elasticity, height, electrical activity, and intracellular calcium concentration. We also propose a novel method in which AFM nanoindentation is triggered with respect to the probed EFP with increasing delays at each electric event. This results in a series of time resolved measurements of the cell elasticity during the beating cycle [6], overcoming one of the main limitation of AFM measurement (e.g. low speed).

2 Methods / Statistics 2.1 Experimental setup The experimental setup is based on a commercial AFM (AFM 5500, Agilent Technologies), mounted onto an optical inverted microscope (model IX70, Olympus) equipped for EPI-fluorescence microscopy. The cell culture is performed on commercial MEAs (60channels MEA, model ITO 200/30, Multichannel Systems). A custom-made sample holder houses the MEA device and provides heating and multi-site probing of bioelectrical signals. The signals pass through an amplification stage, supplied by a 64 channels, 1000-gain amplifier (FA64I, Multichannel Systems). AFM scanner vertical position and cantilever deflection are also recorded. All signals are acquired by a 64 channels DAQ board (NI PCI-6071E, National Instruments).


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Calcium imaging is performed using fluorescent calcium green-emitting dyes (Oregon Green-BAPTAAM/Fluo4-AM, Invitrogen). The images are recorded with a CMOS high-speed, high-sensitivity camera (Flash4, Hamamatsu Photonics) To perform the triggered/delayed nanoindentation, a FPGA board (NI PCI-7830, National Instruments) is used. All the hardware is controlled by NILabVIEW customized softwares. The complete setup is schematically shown in Figure 1.

Fig. 2. Graphs of three signals recorded simultaneously during an experiment: MEA signal (top, blue), cantilever deflection (red, middle), intracellular calcium transient (bottom, green).

Fig. 1. Schematic representation of the combined setup for AFM-MEA-Calcium imaging measurements.

2.2 Experimental procedure Embrionyc rat cardiomyocytes (16-19d days), cultured on MEA for four to seven days, are loaded with calcium sensitive fluorophore one hour before the experiment. EFP recording from microelectrodes are carried out together with optical inspection of the cell culture. A specific cell, sitting onto one microelectrode, which shows spontaneous electrical and beating activity, is selected. Simultaneous electrical and fluorescence imagining, together with AFM triggered/delayed nanoindentation are recorded and saved for off-line analysis.

3 Results

4 Conclusion/Summary In vitro investigation of mechano-electrical behavior of cardiomyocytes can open new ways for detecting and predicting pathological conditions directly linked to the ECC alteration. The use of wellestablished techniques provides a solid support for the interpretation of data, and the exploitation of AFM sub-micrometric resolution allows us to treat the microelectrode array as a multisite sample which increases the throughput of experiments toward the validation of in vitro cardiac muscle cells models, and more efficient drug tests. References [1] [2]

[3]

3.1 Results analysis AFM “force-distance curves” are processed, according to the analysis proposed by Oliver and Pharr [7], to calculate elastic moduli at each delay from the last spontaneous extracellular AP peak measured at the targeted microelectrode [6]. The recorded EFP is also used to evaluate electrophysiology parameters (e.g. beating rate). Acquired fluorescence images are analyzed in terms of change of intensity, normalized with respect to the background (F/F0) in a selected region of the image corresponding to the cell area. All the analysis is performed with customized scripts inMATLAB (Mathworks). An example of the recorded signals is shown in Figure 2

[4]

[5]

[6]

[7]


D. M. Bers, “Excitation-contraction coupling,” Nature, vol. 415, no.January, 2002. “Cardiac mechano-electric feedback: past, present, and prospect,” Progress in Biophysics & Molecular Biology 82 (2003) 3–9, vol. 82, pp. 3–9, 2003. T. G. Kuznetsova, M. N. Starodubtseva, N. I. Yegorenkov, S. A. Chizhik, and R. I. Zhdanov, “Atomic force microscopy probing of cell elasticity,” Micron, vol. 38, pp. 824–833, 2007. D. Kirmizis and S. Logothetidis, “Atomic force microscopy probing in the measurement of cell mechanics,” International Jour nal of Nanomedicine, pp. 137–145, 2010. A. Stett, U. Egert, E. Guenther, F. Hofmann, T. Meyer, W. Nisch, and H. Haemmerle, “Biological application of microelectrode arrays in drug discovery and basic research.,” Analytical and bioanalytical chemistry, vol. 377, no. 3, pp. 486–95 J. F. S. Saenz Cogollo, M. Tedesco, S. Martinoia, and R. Raiteri, “A new integrated system combining atomic force microscopy and microelectrode array for measuring the mechanical properties of living cardiac myocytes,” Biomed Microdevices, vol. DOI 10.100, 2011. W.C. Oliver, G.M. Pharr, “An improved technique for determining hardness and elastic modulus using load and displacement sending indentation experiments”, Journal of materials research, 7(6), 1564–1582 (1992

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Author Index


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Author Index

Author Index Äänismaa R Achilli S Adagunodo MO Agrawal H Alagapan S Albrieux M Alfaro Sáez A Alhebshi A Alloisio S Andrade Caicedo HH Andrei A Anisimova M Arendt T Asboth L Asnaghi V Averna A Baca M Baekelandt V Baez C Bakkum D Ballini M Barrett J Barth B Bartsch H Bartz-Schmidt KU Battiato A Becker B Beckler M Béduer A Beggs J Berdondini L Bergonzo P Bernardi E Bernhard N Bertagnolli E Bertotti G Bestel R Bhat N Biffi E Bikbaev A Birbaumer N Bisio M Blanquie O

362

342 306 286 83 81, 337 295 200 127 124, 131 358 330 170 158 237 124 216 310 56 212 60, 147, 153, 160, 166, 176, 244, 335 244, 302, 326 64 33 310 45 293 76 129 278 328 100, 208 266, 291 293 312 133, 222, 322 247 194, 300 255 306 214 29, 30 208, 216 174

Blau A Bocos-Bintintan V Boedecker J Bogdan M Bonfiglio A Bonifazi P Bosca A Bouchiat V Boucsein C Bourrier A Boven KH Braeken D Bräunig P Brewer GJ Briancon-Marjolet A Brings F Brossard J Brown C Brugger D Bucher V Buehler S Bui-Göbbels K Bullmann T Burkhardt C Calderon N Callewaert G Caluori G Capurro A Carabelli V Carbone E Cesare P Channappa L Charvet I Chaudet L Chaudhary U Chee C Chen CC Chen Y Chew D Chiantore M Chiappalone M Cihova M Conte Granado T

181, 289, 316, 344 181 76 34 242 216 208 295 42 295 42, 137, 247 162, 330, 352 318 81, 83, 337 295 324 51 235 44 38 126 318 158 38, 272, 274, 280 116 352 358 212 284, 293 284, 293 287 41, 85 308 64 29 45 192 244, 326 50 124 208, 210, 216, 308 38 284


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Cools J Cosseddu P Cottance M Courtine G Dabrowski W Daus AW de la Paz F de Pasquale P de Weerd E Defranchi E Degen R Degenaar P Dehnke S Delacour C Delle L DeMarse T Demko L Dermutz H Desai H Destro-Filho JB Devesa I Di Marco S Difato F Dihné M Dijkstra J Dityatev A Divoux JL Dodel N Doerr L Dong N Dragas J Drews G Dürr C Eba I Egert U Eglen S Ehlis AC Ehnert C Eickenscheidt M El Hasni A Elsen G Engleder E Esir P Exler A Fallgatter AJ Famm K Fayuk D Fernández E

Author Index

352 242 291 233, 237 328 194 198 308 259 131 318 64 44 295 314 81, 337 341 341 83 284, 332 118 100 216, 316 178 188 114 266 247 129 64 185 137 68 50 76 187 33 135 41, 66, 78, 247 318 178 133 224 133 33 50 120, 164, 168 198, 200


Ferrández-Vicente J Ferrandiz Huertas C Ferreira Neves A Ferrer-Montiel A Ferrigno G Fertig N Fiscella M Flahaut E Fominov O Forró C Franca E Franke F Frech S Frega M Frey U Fukayama O Fukuda M Füll Y Gabor F Gähwiler B Gallacher D Gallegos-Ayala G Gao ZY Garnham C Garrido J Gekeler F George M Gharabaghi A Gielen GGE Giese M Gimsa J Girard H Giugliano M Giussani V Gladkov A Gohara K Golabchi A Goldental A Gong W Goo YS Gopal K Gosso S Gotoh M

198, 200 118 332 118 306 129 74, 98, 102, 335 278 314 341 81, 337 74, 98, 102, 153, 160, 185, 302, 335 122 339 88, 147, 153, 158, 160, 166, 244, 302 206 270 179 133 335 351 29 284 51 264 45 129 47 330 276 126 266 56, 202 124 226, 228 143, 170 181, 289, 316, 344 149 335 70 116 293 127, 354

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Author Index

Gross G Guenther E Guex A Guinot D Gunning DE Gutöhrlein K Habibey R Hahnewald S Han Z Hanein Y Hannuksela E Hébert C Heidingsfelder S Heikkilä J Heine M Helmhold F Hempel F Herrmann T Hess D Hess L Heuschkel M Hierlemann A

Hight A Hilgen G Hirsch A Hoffman L Holmes K Holzer M Homsy A Hoshino T Hoth M Hottowy P Husar P Huybrechts B Hyttinen J Hyung S Ingebrandt S Isomura T Ito D Ito H Jäckel D

364

109, 116, 156 137, 280, 351 235 129 328 274 181, 289, 316, 344 51 295 139 356 266, 291 251 168 214 66, 85 312, 314 54, 68 351 264 212, 278, 282, 291 60, 74, 88, 98, 102, 147, 153, 160, 166, 176, 185, 190, 244, 302, 326, 335 235 100 237 330, 352 109 158 51 206 251 328 320 352 204, 218 95, 103 251, 312, 314 183 143, 170 230 60, 153, 160, 185, 302, 335

Jackson TL Jans D Jedidi N Jiko H Jimbo Y Joki T Jones I Jones PD Jügelt K Jung H Just T Kalita D Kang DH Kanter I Kanzaki R Katso R Kazantsev V Kenney C Keppner H Kern D Kilb W Kim J Kleber C Knoflach F Knoll A Knott T Konduri A Koppenhöfer D Kotani K Kraushaar U Kriebel M Krippeit-Drews P Krumpholz K Kubon M Lacour S Lai S Laprell L Larmagnac A Lassen D Law J le Feber J Ledee D Lee D Lee WW Lekkala J Lenk K Leondopulos S Lepina A

45 352 282 270 91, 112, 172, 183 342 60, 74, 98 287 135 62 320 295 95, 103 149 147, 166 50 224, 226, 228 328 51 272, 274 174 70 280 190, 335 181 122 316, 344 312 91, 112, 172, 183 137, 280, 351 68 137 318 38 235, 237 242 58 233, 286 122 251, 312, 314 188, 259 116 235 70 268 204, 218 81, 83 226, 228


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Lerche H Lewandowska M Li W Link G Lissorgues G Litke A Liu Y Livi P Löffler H Lorente V Lottner M Lotufo C Lu HR Luhmann HJ Luthi-Carter R Mabuchi K Maccione A MacLaren R Mahmud M Mäkinen M Makohliso S Maljevic S Marconi E Mari J Marmari H Martina M Martinez V Martiniuc A Martinoia S Massobrio P Mathieson K Mathivanan S Maybeck V McLaughlin B Melchionna T Menegon A Mer C Micholt L Mika J Minev I Mistrik P Mita T Mitroshina E Möller A Moon HJ Mukhina I

Author Index

178, 179 176 72 38 291 328 179 244, 326 179 198, 200 264 332 351 174 212 206 100 45 202 120, 168 212, 282 178, 179 51 332 149 280 286 181 131, 196, 210, 220, 242, 339 196, 220, 339 328 118 72 50 51 306 266 162 133, 222, 322 237 51 147, 166 114 42, 247 95, 103 114, 224, 226, 228


Müller-Dahlhaus F Münch T Musienko P Musumeci G Mutter M Nam Y Nanni M Narkilahti S Neil M Nemeth J Neuhof A Nick C Nisch W Nissen M Novellino A Obien MEJ Odawara A Offenhäusser A Okawa S Okujeni S Olivero P Paavilainen T Pajunen M Pan L Park JH Pasquale V Pasquarelli A Passeraub P Pastore VP Patel S Pavlova N Pedrocchi A Peltola M Perrone S Pesce M Piazza S Picaud S Picollo F Pielka AM Pimashkin A Piret G Pirino V Podssun A

60, 74, 98, 102, 153, 244, 302, 326, 335 32 102 233 56 102 62 289, 316, 344 120, 164, 168, 218, 268, 342, 348, 356 64 45 318 300 38 126 124, 131 88 127, 354 72, 304, 324 166 76 293 164, 168 164, 168 81, 337 95, 103 208, 310, 216 284, 293, 332 308 196 50 237 306 268 212 339 196 291 293 135 114, 226, 228 291 220 135

365

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Author Index

Poli D Popov C Porzig T Postma W Premoli I Puers R Pulizzi R Qu B Radivojevic M Raiteri R Ramos-Murguialday A Rao Bhaskar R Rast G Rayan A Regalia G Reinhard K Reithmaier JP Renaud P Riccomagno E Riedmiller M Roccio M Roehler S Rosa F Roscic B Rosenstiel W Rousseau L Russell T Rutten W Ryynänen T Saalfrank D Sachs H Saito J Sakai K Sakharnova T Salonen P Samba R Sandoz A Santoro F SaseendranKumar S Sauter-Starace F Schaerlaeken S Scheel O Schnakenberg U Schneider K Schnitker J Schober A Scholze P

366

196 276 310 259 32 330 56, 202 251 60, 74, 153, 160, 244, 302, 335 358 29 255 347 214 306 102 276 235, 278 220 76 51 38, 272 179 335 34 291 74, 153, 160, 335 259 268 344 45 332 91, 172 114 342 38, 54 308 304, 324 76 266 352 122 318 274 72, 304, 324 310 222, 322

Schönecker S 137 Schreiter M 247 Schröder O 135 Schuller P 222 Schwarz C 44 Schwarz K 322 Scorsone E 291 Sejer Wismer M 264 Senn P 51 Sernagor E 64, 100, 187 Shadmani A 326 Sher A 328 Shimba K 91, 172 Shin HJ 95, 103 Shirakawa K 270 Sikdar SK 255 Simonov A 224 Singh K 255 Sinning A 174 Sitaram R 29 Sivasubramaniapandian M 268 Smith D 109, 156 Smith LS 187 Soekadar SR 29, 30 Spanu A 242 Spüler M 34 Sridhar A 50 Stamm B 274 Stassen A 352 Stauffer F 286 Stelzle M 38, 287 Stengl M 276 Stern M 300 Stett A 38, 247, 272, 274 Stieler J 158 Stingl K 45 Stoelzle-Feix S 129 Stoppini L 308 Streit J 51 Stubbe M 126 Stutzki H 66 Succol F 289 Suh JKF 95, 103 Sumser M 58 Sun X 64 Susloparova A 312, 314 Suzuki I 127, 270, 354 Suzuki M 170


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Takahashi H Tanskanen JMA Tateno T Tedesco M Thewes R Thielemann C Thomas U Tietz L Tillack B Tipney H Toimela T Trauner D Treutlein T Tscherter A Uchida T Ullmann S van den Haute C van Lier B van Stiphout P Vardi R Varshney A Vasilaki E Vedunova M Veliev F Velychko D Verma R Viswam V Voeroes J von Metzen R Vornanen I Voss A Vu XT Wallach G Walter A Wandinger KP Wanzenboeck H

Author Index

147, 160, 166 204 298 242, 339, 358 41, 78, 247 194, 300 129 218 247 50 342 58 158 51 170 304 56 190 122 149 278 202 114 295 78 235 60, 302 233, 286, 341 38 204 135, 276 251, 312, 314 139 34 178 133, 222, 322


Wei H Weinmann M Weir K Wenger N Weusthof M Wheeler B Widmer HR Williamson A Wimmer R Wirth M Witkowski M Wolansky D Wolf CJ Wolfrum B Wolking S Wong D Wrobel WG Wülfing J Yada Y Yakushenko A Ylä-Outinen L Ylikomi T Yokoyama K Yoshida L Young D Yvert B Zakharov Y Zanin M Zeck G Zhou X Ziemann U Zrenner E

276 38 174 233 259 81, 83, 337 51 310 76 133 30 247 118 324 178 45 43 76 206 324 120, 164, 218, 356 342 170 112 328 291 114 124, 131 41, 54, 66, 68, 78, 85, 247 251 32 45

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ISSN 2199-1596
