Brain stimulation during an afternoon nap boosts slow

NeuroImage 142 (2016) 311–323

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Brain stimulation during an afternoon nap boosts slow oscillatory activity and memory consolidation in older adults Julia Ladenbauer a,b,⁎, Nadine Külzow a,b, Sven Passmann a,b, Daria Antonenko a,b, Ulrike Grittner c, Sascha Tamm d, Agnes Flöel a,b,⁎ a

Department of Neurology, Charité Universitätsmedizin Berlin, 10117 Berlin, Germany NeuroCure Cluster of Excellence, Charité Universitätsmedizin Berlin, 10117 Berlin, Germany Biostatistics and Clinical Epidemiology, Charité Universitätsmedizin Berlin, 10117 Berlin, Germany d Department of Psychology, Freie Universität Berlin, 14195 Berlin, Germany b c

a r t i c l e

i n f o

Article history: Received 23 February 2016 Accepted 30 June 2016 Available online 2 July 2016 Keywords: Aging Declarative memory Sleep Sleep spindles Slow oscillations tDCS

a b s t r a c t Sleep-related consolidation of declarative memories, as well as associated neurophysiological events such as slow oscillatory and spindle activity, deteriorate in the course of aging. This process is accelerated in neurodegenerative disease. Transcranial slow oscillatory stimulation (so-tDCS) during sleep has been shown to enhance slow oscillatory brain activity and thereby improve memory consolidation in young subjects. Here, we investigated whether so-tDCS applied to older adults during an afternoon nap exerts similar effects. Eighteen older human subjects were assessed using visuo-spatial (picture memory, primary, and location memory) and verbal memory tasks before and after a 90-min nap either comprising weak so-tDCS at 0.75 Hz over fronto-central location or sham (no) stimulation in a within-subject design. Electroencephalographic activity was recorded throughout the naps and immediate effects of stimulation on brain activity were evaluated. Here, spectral power within three frequency bands of interest were computed, i.e., slow oscillatory activity, slow spindle and fast spindle activity; in 1-min stimulation-free intervals following 5 stimulation blocks. So-tDCS significantly increased frontal slow oscillatory activity as well as fast spindle activity, and significantly improved picture memory retention after sleep. Retention in the location memory subtask and in the verbal memory task was not affected. These findings may indicate a novel strategy to counteract cognitive decline in aging in a convenient manner during brief daytime naps. © 2016 Elsevier Inc. All rights reserved.

Introduction Sleep plays a pivotal role in memory consolidation (for review, see Rasch and Born, 2013). Even short naps of 1–2 h lead to considerable improvements of memory retention performance (Tucker et al., 2006; Van der Helm et al., 2011). There is convergent evidence that the beneficial effects of sleep on declarative memory are associated with characteristic field potential oscillations measured by electroencephalography during non-rapid eye movement (NREM) sleep, such as slow oscillations (SO, b1 Hz; Marshall et al., 2006; Mölle et al., 2009; Diekelmann

⁎ Corresponding authors at: Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany. E-mail addresses: [email protected] (J. Ladenbauer), [email protected] (N. Külzow), [email protected] (S. Passmann), [email protected] (D. Antonenko), [email protected] (U. Grittner), [email protected] (S. Tamm), [email protected] (A. Flöel).

http://dx.doi.org/10.1016/j.neuroimage.2016.06.057 1053-8119/© 2016 Elsevier Inc. All rights reserved.

and Born, 2010) and sleep spindles (8–15 Hz; Gais et al., 2002; Schabus et al., 2004; Tamminen et al., 2011). According to the active system consolidation hypothesis (Born and Wilhelm, 2012; Diekelmann and Born, 2010), newly acquired declarative memories are initially encoded into hippocampal networks during wakefulness. During subsequent sleep, these memory traces are repeatedly activated, accompanied by hippocampal sharp-wave ripples, and gradually transferred to neocortical sites for long-term storage. According to the model, SOs play an important role in this process. They stimulate the redistribution of hippocampal memories toward neocortical sites by temporally grouping spindles with hippocampal sharp-wave ripples (Buzsáki, 1998; Mölle et al., 2002; Steriade, 2006). Previous studies revealed that slow spindles (8–12 Hz) are strongest in the frontal region and fast spindles (12–15 Hz) are mainly distributed over the central and parietal regions (Mölle et al., 2011). Both types of spindles further differ in their circadian and homeostatic regulation, their age-related changes (Doran, 2003), as well as their pharmacological properties (Ayoub et al., 2013). Fast spindles, as compared to slow spindles, have been

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J. Ladenbauer et al. / NeuroImage 142 (2016) 311–323

more consistently linked to memory consolidation effects (Rasch and Born, 2013). A study that specifically induced an increase in endogenous slow oscillation activity by transcranial application of slow oscillatory stimulation (so-tDCS) during NREM sleep improved declarative memory consolidation after a full night of sleep in healthy young subjects (Marshall et al., 2004, 2006). This type of stimulation within the frequency range of slow oscillatory activity (0.7–0.8 Hz) applied during early nocturnal sleep increased frontal SO as well as frontal slow spindle activity and in parallel enhanced the overnight retention of word-pair memories. In the course of aging, sleep quality, including SO and spindle activity, declines, with associated deterioration in declarative memory consolidation (Backhaus et al., 2007; Mander et al., 2013, 2014). This process is accelerated in Alzheimer's disease (AD, Wang et al., 2011) and its precursor mild cognitive impairment (Westerberg et al., 2012). Therefore, findings of improved sleep parameters by means of so-tDCS might open novel strategies to counteract age-associated detrimental changes. However, previous studies on so-tDCS effects in older adults have yielded inconsistent findings. One study in older adults using so-tDCS during nocturnal sleep could not show an improvement of memory-relevant sleep parameters and overnight retention performance on a word-pair association task (Eggert et al., 2013). Here, small but possibly important differences in stimulation protocol may have prevented beneficial effects. For example, Eggert et al. included a current ramping at the beginning and end of each stimulation interval, which might have prevented entrainment of slow oscillatory activity by the stimulation. Differences in the applied current (0.331 mA/cm2 vs. 0.517 mA/cm2) and the type of Ag–AgCl electrodes (non-sintered vs. sintered) as compared to Marshall et al. (2006) may have also affected the stimulation outcome. In addition, their stimulation protocol did not account for changes in sleep characteristics of older adults, such as higher sleep fragmentation. This might be a crucial issue, since so-tDCS effects seem to critically depend on ongoing brain state (Kanai et al., 2008; Kirov et al., 2009; Marshall et al., 2011). A recent study on so-tDCS during an afternoon nap in older subjects, however, was able to demonstrate improvement in word-pair recall, and increase in SO activity (Westerberg et al., 2015). However, this study failed to enhance memory for associative fact-face information, possibly due to differences in underlying cognitive processes (forced-choice vs. yes–no recognition) and associated brain structures. Note also that a forcedchoice recognition task, in which the familiarity system is more strongly involved than recollection processes, was employed here (Westerberg et al., 2006), and it is known that the contribution of the hippocampus to familiarity-based decisions is limited (Sauvage et al., 2007; Yonelinas et al., 2005). Therefore, the face-fact recognition task might not have been sensitive to so-tDCS-induced effects on sleep physiology. Given the mixed findings so far, and a focus primarily on verbal information (Barham et al., 2016), additional research is needed to explore the efficacy of so-tDCS in relation to sleep and memory consolidation especially in older adults. Thus, the present study examined the impact of so-tDCS on memory-relevant sleep parameters, similar to previous studies, and for the first time investigated so-tDCS effects on consolidation of memories in a visuo-spatial memory task combing visual (primary) with more complex (location) memory in older adults. These functions are important in everyday life (Bishop et al., 2010; Flöel et al., 2012) and known to decline with advancing age (Hedden and Gabrieli, 2004). Visual recognition memory, in particular, is impaired early in the course of AD (Barbeau et al., 2004) and might thus be a promising target for interventional approaches. Specifically, we hypothesized that (i) so-tDCS during napping benefits the offline retention performance of visual declarative memory (picture recognition) and that (ii) so-tDCS enhances memory-relevant sleep parameters such as endogenous slow oscillatory and spindle activity relative to sham condition. For comparison with previous studies (Eggert et al., 2013; Westerberg et al., 2015), consolidation of wordpair recall was also assessed, in addition to a procedural control task

(Eggert et al., 2013; Göder et al., 2016; Marshall et al., 2006, 2011; Prehn-Kristensen et al., 2014). Here, we hypothesized that location and verbal memory would likewise benefit from so-tDCS during the nap. No modulation, however, was expected for procedural memory, given the absence of so-tDCS effects for procedural memory in previous studies (Marshall et al., 2006, 2011). Important from a practical point of view, it might be sufficient to apply so-tDCS during a daytime nap (Antonenko et al., 2013; Del Felice et al., 2015; Westerberg et al., 2015), thus avoiding the more inconvenient setup of nocturnal stimulation (i.e., stressful due to testing in the late evening, and unfamiliar environment during nocturnal sleep). Hence, in the present study, stimulation was applied during an afternoon nap. Sleep was monitored following each stimulation block and stimulation was only applied during NREM sleep stage 2, 3, and 4. Materials and methods Participants Healthy older adults aged between 50 and 80 years were recruited via a local database of the Charité University Hospital Berlin, Germany, and 54 underwent a structured telephone interview to clarify major exclusion criteria, including history of severe untreated medical, neurological, and psychiatric diseases; subjective cognitive decline; sleep disorders; cognitive impairment; intake of medication acting primarily on the central nervous system (e.g., antipsychotics, antidepressants, benzodiazepines, or any type of over-the-counter sleep-inducing drugs like valerian); daily consumption of N 50 g of alcohol or N10 cigarettes; and not native German speaking. During baseline visits, eligible subjects underwent a medical and neuropsychological screening comprising magnetic resonance imaging (MRI) of the brain for neuroradiological evaluation (exclusion if brain tumor or previous stroke was detected) and cognitive screening with the Mini-Mental State Examination (exclusion if scores b27 points; Folstein et al., 1975) and the Consortium to establish a Registry for Alzheimer's Disease (CERAD-Plus; www.memoryclinic.ch; exclusion if verbal recall below 1.5 SD of age/education norms). Moreover, psychiatric comorbidity was monitored by Beck's Depression Inventory-II (BDI-II, exclusion if BDI-scores ≥ 13; Kuehner et al., 2007) and State Trait Anxiety Inventory (STAI- X 1, exclusion if STAI-X 1 score ≥ 40; Spielberger et al., 1970). Out of 33 participants that had entered the main study including baseline visits and up to three nap sessions, 2 participants had to be excluded due to elevated depression scores (BDI-scores ≥13), 12 subjects due to insufficient sleep (when less than three so-tDCS /sham intervals were possible; which corresponds to ~22 min spent in sleep stage 2 or SWS), and one subject due to severe EEG artifacts, leaving 18 subjects (10 female, mean age 65 ± 1) for final analysis (see Table 1 for baseline characteristics of included subjects). The excluded subjects did not differ in baseline parameters from the 18 subjects except for SEX (χ2(1,33) = 4.33; p = 0.037). More male compared to female participants were excluded. Baseline assessments Participants underwent comprehensive neuropsychological testing for assessment of general cognitive status comprising memory performance (German version of Auditory Verbal Learning Test (AVLT; Helmstaedter et al., 2001), working memory (Wechsler, 1997), executive functions (Stroop color-word test; Van der Elst et al., 2006), Trail Making test (TMT) part A and B (Tombaugh, 2004), and attention (AKT; Gatterer, 2008). The affective state at the time of the testing was assessed using the Positive and Negative Affect Schedule (PANAS; Watson et al., 1988). For baseline characteristics, see Table 1. In addition, subjective and objective sleep habits were assessed using the Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 2015), Epworth Sleepiness Scale (ESS; Johns, 1991), the German version of

J. Ladenbauer et al. / NeuroImage 142 (2016) 311–323 Table 1 Baseline characteristics. n (female / male)

18 (10 / 8)

Age (years) Education (years) Beck's Depression Index (score) Mini-Mental State Examination TMT, part A (time to complete, in sec) TMT, part B (time to complete, in sec) Stroop color-word test (delay of incongruent vs. neutral condition, in sec) Verbal fluency, phonematic (no. of words) Verbal fluency, categories (no. of words) Digit span, forward Digit span, backward AKT

65 ± 1 (57–77) 15.6 ± 0.6 (11−22) 4.1 ± 1.0 (0−12) 29.5 ± 0.2 (28–30) 41.8 ± 3.0 (25–69) 81.9 ± 7.9 (51–192)a 34.5 ± 3.0 (18–60) 16.2 ± 1.0 (7–24) 27.2 ± 1.4 (12–36)a 7.3 ± 0.5 (4–11)a 6.6 ± 0.4 (4–10) 19.2 ± 0.2 (18–20)

Data are given as mean ± SEM and range (min–max). In the Mini-Mental State Examination scores between 27 and 30 indicate normal cognition. In all other neuropsychological tests scores worse than 1.5 SDs below age-corrected and education norms (cutoff score) were defined impairment in these tests. TMT, Trail Making Test; AKT, Alters– Konzentrations Test (Geriatric Concentration Test). a In these tests, one subject scored below cutoff. No subject scored below cutoff in more than one of these tests.

Morningness-Eveningness-Questionnaire (d-MEQ, Griefahn et al., 2001), and the Essen Questionnaire on Age and Sleepiness (“Essener Fragebogen Alter- und Schläfrigkeit”; EFAS, Frohnhofen et al., 2010). Daily sleep diaries and actigraphy (GT3X, ActiGraph, Pensacola, FL, USA) were used to monitor habitual bedtimes and wake times 7 days prior to the experimental nap sessions. The experimental protocol was approved by the ethics committee of the Charité University Hospital Berlin, Germany, and was conducted in accordance with the declaration of Helsinki. All subjects received a small reimbursement and gave written informed consent prior to participation. Study design Following an adaptation nap to familiarize participants with the experimental setup (including short versions of each memory task and electrode setup; maximal interval to first stimulation nap 1 week), they were tested in a balanced cross-over design in two conditions, a stimulation and a sham condition (n = 9 participants received so-tDCS/n = 9 received sham on first experimental nap) that were separated by an interval of at least 2 weeks to prevent carry-over effects.

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All three afternoon naps (adaptation and two experimental nap sessions) took place at the sleep laboratory of the Free University Berlin, Germany. Subjects arrived at the laboratory at 11:30 h. Following preparation for sleep monitoring (electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG) recording), subjects performed on tasks of declarative memory (verbal paired-associate and visuo-spatial learning including picture and location memory) and procedural memory (finger sequence tapping). For each experimental session, different (parallel) versions of the memory tasks were used. Then, following a standardized small meal and preparation for so-tDCS, subjects took a nap (at about 14:00 h; 30 min after the end of the memory tests) under polysomnograhic registration and were asked to attempt to sleep for during a period of 90 min. About 30 min after awakening, retrieval of memories was examined. To control for potential confounding influences in mood, alertness, and sleepiness, the PANAS scales (Watson et al., 1988), the Stanford Sleepiness Scale (SSS) for assessing subjective sleepiness (Hoddes et al., 1973), and the Visual Analog Scale (VAS) of sleepiness/tension were administered before encoding and before retrieval, respectively. Additionally, attentional capabilities were assessed using the Test of Attentional Performance (TAP; Zimmermann and Fimm, 1995) before learning and retrieval following the nap. See Fig. 1 for the experimental procedure in more detail. Slow oscillatory stimulation (so-tDCS) Stimulation parameters employed in the present study closely resembled those from Marshall et al. (2006). Anodal current was applied by a battery-driven stimulator (DC-Stimulator; NeuroConn, Ilmenau, Germany) split into bilateral frontal electrodes at sites F3 and F4 of the international 10–20 system (electrodes were 8 mm in diameter and mounted into an EASY cap (Falk Minow Services, Munich, Germany)) with reference electrodes placed at each mastoid (ipsilateral; likewise 8 mm in diameter). The induced current oscillated sinusoidally between zero and 260 μ A at a frequency of 0.75 Hz (no current ramping), resulting in a maximum current density of 0.522 mA/cm2. The electrode resistance was always kept below 2 kΩ. Stimulation started 4 min after the subject had entered stable NREM sleep stage 2 (no transitions back to stage 1 sleep or wakefulness) and was applied five times, each as a 5-min block of stimulation, separated by stimulation-free inter-block intervals of 1 min 40 s. The marker for the beginning of each 1-min stimulation-free interval (for analyses) was always set manually after 40 s to exclude the strong and long-

Fig. 1. Study design. Subjects learned a verbal, a visuo-spatial (blue squares; declarative) and a procedural task (gray squares; control test) in the indicated order following psychometric control tests. During a subsequent 90-min nap (14:00 h to 15:30 h) either so-tDCS or sham stimulation was applied (within-subject design, randomized order). Retrieval and psychometric control tests were tested in the same order following the nap. Red bars indicate 5-min blocks of so-tDCS/SHAM; with each bar placed below the respective sleep stage in the example hypnogram. Note that stimulation blocks started 4 min after sleep stage 2 onset, discontinued in this example as the subject moves into sleep stage 1, and resumed after subjects again enters sleep stage 2 (or lower). REM, rapid eye movement sleep (vertical black bar in the hypnogram); S1–S4, sleep stages 1–4.

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lasting stimulation-induced drifts visible in our unfiltered online EEG signal from the analysis (interval of analysis will be referred to hereafter as “1-min stimulation-free interval”). Contrary to Marshall et al. (2006), the number of stimulation blocks and the duration of the stimulationfree intervals depended on the individual subject's sleep, as sleep was monitored following each stimulation block and each stimulation block was only initiated during NREM sleep stage 2 or slow wave sleep (SWS, stages 3 and 4 NREM; in accordance with the protocol used by Antonenko et al., 2013). This procedure was implemented to account for higher sleep fragmentation in older adults. In the sham session, stimulation electrodes were placed identical to the stimulation session, but the tDCS device remained off. The same criteria as for the so-tDCS condition were applied for the sham condition (first sham block started 4 min after sleep stage 2; and sleep stage 2 or SWS was required for subsequent sham blocks). Participants were blinded with regard to stimulation condition throughout the study. After completing all study-related procedures, they were asked whether they felt any sensations, and if they could guess in which experimental nap the stimulation had been applied. Sleep monitoring and pre-processing EEG data (ground: fronto-central (FCz); impedance b 5 kΩ, reference: nose tip, bandpass: 0.05–127 Hz, 500 Hz digitization rate) were acquired from 26 scalp sites (Fp1, Fp2, AFz, F7, Fz, F8, FC5, FC1, FC2, FC6, C3, Cz, C4, T7, T8, CP5, CP1, CP2, CP6, P7, P3, Pz, P4, P8, O1, O2) placed according to the extended 10–20 international EEG system using the BrainAmp amplifier system (Brain Products GmbH, Munich, Germany) and sintered Ag–AgCl ring electrodes mounted into the EASY cap. Additionally, EMG at the chin and horizontal and vertical EOG were recorded according to standard sleep monitoring. All recordings were stored for later offline analyses. EEG processing and analysis EEG analyses were conducted using BrainVision Analyzer (Version 2.0, Brain Products, Munich, Germany). A combined semi-automated (to reject stimulation-related artefacts) and visual rejection of raw data was applied to eliminate epochs contaminated by artefacts. Apart from a 50 Hz notch filter, no additional filters were applied prior to power spectral analysis. Offline sleep stage scoring Following EEG downsampling to 250 Hz, 30-s epochs were scored manually according to Rechtschaffen and Kales (1968) by means of Schlafaus software (Steffen Gais, Lübeck, Germany) in sleep stages 1, 2, 3, 4, and REM sleep, epochs of wakefulness or movement artifacts. Subsequently, time and proportion spent in different sleep stages were determined. During sleep stage scoring, the rater was not blinded to the stimulation condition, given that the stimulation was seen on EEG. Epochs during so-tDCS were not scored due to strong electrical artifacts from so-tDCS in the EEG signal. Likewise, corresponding epochs in the sham session were not scored, to obtain comparable time and proportions of sleep stages. Moreover, sleep during the period after the last stimulation block to the end of the nap was scored for both conditions. Scoring for the 1-min stimulation-free intervals (between the 5-min blocks of acute stimulation) was additionally performed for 10-s epochs. Spectral power analysis In a second analysis, the short-term effects of so-tDCS on EEG power during stimulation-free intervals following so-tDCS (or sham stimulation) were examined. Three to five intervals, depending on the number

of actually performed stimulations or sham stimulations in a subject, were selected for the analysis. If participants failed to sleep long and deep enough for at least three stimulation or sham stimulation intervals, they were excluded from the analysis. EEG power was calculated by means of Fast Fourier Transform (FFT). Up to 11 overlapping (by 5 s) artifact-free segments each lasting 10 s were used for every stimulationfree interval (segments per subject: mean ± SEM = 46.4 ± 1.8). Corresponding intervals were used for the sham session (segments per subject: 46.8 ± 2.3). On each of these 10 s segments of EEG data, a Hanning window (100%) was applied before calculating the power spectra using FFT (frequency resolution, 0.06104 Hz). Mean power in the following EEG bands was calculated for each 1-min stimulationfree interval: SO activity (0.5–1 Hz), slow spindle activity (8–12 Hz) and fast spindle activity (12–15 Hz). Frequency bands and topographic regions of interest (ROI) were selected on the basis of previous research (Klinzing et al., 2016; Mander et al., 2014; Marshall et al., 2006). According to the topographies of SO (frontal), slow spindle activity (frontal), and fast spindle activity (frontal and centro-parietal), the electrodes FC1, Fz, FC2 and CP1, Cz, CP2 were pooled into ROIs, respectively. An additional analysis considered the effect of so-tDCS on frontal power in the neighboring delta band (1–4 Hz), as Marshall et al. (2006) found a slight but not significant increase for this frequency range. Spindle density In addition to EEG spindle power, spindle density (mean spindle count per 30-s epoch) was calculated during the 1-min stimulationfree intervals for frontal slow and frontal and centro-parietal fast spindles. Spindle detection was performed in Matlab (R2012b, Mathworks, Natick, MA, USA) by means of an algorithm adopted from previous studies (Gais et al., 2002; Marshall et al., 2006). Following the EEG signal filtering in the corresponding frequency bands (8–12 Hz, 12–15 Hz), the root mean square (RMS) of each 100 ms interval was calculated and the times the RMS power exceeded a detection threshold of the 10 μV for 0.5–3 s were counted. Because discrete spindles in the EEG can be measured best on midline electrode positions (Gais et al., 2002), analyses focused on results from frontal (Fz), central (Cz), and parietal (Pz) electrodes of the midline. Memory tasks All memory tasks were programmed using Presentation software (Neurobehavioral Systems, Version 14.8) and parallel versions were used for all tasks in the two experimental nap sessions. One declarative memory test was a visuo-spatial task, modeled after the task used by Alger et al. (2010) in a nap study, whereas the other declarative memory task was a modified version of the word-pair task used in Marshall et al. (2004, 2006). Consolidation for both tasks was previously shown to be sleep dependent (Alger et al., 2010; Marshall et al., 2004, 2006; Plihal and Born, 1997). In the visuo-spatial and verbal memory tasks subjects were instructed to memorize items for a later recall, but no specific strategy was recommended. There was no overlap regarding the stimuli used in the visuo-spatial and the verbal memory tasks. Processing of the behavioral data was fully automatized by using scripts. Visuo-spatial memory task The visuo-spatial memory task required subjects to encode 38 neutral pictures (objects, plants, scenes taken from the Affective Picture System (IAPS, Lang et al., 2008; MULTIMOST, Schneider et al., 2008); picture memory) and additionally to memorize the location at which they were presented (location memory) (see Fig. 2). Pictures appeared randomly at one of the four possible quadrants on the screen for 2 s with an inter-stimulus interval of 1 s. To account for primacy and recency effects four filler pictures (two at the beginning and two at the end) were


Fig. 2. Visuo-spatial memory task. (a) Example encoding trial. During each trial, a fixation was first presented, followed by a gray square randomly at one of the four possible quadrants on the screen. This was followed by a neutral picture within the square for 2 s. Subjects were instructed to memorize both the picture and the location at which they were presented. (b) Example recognition trial, presenting a studied picture. Following a fixation, a picture (studied or unstudied) was displayed in the center of the screen for 3 s. Within this time period subjects were asked to indicate (by button press) whether they believed that they had seen the picture earlier (picture memory). If subjects recognized an item, they also indicated in which quadrant they believed the item had been presented (location memory).

added. During immediate and delayed recognition testing stimuli (38 studied and 38 new pictures) were randomly displayed in the center of the screen for 3 s. Within this time period, subjects had to provide “Old/New”-decision to indicate (by button press) whether or not the respective picture occurred during learning (“Old”), and only in case of an “Old”-decision, a subsequent retrieval of the picture's location was required. All responses (correct “Old” responses to studied items (hits), correct “New” responses to unstudied items (correct rejections), incorrect “Old” responses to unstudied items (false alarms), and incorrect “New” responses to studied items (misses)) were recorded and stored for offline analyses of performance. As a measure of picture recognition memory (accuracy), percent correct responses (PC) were determined for each participant as follows: proportion of hits + proportion of correct rejections. Potential response biases were monitored by calculating the sum of the proportion of hits and false alarms. To determine the accuracy of location memory, both correctly and incorrectly retrieved picture locations were taken into account as follows: Number of correctly retrieved locations/ number of hits − number of falsely retrieved locations/number of hits. No performance feedback was provided for any part of the visuo-spatial memory task. Data from one subject were excluded from analyses of this task due to a very high proportion of missing responses (62%). Verbal memory task Forty semantically related German word pairs (category-instance pairs: e.g. fruit–banana) were used to assess declarative verbal memory (plus four buffer word pairs to prevent primacy–recency effects). During encoding, each word pair appeared centrally on the screen for 5 s with an inter-stimulus interval of 100 ms. Following encoding, an initial cued-recall test was performed. The category name (cue) was presented and subjects had to provide (verbally) the respective stimulus (instance) word after indicating (by button press within 10 s) whether they remembered the respective instance word. Subsequently, the correct word pair was shown for 2.5 s. This initial cued-recall test provided an additional learning opportunity, to help reach about 60% correct responses at the subsequent cued-recall test. Thereafter, a cued-recall test before sleep (baseline) and a cued-recall test after sleep were administered (without additional presentation of correct word pairs). In each encoding and recall trial, word pairs were

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presented in a different randomized order to prevent serial learning. Subject's verbal responses were recorded and cued-recall performance was obtained by the proportion of the correct retrieved targets in the recall tests before and after the nap, respectively. Moreover, we tested the impact of stimulation on interference rate (proportion of incorrect instance words in a nap session that were correct responses to the same categories in a previous session). Our verbal memory task was similar to that used in Marshall et al. (2006) and Westerberg et al. (2015). However, given the significant fraction of emotional words used in Marshall et al. (2006); 12.5%, 27 words total, e.g., “death,” “pain,” or “celebration”) according to affective norms (Warriner et al., 2013), more neutral word lists were designed for the present study. These contain only 5 (4.2%) mildly positively rated words (“animal,” “book,” “family,” “plant,” and “toy”). In addition, we employed a pre-screening that included ratings for emotionality, vividness, concreteness and arousal (unpublished pilot ratings (N = 30)). Furthermore, to achieve high comparability between word-pair lists for the two experimental naps, we (i) ensured equal relation type between cue and target (by category-instance pair design), (ii) used the same category words for both lists, and (iii) matched the two sets of instance words for word length, number of syllables, word frequency (frequency of word occurrence in daily speech; Goldhahn et al., 2012), emotional valence, arousal, vividness, concreteness (pilot ratings), and association strength (subjective measure). The association strength for the applied word pairs was assessed using a published norm (Nelson et al., 2004), showing a mean cue-to-target strength value of 0.08 (applicable to 29% of the pairs), compared to a value of 0.04 in Westerberg et al., 2015 (similar applicability fraction). Procedural task Procedural memory was investigated by a sequential finger tapping task (SFTT; adapted from Walker et al., 2002). Subjects were required to repeatedly tap a five-digit sequence (e.g. 4-2-3-1-4) presented on the screen with the non-dominant left hand as accurately and as quickly as possible within a 30-s interval (=trial). During learning before the nap, subjects performed on twelve trials separated by 30-s breaks, whereas retrieval testing contained four trials. Performance at learning and retrieval testing was determined by averaged scores (correctly tapped sequences) from the final three trials, respectively. In addition, reaction times were assessed during the respective learning and retrieval trials. Data from one subject were excluded from analyses for this task since he failed to follow instructions. Statistical analyses Sleep stages T-tests (or Wilcoxon signed-ranks tests if indicated) were performed for differences between stimulation conditions in sleep time (in %) spent in different sleep stages during the entire nap, during the stimulation-free intervals, and during the period after the last stimulation block to the end of the nap. Spectral power We calculated linear mixed models (LMM; Verbeke and Molenberghs, 2000) separately for the five outcome measures (i.e., frontal SO activity, frontal delta activity, frontal slow spindle as well as frontal, and centroparietal fast spindle activity), where the five time points of the 1-min stimulation-free intervals following each stimulation block (factor TIME) were level-one units nested in subjects (level-two units). Random intercept models tested differences between the two stimulation conditions (so-tDCS, sham), while allowing variation of subjectspecific intercepts. This model was chosen due to evidence for traitlike individual differences in sleep physiology (Buckelmüller et al., 2006; Tucker et al., 2007). The model further assumed that slopes were similar (no random slope model), since there was no previous

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evidence on inter-individual or group-specific differences in stimulation effects (slopes) on sleep physiology. Additionally, we included a squared centered time variable (TIME2) to test if there was a curvilinear course of slow oscillatory and spindle activity. The interaction TIME × STIMULATION assessed whether the slopes of the curves differed between the stimulation conditions and an interaction term of TIME2 × STIMULATION was included to test whether the shape of the curves differed between the stimulation conditions. To adjust for baseline differences in each frequency band, a baseline variable (BASELINE: 1 min preceding the first stimulation/sham block) was included as covariate, respectively. Spindle density The same random intercept models used for spectral analyses were calculated for spindle density; separately for frontal slow spindles over Fz and Cz, and for frontal and centro-parietal fast spindles over Fz, Cz, and Pz, respectively. Behavioral data Differences in memory scores were tested by repeated-measures analyses of variance (rmANOVA), including the within-subject factors “STIMULATION” (so-tDCS vs. sham stimulation) and “TIME” (before vs. after nap) and a Greenhouse–Geisser correction for degrees of freedom was used, if indicated. Moreover, associations between slow oscillation and spindle power measures with memory performance were assessed by calculating Pearson's correlations between these measures within each stimulation condition (so-tDCS and sham). All statistical analyses were conducted using SPSS version 19.0 (SPSS, Inc., Chicago, IL, USA). A two-sided significance level of α = 0.05 was considered. Given multiple testing for primary parameters of interest (picture memory, 4 memory-related EEG frequency bands, i.e., frontal slow oscillatory power, frontal slow spindle power, frontal fast spindle and parietal fast spindle power), we used the Benjamini–Hochberg correction (controlling the false discovery rate at 0.05) (Benjamini and Hochberg, 1995) to manage Type I errors while maintaining statistical power (Glickman et al., 2014). All other tests and comparisons were related to secondary hypotheses and p-values should be interpreted in a framework of exploratory analysis. Hence, because of small sample size and proof-of-concept nature of the study no adjustment for multiple testing was applied. Data are expressed as mean ± standard error of the mean (SEM), unless indicated otherwise.

39.71) as indicated by a main effect of TIME for reaction times in the picture memory task (F(1,16) = 60.92, p b 0.001). Location memory. No impact of so-tDCS during the nap was found for location memory (F(1,16) = 0.70, p = 0.414), but a main effect of TIME reached significance indicating a general decline of accuracy in spatial memory following the nap (before in %: 20.67 ± 9.52, after in %: 9.62 ± 9.68; F(1,16) = 13.27, p = 0.002). Verbal memory task A rmANOVA on cued-recall performance (in percent) in the wordpaired-associate learning task revealed no significant difference between so-tDCS compared to sham condition (F(1,17) = 0.78, p = 0.844). Similarly, error rates for the word-pair task showed no stimulation-dependent modifications, but a significant TIME effect with increased error rates following the nap independent of stimulation condition (before: 9.17 ± 1.57, after: 12.78 ± 2.10; F(1,17) = 7.06, p = 0.013) emerged. An additional rmANOVA on interference rate (proportion of errors caused by the same category word; ability to discriminate) revealed no stimulation effect (TIME × STIMULATION interaction: F(1,17) = 0.27, p = 0.614). Importantly, interference rate before the nap (baseline) did not differ significantly between the first and second nap session (incorrect instance words in the first nap session that were correct responses to the same category in the adaptation nap vs. incorrect instance words in the second nap session that were correct responses to the same category in the first nap session; mean ± SEM: 0.83 ± 0.35 vs. 1.67 ± 0.50; t(17) = −1.56, p = 0.138). Procedural task Performance on the finger sequence tapping task, as indicated by the count of correctly tapped sequences, did not significantly change dependent on stimulation condition during the nap (F(1,16) = 0.19, p = 0.667). However, a main effect of TIME was found (F(1,16) = 5.80, p = 0.028) indicating an improvement on the procedural task following napping independent of stimulation condition (before: 28.03 ± 3.29 after: 32.82 ± 3.71). To summarize, on the behavioral level so-tDCS significantly improved the recognition performance of neutral pictures, but showed no effect on retrieval performance of locations, retention of word pairs and on the performance in the procedural task (Fig. 3). Sleep analyses

Results Behavioral data Visuo-spatial memory task Picture memory. Overall retention performance of recognized pictures (primary outcome), as indicated by a significant TIME X STIMULATION effect in PC scores, was improved after the nap with so-tDCS compared to sham stimulation during the nap (so-tDCS: 1.78 ± 1.00, sham: − 0.70 ± 0.87; F(1,16) = 7.79, p = 0.013; see also Fig. 3 for pre- and post-nap scores). The stimulation effect remained significant when controlling for sleepiness (covariate) in the respective ANCOVA (F(1,15) = 4.98, p = 0.044). Baseline retention performance of percent correct responses before napping did not differ between so-tDCS and sham session (t(16) = − 0.06, p = 0.950). Further control analysis revealed no significant differences with regard to response tendencies between so-tDCS (mean and SD) compared to sham (mean and SD) condition (F(1,16) b 0.01, p = 0.845; mean decision criterion C ~ 0 at all testings), number of incorrect responses at retest (i.e. false alarms plus misses; F(1,16) = 0.62, p = 0.443), and reaction time to correct responses in picture memory (F(1,16) = 0.49, p = 0.508). However, participants tended to react generally faster on correct responses after the nap independent of stimulation condition (before: 1519.75 ± 49.26 vs. after: 1365.73 ±

Sleep stages T-test for paired observations (or Wilcoxon matched-pairs signedranks test if indicated) revealed no significant differences in total sleep time, number of stimulation, times spent in the different sleep stages, and sleep efficacy (see Supplementary Table 1). No significant differences were observed for the period after the last stimulation block to the end of the nap in sleep staging between so-tDCS and sham condition (all p N 0.09). Likewise, no stimulation effects were found on sleep stages in the stimulation-free intervals following each stimulation block (see Supplementary Table 1). Spectral power analysis Frontal slow oscillatory activity. Analysis of the stimulation-free intervals following the 5-min stimulation blocks (sham stimulation vs. so-tDCS) revealed a significant effect of STIMULATION on power within the slow oscillation band during the nap (β = − 0.18; SE = 0.08, t(146) = − 2.21, p = 0.029) indicating higher SO power compared to sham condition, following the second, third, and fourth so-tDCS block (see Fig. 4). The effect of TIME itself was not significant (β = 0.04; SE = 0.03, t(140) = 1.47, p = 0.145) suggesting that power in slow oscillatory activity did not significantly change in the course of the nap independent of


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Fig. 3. Retention performance in declarative memory tasks in the so-tDCS versus sham condition. (a) Recognition performance (percent correct: proportion of hits and correct rejections) in the picture memory subtask (left), and (b) cued-recall performance (in %) in the verbal memory tasks (right) for so-tDCS and sham stimulation depicted for pre- (white bars) and post-nap (black bars). c) Retention performances are expressed as difference (in %) between post-nap and pre-nap memory performance (long-delay–short-delay), reflecting a measure of memory consolidation. A significant stimulation effect emerged for picture memory, with higher picture recognition performance following so-tDCS compared (dashed bars) to sham condition (white bars) (p = 0.013). No stimulation effect was evident for verbal memory consolidation as well as location memory. Data are expressed as means ± SEM. ⁎p b 0.05.

stimulation condition. Similarly, the TIME × STIMULATION interaction was not significant (β b −0.01; SE = 0.04, t(140) = −0.18, p = 0.986) which shows that the slope of change in slow oscillatory activity is not significantly different for the stimulation conditions (sham vs. so-tDCS). Further, BASELINE values showed no significant effect (β = 0.05; SE = 0.12, t(134) = 0.39, p = 0.694) indicating that SO activity before so-tDCS or sham stimulation did not predict SO activity following the stimulation blocks. However, the effect of TIME2 was significant and negative indicating an inverted U-shape of change in slow oscillatory activity in the course of the nap independent of stimulation condition (β = −0.60; SE = 0.03, t(138) = −2.79, p = 0.006). Further, the TIME2 × STIMULATION STIMULATION interaction revealed a trend (β = − 0.05; SE = 0.03, t(138) = −1.77, p = 0.080), due to the fact that the curve in the stimulation session was (trend wise) steeper than the curve in the sham session. Frontal delta activity. Analysis of the adjacent delta activity at frontal sites showed no significant effect of STIMULATION (β = −0.12; SE = 0.08, t(147) = −1.59, p = 0.115). Only a significant effect of BASELINE (β = 0.54; SE = 0.16, t(49) = 3.31, p = 0.002) was observed for the

delta frequency indicating that the pre-stimulation delta power predicted the power in the same frequency band during intervals following the five so-tDCS/sham blocks. All other terms were not significant (all p's N 0.05). Spindle activity. LMM analyses for slow spindle activity revealed no significant changes between so-tDCS and sham session (see Table 2 for an overview of the results on sleep spindle measures). Only a significant effect of BASELINE (β = 0.57; SE = 0.10, t(27) = 5.56, p b 0.001) indicates that the pre-stimulation power in the frequency band of slow spindle activity at frontal electrode sites significantly predicted the power in the same frequency band during intervals following the five stimulation blocks (so-tDCS: 0.29 ± 0.15, sham: 0.22 ± 0.13). Analysis of fast spindle activity revealed a significant effect of STIMULATION at frontal (β = − 0.15; SE = 0.05, t(137) = − 2. 98, p = 0.003) and a clear trend at centro-parietal electrode sites (β = −0.09; SE = 0.05, t(133) = − 1.95, p = 0.054), with higher EEG power in the fast spindle frequency range in the so-tDCS compared to sham condition.

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Fig. 4. so-tDCS enhances EEG power within the slow oscillation band and fast spindle frequency range. a) EEG topographic plots of frontal slow oscillatory activity (0.5–1 Hz) and frontal as well as centro-parietal fast spindle activity (12–15 Hz) averaged over all 1-min stimulation-free intervals for so-tDCS and sham condition. ROIs (frontal: Fz, FC1, FC2; centro-parietal: Cz, CP1, CP2) are indicated by the dashed squares in the frontal and posterior head views, respectively. b) Time course of EEG power in the five stimulation-free intervals for frontal slow oscillations (first on the left), frontal delta activity (second on the left), frontal fast spindle (third on the left), and centro-parietal fast spindle activity (right). Asterisks indicate significant differences in power between so-tDCS and sham condition. Note that frontal slow oscillatory was significantly enhanced under so-tDCS versus sham condition (following the second, third, fourth sotDCS block). Likewise, a significant stimulation impact was found for frontal fast spindle power (following second, third, fourth, fifth so-tDCS block). A clear trend for fast spindle activity over centro-parietal derivations under so-tDCS compared to sham condition was observed, but failed to reach significance (p = 0.054). No significant stimulation effects were detected for frontal delta activity and frontal slow spindle activity. Log10 power values with regression lines ± SEM for so-tDCS (red) and sham condition (gray) are indicated. *p ≤ 0.05.

In order to test whether changes induced in sleep physiology during the sleep period after stimulation to the end of the nap accounted for memory improvements following so-tDCS, we additionally analyzed these sleep periods with respect to the EEG frequency bands of interest in so-tDCS versus sham condition. No significant stimulation effects were found on this later sleep period of the nap (all p's N 0.3). Spindle density Similarly to spectral analyses, LMM analyses on slow spindle density (counts per 30-s epoch) over frontal and central location revealed no significant stimulation effects (all p's N 0.1, see Table 2). Confirming the results for fast spindle power, LMM analyses of discrete fast spindles revealed significantly higher spindle counts during 1-min stimulation-

free intervals following so-tDCS compared to sham blocks over frontal and centro-parietal locations (Fz: β = − 0.99; SE = 0.46, t(144) = −2.16, p = 0.033; Pz: β = −1.78; SE = 0.51, t(150) = −3.51, p = 0.001, see Fig. 5). In sum, so-tDCS enhanced EEG power in the slow oscillation and fast spindle frequency band, while slow spindle power and sleep stages remained unaffected. Fast spindle density measures confirmed sotDCS effects on spectral power results in the fast spindle frequency range. Correlations between sleep parameter and memory retention performances No correlations were found for the visuo-spatial (picture and location memory), verbal, and procedural task with slow oscillatory activity

Table 2 LMM results for spindle power and spindle density measures during 1-min stimulation-free intervals. Significant effects (p ≤ .05) appear in bold print.

Spectral power Slow spindle Fast spindle

Spindle density Slow spindle Fast spindle

Measures N

STIM β (SE)

p

TIME β (SE)

p

BASELINE β (SE)

Frontal Frontal Centro-parietal

163 165 165

−0.02 (.05) −0.15 (.05) −0.09 (.05)

.626 .003 .054

−0.02 (.02) −0.03 (.02) −0.01 (.02)

.266 .067 .627

0.57 (.10) 0.16 (.10) 0.37 (.08)

b.001 .118 b.001

Fz Cz Fz Cz Pz

164 164 164 164 164

0.19 (.60) −0.72 (.61) −0.99 (.46) −0.70 (.43) −1.78 (.51)

.747 .238 .033 .107 .001

−0.34 (.19) −0.38 (.19) 0.07 (.15) −0.11 (.14) 0.01 (.16)

.075 .050 .636 .430 .954

0.32 (.12) 0.67 (.11) 0.07 (.11) 0.12 (.09) −0.07 (.11)

.010 b.001 .508 .157 .516

p

Results are depicted for the main factors STIMULATION, TIME, and BASELINE on spindle power (ROIs; frontal: Fz, FC1, FC2; centro-parietal: Cz, CP1, CP2) and spindle density measures (midline electrodes). Note, while no stimulation effect was detected for slow spindles, positive stimulation effects were evident for fast spindle measures consistently over frontal locations (spindle power and density measures), and in case of spindle density, over parietal electrode site. Spectral power measures were log10-transformed before subjected to analysis; β = regression coefficients; SE = standard error.


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Discussion This interventional study showed that so-tDCS during a daytime nap in older adults boosted memory-relevant sleep parameters, including endogenous frontal slow oscillatory and frontal as well as parietal fast spindle activity, and improved the consolidation of picture memory in a visuo-spatial task. Verbal and spatial memory consolidation, however, were not affected by so-tDCS. Impact of stimulation on sleep physiology and memory consolidation

Fig. 5. so-tDCS enhances frontal and parietal fast spindle density. Time course of fast spindle density (counts per 30-s epoch) in the five stimulation-free intervals for so-tDCS and sham condition at Fz and Pz. Spindle density values with regression lines ± SEM for so-tDCS (red) and sham condition (gray) are indicated. Fast spindle density is significantly enhanced for so-tDCS compared to sham condition at both electrode positions. No differences between stimulation conditions were found for frontal slow spindle density measures at electrode positions Fz and Cz. *p ≤ 0.05.

during stimulation or sham condition. Likewise, spindle activity in the slow and fast range frequencies did not show any significant associations with memory retention performances in the visuo-spatial, verbal, or procedural task (all p's N 0.1).

Blinding, attention, mood, alertness, and sleepiness Subjects tolerated the stimulation well and did not report sensations after sleep except one participant who reported a tingling after waking up from the nap. Asked about a guess on the nap of real stimulation, most of them answered “do not know” (n = 13), 3 subject suggested the real stimulation in the first (1 was correct) and 2 in the second (1 was correct) nap. Thus, subjective reports did not indicate that the stimulation was perceived during the nap. All participants were given a 90-min sleep opportunity. At the end of these 90 min, not all participants were still asleep. Participants were slightly more often awake in the sham compared to so-tDCS sessions at the end of the 90 min (n = 10 vs. n = 7); however, this difference did not reach significance (McNemar-test, p = 0.375). No differences were seen between stimulation conditions regarding the number of so-tDCS/sham blocks participants received (Supplementary Table 1 provides detailed information regarding the number of so-tDCS/sham blocks). Furthermore, time periods elapsed between stimulations blocks in the so-tDCS vs. sham condition were similar (Friedman's test, p = 0.468). No significant stimulation-dependent changes on mood, subjective sleepiness (Stanford Sleepiness Scale), tension, or attention were noted (all p N 0.1). Only the measurement of the Visual Analog Scale for sleepiness revealed a significant stimulation effect (F(1,17) = 5.98, p = 0.026). Post hoc t-tests revealed that participants felt sleepier after nap with so-tDCS compared to sham stimulation (differences after nap minus before nap; − 1.52 ± 0.50 vs. –0.45 ± 0.31). One possible explanation for this finding could be that because slightly more participants were awake in the sham compared to so-tDCS session at the end of the 90 min (n = 10 vs. n = 7 participants), they might have already been better recovered from drowsiness at the time of questionnaire administration. Also note that absolute differences in sleepiness between stimulation conditions were small.

Mechanisms underlying this consolidation enhancement may include impact of stimulation on endogenous sleep-related brain activity, in particular on slow oscillatory activity (see e.g. Marshall and Binder, 2013). Neocortical slow oscillations play a crucial role in the redistribution of memory representations to neocortical networks for long-term storage (Sirota and Buzsáki, 2005). The depolarizing slow oscillation up-states were previously shown to drive and synchronize thalamocortical fast spindles with hippocampal sharp-wave ripples (Steriade, 2006), thereby supporting effective hippocampus-to-neocortex transfer (Mölle and Born, 2011). Here, we found an increase in the EEG power within the frontal slow oscillation band (0.5–1 Hz), paralleled by a gain in fast spindle activity (in power and density measures, 12–15 Hz) at frontal and parietal derivations following so-tDCS. This finding is consistent with studies indicating a link between slow oscillation and classic fast spindle activity (Klinzing et al., 2016; Mölle et al., 2002, 2011; Steriade and Timofeev, 2003). The functional relevance of so-tDCS-enhanced fast spindles along with SO activity for memory consolidation is further underlined by reports on plastic synaptic processes associated with sleep spindles nested in SOs (Mölle and Born, 2011; Rosanova and Ulrich, 2005). In addition, functional significance of stage 2 fast spindle activity was also previously demonstrated for declarative memory retention performance (Ruch et al., 2012; Schabus et al., 2004). Nevertheless, previous studies indicated rather conflicting results with respect to so-tDCS impact on spindle activity. While Marshall et al. (2006) demonstrated a conjunct increase in frontal slow spindle activity (8–12 Hz) with SO power, comparable stimulations during a nap in young subjects failed to show any effect on sleep spindles (Antonenko et al., 2013). A recent study on so-tDCS during a nap in older subjects showed a decrease in fast spindles over parietal location (Westerberg et al., 2015). One, albeit speculative, explanation for the discrepancies in stimulation effects between young adults (Antonenko et al., 2013; Marshall et al., 2006) and the present findings in older adults could relate to an interaction of age and stimulation timing, given that the two types of spindle frequencies differ in their circadian regulation and show differential sensitivity to the factor age (Gennaro and Ferrara, 2003). The divergent findings between the present study and the Westerberg et al. study, both conducted in older adults and both acquired during a nap, may be due to differences in positioning of the stimulation electrodes, with more fronto-temporal locations in the Westerberg et al. study versus more fronto-central locations in the present study. In conclusion, further research is needed to clarify the impact of so-tDCS during sleep on the two kinds of spindles, which are suggested to reflect independent mechanisms contributing to memory consolidation (Mölle et al., 2011). Regarding stimulation effects on sleep stages, no enhancing effect was evident, but rather a tendency of less sleep stage 3 during so-tDCS condition. However, note that sleep stage 3 only comprised a very limited percentage of total sleep (1.4% in so-tDCS and 3.3% in sham session; ≤1% for sleep stage 4 in both conditions), so SWS alone will probably not be sensitive to any stimulation-induced changes. Moreover, sleep stage measures only provide a very rough classification of sleep, especially in older adults given the decrease in slow oscillation amplitude (Bliwise, 1993). Spectral power measures offer a more sensitive and objective analysis of slow oscillatory activity. Thus, our data suggest that

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so-tDCS enhanced SO power within sleep stage 2; and these effects contributed to enhanced memory consolidation. These results are in line with previous findings showing enhanced slow oscillations and spindles promoting memory consolidation not only during SWS, but also during sleep stage 2 (Ruch et al., 2012). Alternative mechanisms underlying the consolidation enhancement in picture memory, however, cannot be completely ruled out, since there was no correlation between sleep parameters and memory performance. It is conceivable that tDCS effects in general (frequencyindependent) boosted memory via neuroplastic effects (e.g., enhanced excitability), although previous findings are not in line with this hypothesis. First, the specific frequency of the oscillating current (0.7–0.8 Hz) was previously shown to be crucial for the memoryimproving effect of the stimulation, as a 5 Hz-oscillating current during SWS impaired consolidation of declarative memories (Marshall et al., 2011). Similarly, disrupting SO power by cross-hemispheric alternating current (0.75 Hz) during SWS has shown to be associated with a decrease in memory consolidation (Garside et al., 2014). In addition, the morphology of the tDCS pulse might likewise be important, given the fact that oscillating square wave tDCS during SWS failed to enhance declarative memory consolidation (Sahlem et al., 2015). Thus, although our experimental design does not allow us to definitely rule out alternative tDCS mechanisms responsible for improved memory consolidation, the above referred evidence consistently underlines the importance of SO activity induced by so-tDCS. For declarative memory consolidation, we here for the first time showed improvements in consolidation of visuo-spatial memories, specifically, picture memory, induced by so-tDCS in older adults during a nap. Given the high relevance of such memories for everyday life (Bishop et al., 2010; Flöel et al., 2012), and their frequent decline with advancing age (Hedden and Gabrieli, 2004), our findings may constitute a first step in developing therapeutic approaches for patients with decline in memory functions. We combined a rather simple picture memory task with a more complex task of location memory in which visual and spatial information have to be bind to form coherent memories. While consolidation of picture memory benefitted from so-tDCS, location memory was not significantly modulated by so-tDCS versus sham. These differential findings are probably not due to the nature of the task, but may be due to the low number of valid items for this subtask, given that participants were only asked to retrieve the location of an item if they reported to recognize it as “old”. However, note that the results are in line with a recent meta-analysis (Barham et al., 2016) that found positive so-tDCS effects primarily for consolidation of relatively simple information. Thus, sleep-dependent stimulation effects on consolidation of more complex tasks remains to be explored in more detail in future studies. To compare it to previous studies, we also assessed verbal declarative memory and found that consolidation of verbal memory was not affected by so-tDCS. Note though that we modified the verbal memory task from the version employed by Marshall et al. (2006) in several aspects. Although both tasks used a paired-associate learning approach with semantically related German nouns, we generated new word lists containing non-emotional category–instance pairs. Emotional stimuli (especially emotionally negative ones) as compared to neutral stimuli lead to stronger consolidation effects (Payne and Kensinger, 2010). Thus, a higher fraction of emotional words in Marshall et al. (2006) might have amplified so-tDCS effects in their word-pair task. As an alternative explanation, stronger semantic associations in our word-pair task might have prevented a beneficial effect of stimulation. While we aimed at enhancing comparability between word-pair lists in the two experimental sessions by the category-instance framework, these word pairs exhibited stronger semantic associations in comparison with previously employed word-pair lists (Marshall et al., 2006; Westerberg et al., 2015). Previous research, however, indicates higher sleep-dependent benefits for weak associations (Drosopoulos et al., 2007), leading to the conclusion that the verbal memory task employed

in our study might have been less sensitive to sleep modulations induced by so-tDCS. Another recent study in older adults employing so-tDCS during nocturnal sleep failed to show improved overnight retention performance (word-pair task) and modulations in sleep oscillations (Eggert et al., 2013). Reasons for the conflicting results may be attributable to some differences in stimulation protocol, as Eggert and colleagues introduced a current ramping at the beginning and end of each stimulation interval. This modulation may have precluded short-lasting stimulationdependent entrainments of the slow oscillatory activity. In the present study, we additionally controlled for the sleep phase preceding every stimulation interval. Since previous findings underline the strong dependence of oscillatory stimulation effects on ongoing network activity and brain state (Kirov et al., 2009; Marshall et al., 2011), and older adults exhibit more fragmented sleep (Bliwise et al., 2009), it is conceivable that stimulation intervals in Eggert et al. were sometimes applied during inappropriate times (e. g., sleep stage 1 or wake phases) and therefore failed to induce the intended effect on sleep physiology in that study. Overall, the present study provides evidence that so-tDCS applied during a daytime nap modulates memory-relevant sleep physiology and consolidation of visual declarative memory in older adults. However, stimulation did not alter declarative verbal and location memory consolidation, possibly due to the specific features of the tasks employed here. As hypothesized, procedural memory was not modulated by so-tDCS in the present study. This finding is consistent with previous studies applying electrical oscillating stimulation during sleep (Eggert et al., 2013; Göder et al., 2016; Marshall et al., 2006, 2011; Prehn-Kristensen et al., 2014), even though several findings relate sleep-dependent consolidation of procedural memories to sleep, stage 2 as well as sleep spindles (Laventure et al., 2016; Nishida and Walker, 2007; Walker et al., 2002), and more recent studies also indicate an involvement of SWS in procedural memory consolidation (Holz et al., 2012; Huber et al., 2004; Landsness et al., 2009). However, electrical stimulation during sleep seems to be inefficient to modulate procedural memory (see also meta-analysis Barham et al., 2016), possibly due to dissociable mechanisms that underlie declarative and procedural memory consolidation. Declarative memory systems are known to depend strongly on the hippocampus (Eichenbaum, 2000), whereas procedural memory relies on the cortico-striatal network (Doyon et al., 2003; Doyon and Benali, 2005, but see also Keele et al., 2003; Schendan et al., 2003). Implications of sleep modulation for cognitive decline and neurodegenerative disease Increasing evidence supports the relationship between age-related disruptions in sleep physiology and cognitive decline in aging (Backhaus et al., 2007; Mander et al., 2013, 2015), a process accelerated in mild cognitive impairment (MCI) due to AD (Westerberg et al., 2012). First data from animal models and patients indicated that sleep-related deficits in AD might not just reflect underlying circuit malfunction, but could also play a direct role in the progression of the disorder (Wang et al., 2011). Insomnia in adults represents a significant risk factor for AD, and sleep disturbances are an early component of AD (Wang et al., 2011). Animal models demonstrated that the Aβ content of the cortex is under the influence of the sleep-wake cycle (Kang et al., 2009), and imposing sleep reduced Aβ burden and associated amyloid-precursor protein-dependent synaptic abnormalities (Kang et al., 2009). These preclinical data suggest that sleep disturbances could exacerbate the pathophysiological process leading to neurodegeneration. Conversely, optimization of sleep by slow wave enhancement may inhibit aggregation of toxic amyloid-related proteins (Kang et al., 2009; Xie et al., 2013). Accordingly, so-tDCS applied during a daytime nap may not only delay decline in declarative memory, but may even tackle a


fundamental process of aging and AD-pathology by improving sleep physiology.

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Conflict of interest None declared.

Limitations Some limitations should be noted when interpreting these findings. First, the sample size was relatively small. However, subjects were well-characterized at baseline and tested in a within-subject design, rendering confounding factors between subjects unlikely to account for the current findings. Moreover, cohort size analyzed for the present trial was comparable to previous reports (Marshall et al., 2006; Westerberg et al., 2015). Second, no significant association was found between SO power and retention performance of picture memories, even though they were conjunctly enhanced by so-tDCS. This may be attributable to statistical power issues given the relatively small sample size. Another possibility is that consolidation improvements are not associated in a simple linear fashion with SO power. The impact on behavior might be driven by complex interaction effects with spindle activity and not only SO power. Third, high drop-out rate due to insufficient sleep, as seen in the present study, may limit the clinical applicability of this approach. However, this problem may be attenuated by extending the duration of a session as well as customizing the timing of the nap in a clinical context. Moreover, additional adaptation naps and/ or repeated stimulation sessions might help participants to become more familiar with the rather artificial situation in the sleep laboratory. Fourth, our results indicate that participants felt sleepier after napping with so-tDCS compared to napping with sham stimulation, possibly due to a tendency toward more S3 during sham sessions (in the later period of the nap). Despite lower percentage of S3 during so-tDCS sessions, SO power was stronger in closer proximity to the task, accounting for improved consolidation. However, please note that the limited percentage of SWS during the naps does not allow for definitive conclusions. Conclusions Encouragingly, we here found significant increases induced by single-session so-tDCS in both sleep parameters and memory consolidation in older adults, a group known to show deteriorations in both SO activity (Carrier et al., 2011; Mander et al., 2013) and declarative memory consolidation (Backhaus et al., 2007). However, no significant so-tDCS effects were evident for declarative verbal and location memory in the present study. Nevertheless, in conjunction with previous reports on beneficial so-tDCS effects on verbal memory consolidation in older adults during a nap (Westerberg et al., 2012), so-tDCS warrants further exploitation as a potential treatment for cognitive decline in aging. Advantages of so-tDCS include its non-invasiveness and excellent safety profile (Flöel, 2014). Moreover, frequency of daytime naps increases with age (Humm, 2001), rendering so-tDCS during naps in older adults a feasible approach from a practical point of view. Future studies should now evaluate whether repeated stimulation sessions reveal stronger and sustained effects on sleep parameters, memory consolidation, and possibly even cerebral beta-amyloid levels, in healthy adults and patients with incipient neurodegenerative disease. Moreover, including cross-frequency phase-amplitude coupling analyses in future studies may help to further unravel so-tDCS modulating effects with regard to functional coupling between SOs and spindles (Niknazar et al., 2015). Subsequently, closed-loop systems that automatically detect sleep phases and apply so-tDCS accordingly can be developed to automatize slow wave enhancement during naps (Ngo et al., 2013). Author contributions NK, DA, SP, and AF designed the study; JS conducted the experiment; JS, NK, ST analyzed data; and JS, UG performed statistical analysis; JS, NK, and AF wrote the paper. All authors read and approved the final manuscript.

Acknowledgments This work was supported by grants from the Deutsche Forschungsgemeinschaft (Fl 379-8/1, Fl 379-10/1; Fl 379-11/1, and DFG-Exc 257); and the Bundesministerium für Bildung und Forschung (FKZ 01EO0801, 01GQ1424A, 01GQ1420B). We are grateful to Rebecca de Boor and Kim Hasemann for help with data acquisition, and Steffen Gais for providing us with the Schlafaus software. We thank Josef Ladenbauer for fruitful discussions and helpful contributions to the manuscript.

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