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Brain Struct Funct DOI 10.1007/s00429-014-0898-2

ORIGINAL ARTICLE

Prolonged fasting impairs neural reactivity to visual stimulation N. Kohn • A. Wassenberg • T. Toygar • T. Kellermann • C. Weidenfeld • M. Berthold-Losleben • N. Chechko • S. Orfanos • S. Vocke • Z. G. Laoutidis F. Schneider • W. Karges • U. Habel



Received: 23 May 2014 / Accepted: 19 September 2014 Ó Springer-Verlag Berlin Heidelberg 2014

Abstract Previous literature has shown that hypoglycemia influences the intensity of the BOLD signal. A similar but smaller effect may also be elicited by low normal blood glucose levels in healthy individuals. This may not only confound the BOLD signal measured in fMRI, but also more generally interact with cognitive processing, and thus indirectly influence fMRI results. Here we show in a placebo-controlled, crossover, double-blind study on 40 healthy subjects, that overnight fasting and low normal levels of glucose contrasted to an activated, elevated N. Kohn (&) Institute of Neuroscience and Medicine (INM-6), Ju¨lich Research Centre, 52425 Ju¨lich, Germany e-mail: [email protected] N. Kohn  A. Wassenberg  T. Kellermann  C. Weidenfeld  M. Berthold-Losleben  N. Chechko  S. Orfanos  S. Vocke  F. Schneider  U. Habel JARA Brain, Translational Brain Medicine, Julich, Aachen, Germany A. Wassenberg  T. Toygar  T. Kellermann  C. Weidenfeld  M. Berthold-Losleben  N. Chechko  S. Orfanos  S. Vocke  Z. G. Laoutidis  F. Schneider  U. Habel Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany T. Toygar Department of Biology, RWTH Aachen University, 52074 Aachen, Germany Z. G. Laoutidis Department of Psychiatry and Psychotherapy, University of Du¨sseldorf, Bergische Landstrasse 2, 40629 Du¨sseldorf, Germany W. Karges Division of Endocrinology and Diabetes, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany

glucose condition have an impact on brain activation during basal visual stimulation. Additionally, functional connectivity of the visual cortex shows a strengthened association with higher-order attention-related brain areas in an elevated blood glucose condition compared to the fasting condition. In a fasting state visual brain areas show stronger coupling to the inferior temporal gyrus. Results demonstrate that prolonged overnight fasting leads to a diminished BOLD signal in higher-order occipital processing areas when compared to an elevated blood glucose condition. Additionally, functional connectivity patterns underscore the modulatory influence of fasting on visual brain networks. Patterns of brain activation and functional connectivity associated with a broad range of attentional processes are affected by maturation and aging and associated with psychiatric disease and intoxication. Thus, we conclude that prolonged fasting may decrease fMRI design sensitivity in any task involving attentional processes when fasting status or blood glucose is not controlled. Keywords Blood glucose  fMRI  Checker board  Arousal  Adrenaline

Introduction Many people report inability to concentrate or impaired attention when becoming hungry. Indeed prolonged fasting has been found to be associated with impaired cognitive functions (Green et al. 1997). Hunger is triggered by a decrease in blood glucose levels (BGL) (Southgate 1995). Pathologically low blood glucose (hypoglycemia) has a broad impact on cognitive functioning. Cognitive impairments may arise at below 55 mg/dl in healthy adults (Warren and Frier 2005). Generally, complex tasks are

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Author's Personal copy more strongly impaired by hypoglycemia than simple tasks. Most prone to disruptions by hypoglycemia are memory performance, attention and visual or auditory processing, this effect may even persist after BGL have risen again. Very low levels or persisting hypoglycemia may even lead to brain damage (Warren and Frier 2005). Working memory performance seems to already be influenced after prolonged fasting as observed in dieters (Green et al. 1997). The exact nature of these impairments has not been fully clarified, adequate brain functioning seems to be ensured since the brain’s glucose proliferation is very well regulated and is probably only disrupted by clinically relevant hypoglycemia or long-lasting hypoglycemic states (Paulson et al. 2010). Neuronally, there are data that imply a strong influence of hypoglycemia on the BOLD response. Anderson et al. (2006) investigated the impact of acute hypoglycemia in a hyperinsulinemic hypoglycemic and euglycemic clamp study (BGL kept constant at 50 mg/dl) and observed signal decreases in response to basal visual stimulation of up to 28 %, which reversed when an euglycemic state was restored. Similarly, Driesen et al. (2007) showed signal decreases in primary auditory and visual areas during hypoglycemia induced by hyperinsulinemic clamp in response to auditory and visual stimulation, respectively. The association between BGL and BOLD response lies at hand as neuronal activity in the human brain mainly relies on glucose metabolism (Fox and Raichle 2007; Fox et al. 1988; Pellerin 1994; Paulson et al. 2010). Investigations of effects of BGL on the intensity of the BOLD response during fMRI experiments hitherto mainly focused on BGL significantly lower than those found in ‘life-like’ situations (Driesen et al. 2007; Anderson et al. 2006; Warren et al. 2008; Schafer et al. 2012). These insulin clamp studies give rise to BGL that would usually lead to clinical treatment for hypoglycemia. Prolonged fasting by missing breakfast or by diet in healthy individuals occurs rather frequently and leads to peripheral BGL within a low normal range. After an initial drop, plasma glucose levels will usually remain relatively stable over a long period of time (Hojlund et al. 2001). After 18 h of fasting Hojlund et al. (2001), observed mean plasma glucose levels in fasting individuals of 66 mg/dl (range 55–79 mg/dl), which remained relatively stable with only slight decrease for up to 3 days fasting. In the study a decrease of plasma glucose levels was detected after approximately 6 h fasting. Such physiologically low BGL after prolonged fasting might not have a similarly strong impact on the BOLD signal than hypoglycemia, as glucose proliferation in the brain is very well regulated and is probably only disrupted by clinically relevant hypoglycaemia (Paulson et al. 2010). Recently, a study investigated the influence of blood glucose after fasting and its

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modulation after glucose administration on processing of emotional stimuli. A modulation of hypothalamus activation in a hyperglycemic state was reported, but no overall strong modulation of neural correlates of emotion processing during low BGL after fasting (Scho¨pf et al. 2013). Nevertheless, prolonged fasting states may occur voluntarily (dieters) or involuntarily by having skipped breakfast in subjects participating in fMRI studies, thus even rather small effects on the BOLD signal may lead to distortions in group statistics, as the sensitivity of the experimental design may be decreased. In this study, we aimed to investigate the effects of overnight fasting on basal visual brain activation patterns elicited by a flickering checkerboard (Dale and Buckner 1997). The procedure involves presenting black and white checkerboard on the left or right half of a screen and alternating the color of the squares in high frequency. This leads to robust activation patterns in the occipital gyrus. Our placebo-controlled, double-blind, within-subject design models BGL that occur naturally in healthy human beings after overnight fasting and also in subjects partaking in fMRI studies in general. Such low normal BGL are usually not controlled for in experiments. We therefore instructed our participants to fast overnight for a minimum of 14 h prior to the fMRI measurements, to elicit a low normal BGL state. Every participant had a BGL lower than 80 mg/dl at the beginning of the fMRI measurements. Participants were scanned in an elevated BGL condition, in which the BGL was elevated via infusion of glucagon and a fasting BGL condition, in which participants received sodium chloride and remained in a prolonged fasting state (low normal BGL). We hypothesize decreased brain activation after prolonged fasting in the occipital cortex compared to an elevated BGL state, as this state is associated with decreased arousal. Low arousal states are in turn related to diminished task-related brain activation. Additionally, decreased BOLD signal strength has been observed previously in fMRI studies during hypoglycemia (Anderson et al. 2006; Driesen et al. 2007). We furthermore expect to see broader functional connectivity patterns of brain areas related to visual processing in an elevated blood glucose condition compared to a fasting state, due to different arousal states and possible alterations in brain connectivity patterns in a resting state.

Methods Participants Forty healthy (20 male) participants, all of whom were native speakers of German, took part in the study.

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Participants were between 20 and 32 years of age (mean = 24.5, SD = 3.4) and with a body mass index (BMI) ranging from 20 to 24.6 (mean = 22, SD = 1.4). All participants gave written informed consent and were monetarily compensated for their participation. All participants were recruited by flyers posted on campus and screened on the phone for right-handedness (Oldfield 1971), and suitability for MRI-procedure (no metal implants, no tattoos, etc.). Physical fitness was assessed with BMI inclusion within normal range (to maximize homogeneity of glucose metabolism), by a semi-structured anamnestic interview and by physical examination (topical neurological exam). All suitable participants then underwent neuropsychological testing, exclusion of history of psychiatric diseases (Wittchen et al. 1997) and a venous blood screening (blood glucose, insulin, epinephrine, norepinephrine) to control for further exclusion criteria. Participants on any type of medication (except hormonal contraceptives), impaired in physical fitness or with a history of metabolic, cardiovascular, neurological or psychiatric disorders, abuse of psychoactive substances, head trauma or allergies or whose lab results were out of normal ranges were excluded. The study was reviewed and approved by the ethics committee (institutional review board) of the University Hospital RWTH Aachen. Procedure Before measurements all subjects had been fasting for at least 14 h (i.e., not consuming food or any beverages that might contain carbohydrates, fat, protein, alcohol or artificial sweeteners), all measurements started between 12 a.m. and 1 p.m. All participants were measured twice, with 4 weeks in between measurements to account for cyclic hormonal effects. Glucagon or sodium chloride was administered intravenously to every subject. In the elevated BGL Condition (EC), we applied a 1 mg bolus injection ?0.5 mg/h of glucagon (GlucaGenÒ Hypokit, powder, Novo Nordisk, Mainz, in 50 ml AmpuwaÒ, Fresenius Kabi, Bad Homburg, Germany) which resulted in *1.5 mg glucagon in EC. The same total volume of 50 ml sodium chloride (NaCl 0.9 %, B. Braun, Melsungen, Germany) was administered in the prolonged fasting condition (FC) in which glucose levels remained in a normal low range. All measurements were conducted under double-blind conditions with half of the participants randomly assigned to be administered glucagon during the first session and NaCl during the second session and the other half vice versa. Glucagon is a polypeptide, mostly expressed by the pancreas. Glucagon leads to a secretion of glucose (via conversion of stored glycogen into glucose) into the blood stream and therefore has the opposite effect of insulin

(Jiang and Zhang 2003). BGL achieved by glucagon administration reflect naturally occurring BGL. We avoided oral glucose administration as the taste of carbohydrates triggers reward-related brain activation (Chambers et al. 2009) and shows strong interindividual differences in resorption rates. Furthermore, an insulin clamp creates a highly artificial metabolic state, which is only stable regarding glucose and may strongly interfere with other hormonal systems. On arrival and then subsequently in intervals of 15 min in between fMRI runs, blood glucose levels were measured from capillary blood by finger stick via ‘‘Contour’’ blood glucose meter and test strips (Bayer Vital GmbH, Leverkusen, Germany) according to the manufacturer’s recommendations. With 99.3 % accuracy, the Contour meter meets International Organization for Standardization accuracy requirements (ISO15197:2003). Infusions were administered by intravenous catheter and perfusion pump (MRI-Caddy, Medfusion Inc., Duluth, Georgia, USA) through the left brachial vein. Intravenous blood samples were taken from a second intravenous catheter placed in the right brachial vein, 5 min after bolus injection and at the end of each session to determine levels of blood parameters (epinephrine, norepinephrine, insulin, and cortisol). fMRI measurements started after the bolus injection with a 6 min resting state measurement, followed in counterbalanced order by a working memory paradigm (Riccio et al. 2002) or a mood induction paradigm (Schneider et al. 1994; Kohn et al. 2013; Falkenberg et al. 2012). These paradigms were followed by a basal visual stimulation (checker board), after which the infusion was ended. The fMRI session terminated with a final resting state measurement and in one session an anatomical scan. As only the visual stimulation paradigm (checker board) and the resting state measurement are subject of this work, the other paradigms are not described further. Results from the mood induction are part of another manuscript (Kohn et al., submitted). Visual stimulation In this paradigm stimuli known to produce robust activation in the visual cortex were chosen (cp Dale and Buckner 1997). We presented 8 Hz flickering checkerboards on the right or left hemifield with 10 s duration each and three repetitions. In between stimulation phases a white fixation cross was displayed on a black background as baseline for another 10 s. Subjects were asked to constantly fixate the cross. Resting state measurement Resting state measurements were taken at the end and the beginning of each fMRI session and lasted 6 min. Subjects

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Author's Personal copy were asked to keep their eyes open, while they were shown a gray screen (without fixation cross) and asked to let their mind wander and refrain from occupying themselves with anything specific. This method has been shown to produce the highest retest reliability for the visual component (Patriat et al. 2013). Physiological and hormonal data analyses As manipulation check regarding the effect of our experimental design on BGL, we conducted a two-sample t test of mean BGL in FC and EC and 1 two-sample t test of BGL before measurement. All analyses were tested for significance at a threshold of p = 0.05. Further hormonal variables were sampled and analyzed. Epinephrine, norepinephrine and insulin were analyzed in comparison to pre-screening values in three separate repeated measures ANOVAs. Additionally, we analyze cortisol values between EC and FC using a paired t test. Additional analyses were included with gender as between-subject factor and hormonal contraceptive (yes = 11/no = 9) were conducted for mean beta-values from the FWE-corrected clusters. FMRI data acquisition and processing Imaging data were acquired in a 3T Trio Tim MRT (Siemens, Erlangen, Germany) using EPI (echo planar imaging), with TR = 2 s, TE = 30 ms, 36 slices, ST = 3.3 mm, IG = 0.35 mm, MS = 64 9 64, FOV = 240 9 240 mm, FA = 77°. Analyses of functional images were performed with SPM8 (Statistical Parametric Mapping, Wellcome Department of Cognitive Neurology, London, UK), implemented in Matlab 7.7 (The MathWorks). The images of the time-series were realigned with a two-pass procedure, where the first image (first pass) and the mean image (second pass) were used as reference. Each anatomical scan was co-registered to its according mean EPI scan, which was afterward used to determine spatial normalization parameters by means of the unified segmentation approach (Ashburner and Friston 2005). These normalization parameters were applied to the functional scans and thus transformed the time-series into the standard space defined by the Montreal Neurological Institute (MNI). During normalization all images were resampled to a voxel size of 2 9 2 9 2 mm3 and afterward, images were smoothed with an isotropic Gaussian kernel of 8 mm fullwidth at half-maximum. Individual time-series were analyzed (first level) within the framework of the general linear model (GLM). Two box car functions (one for each of the lateralized stimulations) were convolved with the canonical hemodynamic response function (HRF) and then

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used as predictors in the GLM. We used an AAL (TzourioMazoyer et al. 2002)-based map of the visual cortex as explicit mask in our analyses. The mean across time in each voxel was modeled by a constant-term and low-frequency drifts were removed using a high-pass filter with a cutoff period of 128 s. Temporal correlations were modeled by a first-order regression process in the usual way. The resulting images containing the parameter estimates of the two mood induction conditions of each subject under EC and FC were entered into a repeated measures ANOVA with mixed effects. The factor ‘‘subject’’ was used for random effects and the two mood induction conditions and EC/FC served as levels of the fixed effects factor. The contrasts visual stimulation (left, right) in EC versus FC (CB FC \ EC and CB FC [ EC) was calculated in repeated measures ANOVAs with mixed effects. For comparisons we investigated lateralized stimulation separately and contrasted FC versus EC (e.g., CB_rightEC [ CB_rightFC). We calculated the conjunction between lateralized stimulation for FC and EC (two-tailed) and baseline contrast of the lateralized stimulation in FC and EC (e.g., CB_rightEC [ CB_rightFC \ CB_rightEC ? CB_rightFC). Results are displayed at family-wise error correction (FWE) with a cluster extent of five voxels. For display and demonstration purposes we lowered the threshold to p = 0.001 uncorrected with a cluster extent of 20 voxels. As SPM does not provide cluster correction for conjunction analyses, we opted for this approach. Assuming independence between two images in a conjunction, where each was thresholded at p \ 0.001, results in a probability of 0.000001 for each voxel to pass the threshold in both images simultaneously. This probability is even lower than the critical uncorrected p threshold corresponding to a FWE-corrected threshold in a single image from our analysis (p \ 0.0000058). For anatomical localization of the functional data, we referred to probabilistic cytoarchitectonic maps (Eickhoff et al. 2005; Amunts et al. 2000; Malikovic et al. 2007; Kujovic et al. 2013). Resting state analyses Analysis was carried out using group temporal concatenation independent component analysis (Beckmann and Smith 2005) as implemented in MELODIC (multivariate exploratory linear decomposition into independent components) version 3.14, part of FSL (FMRIB’s Software Library, www.fmrib.ox.ac.uk/fsl). Scans from every single subject in both conditions were temporally concatenated to create one single 4D data set. Prior to submission to MELODIC pre-processing steps were conducted: motion correction, brain extraction, spatial smoothing (full-width

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at half-maximum, FWHM = 6 mm), high-pass filter (0.007 Hz), and normalization (FMRIB’s Nonlinear Image Registration Tool, FNIRT). The following data pre-processing steps were applied to the input data: masking of non-brain voxels, voxel-wise demeaning of the data and normalization of the voxel-wise variance. Pre-processed data were whitened and projected into a 20-dimensional subspace using principal component analysis. The whitened observations were decomposed into sets of vectors which describe signal variation across the temporal domain (time-courses), the session/subject domain and across the spatial domain (maps) by optimizing for non-Gaussian spatial source distributions using a fixedpoint iteration technique (Hyva¨rinen 1999). Estimated component maps were divided by the standard deviation of the residual noise and thresholded by fitting a mixture model to the histogram of intensity values (Beckmann and Smith 2004). Differences between EC and FC were analyzed with the dual regression (DR) technique. DR has three stages. First, the concatenated 4D data set is decomposed via ICA to identify patterns of functional connectivity over conditions and subjects. In the second DR step subject-specific temporal dynamics and associated spatial maps are identified within the individual data set. In the last step, the different component maps are combined in one 4D file and tested voxel-wise for statistically significant differences between conditions and subjects. Non-parametric permutation testing is applied in the last step to determine statistical significance. The resulting maps characterize between condition differences. The component of interest was the visual component. This visual component was defined by spatial correlations with the three visual components identified by correlation with BrainMap metadata (Smith et al. 2009). Correction of multiple comparisons within a volume is computed with threshold-free cluster enhancement (TFCE, Smith and Nichols 2009), and correction for multiple comparisons (regarding contrasts) with Bonferroni’s method.

Results Behavioral results After the experiment, subjects were asked, what drug they thought was administered at the first and second measurement. Answers were at chance level. BGL measured by finger prick from all participants 10 min before fMRI measurements did not show significant differences between EC and FC (mean EC = 72 mg/dl; mean FC = 71.2 mg/ dl). During the experiment EC and FC differed

significantly (mean EC = 114.5 mg/dl, mean FC = 73.9, t = 9.12, p \ 0.05; compare Kohn et al., submitted). Blood parameters Insulin levels differed significantly between pre-screening, EC and FC (pre-screening insulin mean = 12.18 mU/l; EC insulin mean = 46.36 mU/l; FC insulin mean = 3.25 mU/ l; p \ 0.001; F2,38 = 49.75, p \ 0.001, g2p = 0.72). For epinephrine levels in FC (EFC), EC (EEC) and at screening (Epre) the repeated measures ANOVA was significant (mean EFC = 161.85 pmol/l; mean EEC = 235.30 pmol/l; mean Epre = 273.13 pmol/l; F2,37 = 9.09, p \ 0.001, g2p = 0.33). Norepinephrine levels in FC (NEFC), EC (NEEC) and at screening (NEpre) also differed significantly (mean NEFC = 1.28 nmol/l; mean EEC = 2.20 nmol/l; mean Epre = 2.47 nmol/l; F2,37 = 28.31, p \ 0.001, g2p = 0.61). Cortisol differed significantly between FC and NC (mean FC = 305.64 nmol/l; mean EC = 409.15 nmol/l; t38 = 4.64; p \ 0.001; g2p = 0.36). Neither inclusion of gender as between-subject factor nor analysis of differences between women taking oral hormonal contraceptives and women without hormonal treatment revealed any significant main effects and interactions besides the ones observed previously. fMRI data Checkerboard Right-sided stimulation Significant brain activation under both conditions and elevated brain activation in EC compared to FC (CB_rightEC [ CB_rightFC \ CB_rightEC ? CB_rightFC). For the right-lateralized stimulation, we observed brain activation differences in EC compared to FC in two clusters assigned to the left middle occipital gyrus (peak voxel MNI: -30/-80/30 and -32/-80/18, t = 4.67 and 4.53; cluster size: 36 and 6 voxel) (Figs. 1, 2). For the more liberal analysis (p = 0.001 uncorrected, k = 20) we observed four clusters. One large cluster in the left middle occipital gyrus extended into the left superior occipital gyrus (area 18; 341 voxel; peak voxel MNI: -30/-80/30; t = 4.67). Furthermore, we observed one cluster on the border of the left inferior occipital gyrus and the left inferior temporal gyrus (77 voxel; peak voxel MNI: -50/-78/-10, t = 4.24) and a second cluster in the left inferior occipital gyrus (extending into V4 and V3v and the fusiform gyrus; 77 voxel; peak voxel MNI: -30/-82/ -8, t = 4.24). The fourth cluster was located in the left fusiform gyrus (22 voxel, peak voxel MNI: -28/-64/-6; t = 3.43, Fig. 3).

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Significant brain activation under both conditions and elevated brain activation in FC compared to EC (CB_rightEC \ CB_rightFC \ CB_rightEC ? CB_rightFC). No significantly stronger activation in FC compared to EC was observed, neither FWE-corrected nor uncorrected.

Fig. 1 Peripheral blood glucose levels are displayed for EC and FC at three time points. The first measurement was conducted prior to the experiment and experimental manipulation, the second measurement was taken after approximately 30 min and the last measurement was taken at the end of the experiment (after approximately 70 min)

Left-sided stimulation Significant brain activation under both conditions and elevated brain activation in EC compared to FC (CB_leftEC [ CB_leftFC \ CB_leftEC ? CB_leftFC). During left-lateralized stimulation, we observed significantly stronger activation in the right middle occipital gyrus (peak voxel MNI: 40/-78/14; t = 4.96; 8 voxel) and the right cuneus (peak voxel MNI: 16/-82/20; t = 4.69; 6 voxel). For the more liberal analysis, we observed five clusters. The largest cluster was located in the right cuneus, extending into the left cuneus (area 18; 147 voxel; peak voxel MNI: 16/-82/20; t = 4.69). The second cluster

Fig. 2 Hormone levels during pre-study screening of insulin, epinephrine, and norepinephrine are displayed with values for the elevated BGL condition after prolonged fasting (EC) and the prolonged fasting condition (FC). Cortisol was only measured in EC and FC

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Fig. 3 Brain activation in response to left and right stimulation against baseline (upper row) left stimulation EC [ FC (middle row) and right stimulation EC [ FC (lower row). Brain activation against baseline is displayed in blue for left and red for right stimulation (FWE-corrected at p = 0.01, k = 5). Significant brain activation clusters from the conservative conjunction (FWE-corrected, p = 0.05 k = 5) are displayed in yellow, clusters form the liberal conjunction (p = 0.001 uncorrected; k = 20) are displayed in red

showed activation in the right middle occipital gyrus (82 voxel; peak voxel MNI: 40/-78/14; t = 4.96). The third cluster encompassed activation in V5 and the right middle temporal gyrus (49 voxel; peak voxel MNI: 42/-72/0; t = 3.67). The fourth cluster was located in the left middle occipital gyrus (V5; 45 voxel; peak voxel MNI: -46/-70/ 2; t = 4.45). The last cluster was assigned to the right lingual gyrus (area 18, 17 and V3v; 30 voxel; peak voxel MNI: 10/-68/2; t = 4.17; Fig. 3). Significant brain activation under both conditions and elevated brain activation in FC compared to EC (CB_leftEC \ CB_leftFC \ CB_leftEC ? CB_leftFC). No significantly stronger activation in FC compared to EC was observed, neither FWE-corrected nor uncorrected. Resting state connectivity Spatial correlations of the three visual components from Smith et al. (2009) showed a correlation coefficient of greater than 0.3 in only one of the 20 extracted components. The ‘‘occipital pole’’ visual component correlated with 0.65 and the ‘‘medial’’ visual component with 0.49 with the fourth extracted component. Comparing functional connectivity in the visual component between EC and FC led to significant connectivity differences in this component. EC [ FC was associated with a stronger coupling of visual areas to two clusters in the inferior parietal cortex (PGa, PGp and PFm; 179 and 43 voxel, peak voxel MNI: 50/-72/40 and 44/-46/24), the bilateral middle frontal gyri (38 voxel; peak voxel MNI: -40/42/22), the left insula (32 voxel, peak voxel MNI: -42/8/0), and left middle temporal gyrus (19 voxel; peak voxel MNI: -66/-32/-14). EC \ FC showed increased functional connectivity between the areas of the visual component and the right

inferior temporal gyrus (extending to the fusiform gyrus; 123 voxel; peak voxel MNI: 52/-46/-20; see Fig. 4).

Discussion This double-blind, placebo-controlled, crossover study investigated the influence of experimental manipulation of prolonged fasting on neural reactivity measured via BOLD signal to basal visual stimulation. We could show that our experimental design was able to distinguish between a low arousal, fasting state and an activated, elevated condition related to patterns of BGL and other relevant blood parameter associated with prolonged fasting and refeeding after fasting. Although, healthy normal regulatory mechanisms ensure stable glucose levels in the brain, we were able to demonstrate that prolonged overnight fasting leads to a diminished BOLD signal in higher-order occipital processing areas when comparing to an elevated blood glucose condition. These effects are comparatively small, yet may interfere with design sensitivity, when fasting status or blood glucose is not controlled in fMRI experiments. The results remain unidirectional, even under a more liberal threshold. Thus, unmodulated prolonged fasting does not lead to increase in relative brain activation, which furthermore underscores the stability of these effects. Additionally, our results demonstrate that overnight fasting and associated stable, low BGL give rise to early changes in neural reactivity to simple visual stimulation, which may interact with higher-order visual processing and thereby interact with emotional or cognitive processes such as working memory performance, which is known to be influenced by prolonged fasting (Green et al. 1997). This is further substantiated by functional connectivity patterns of a visual component extracted via ICA,

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Fig. 4 Visual component displayed in the upper half of the figure with threshold set to an arbitrary level of z [ 2.3. Differences in functional connectivity of the visual component displayed in the lower row, EC \ FC in blue and EC [ FC in red

which shows stronger coupling with brain areas that can be attributed to the dorsal processing stream of attention. Measurement of hormones in EC, FC and at prescreening before fMRI measurements indicated the validity of our design as we observed constant, low normal BGL during FC and elevated BGL during EC. Insulin, epinephrine and norepinephrine levels were attenuated in FC and reached pre-screening values in EC. Insulin showed a characteristically elevated level in EC, which relates to the physiological reaction of increasing glucose levels (e.g., postprandial). The observed pattern of blood parameters would be expected following refeeding after prolonged fasting. Additionally, the difference between epinephrine and norepinephrine levels are physiologically small, yet these small differences may nevertheless reflect arousal differences, which may at least partially explain observed differences in cognitive performance after prolonged fasting in healthy adults (Green et al. 1997). The effects of prolonged fasting on observed BOLD contrast differences could in our view be mediated by four different sources: (1) effects could be related to a direct, systematic influence of prolonged fasting on neural activity, (2) to a systematic influence of prolonged fasting on the hemodynamic response function (HRF), (3) to direct or indirect, mediating influences of associated changes in hormones due to prolonged fasting, (4) to behavioral or psychological modifications in mental states associated with prolonged fasting. The first two sources of influence relate to either a direct impact of modulation of blood glucose levels or our experimental design on neuronal activity or on the validity of the HRF. Although we cannot test differences in neuronal activity directly and we can only indirectly measure modulation of the HRF, we are rather confident that both influences do not apply in our case. Firstly, a systematic

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impact on neuronal activity and a systematic modulation of the HRF would arguably lead to a linear impact on the measured effect size. This in turn would lead to most pronounced differences in areas that show the strongest overall effect, e.g., primary visual areas or occipital pole. As this is not the case and we observe significant differences in higher level visual processing areas, which do not show overly strong activation in the baseline contrast, we would argue that a systematic impact is highly unlikely. In other paradigms applied in our study, we observed strong effects of paradigm order between subgroups of subjects, which renders the idea of systematic influences unlikely. Additionally, it has been shown that the latency and temporal properties of the HRF remain unaffected even under severe hypoglycemia induced by hyperinsulinemic clamp (Driesen et al. 2007). Hormonal changes measured in our paradigm seem to mirror postprandial values and would thus support the validity of our design. Corollary analyses correlating brain activation differences with different hormonal states did not yield any significant associations. Thus, we would for now have to reject the notion that brain activation differences after prolonged fasting are directly associated with differences in catecholamines, insulin or cortisol levels, as correlations did not reach significance and showed no consistent pattern. The last factor of potential influence was the behavioral or psychological variable. As we cannot draw direct conclusions from behavior, we can only infer relationships from the pattern of observed brain activation. We observed robust differences in brain activation in bilateral middle occipital gyri and the right cuneus during lateralized presentation of a flickering checkerboard in an elevated blood glucose condition compared to prolonged fasting and normal low blood glucose levels. These areas

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can be attributed to the dorsal visual stream (Goodale and Milner 1992), which basically relates to movement of visual objects and related functional consequences. The middle occipital gyrus is in consequence observed in a wide range of experimental tasks and behavioral domains, which encompass attention (Martı´nez et al. 1999; Corbetta and Shulman 2002), working memory (Metzak et al. 2011) and its development in adolescence (Ciesielski et al. 2006), action control (Harsay et al. 2011), in both attending and rating of affectively arousing video clips, albeit stronger when emotions have to be rated (Hutcherson et al. 2005) and is coupled with amygdala activity and modulated by arousal (Kensinger et al. 2011). Brain activation magnitude in this areas decreases with aging (Peiffer et al. 2009), shows decreasing activation with increasing alcohol intoxication (Calhoun et al. 2004) and resting state fluctuations in this area have been observed to be deteriorated in schizophrenia (Hoptman et al. 2010). The location of cuneus activation observed in our study corresponds to the very early (50 ms after stimulus) activation observed during lateralized visual stimulation of the lower and upper visual field quadrants using magnetoencephalography. While stimulation in the upper quadrant led to more consistent activation over subject, also lower quadrant stimulation led to activation in a third or over half of the subjects. The authors propose that this activation patterns may be related to automatic activation of parts of attention or visuomotor-related brain networks (Vanni et al. 2001). In general, this part of the cuneus seems to respond to both lower and upper visual field stimulation (Brewer and Barton 2012). Regarding higher-order cognitive processes, cuneus activation in our study likewise reflects a broad range of different domains. It shows an association between aging and attention (Madden et al. 2010), aging and reward-related brain activation (Schott et al. 2007), and an age related decrease in distinctiveness of visual stimulation most probably related to attentional processes (Carp et al. 2011). The cuneus is also associated to better performance in selective attention tasks in children (Booth et al. 2004), is increasingly active in fear conditioning as a function of associative strength (Moratti and Keil 2009), and has recently in a meta-analysis been found to be active in attentional shifts (as well as the middle occipital gyrus; Kim et al. 2012). Furthermore, the cuneus shows a general perfusion decrease in Parkinson’s disease (Ferna´ndez-Seara et al. 2012), and an increased activation to reward anticipation after psychotherapy in major depression (Dichter et al. 2009). This selection of functions associated with areas significantly strongly activated by visual stimulation in our elevated blood glucose condition is far from exhaustive and cannot encompass all mental functions and behavioral domains in which they are reliably engaged. Nevertheless,

based on the association with the occipito-temporal dorsal visual stream (Goodale and Milner 1992), the fronto-parietal (dorsal and ventral) attentional processing stream (Corbetta and Shulman 2002) and subjective reverse inference, the areas in the middle occipital gyrus and cuneus found in our study will most probably play a role in a broad range of attentional processes. These processes deteriorate with aging, increase their functional contribution during maturation in childhood and adolescence, and may be impaired in a variety of psychiatric diseases and during intoxication. The involvement of these regions in mental disease, intoxication and development may indicate its dynamic role in attentional adaptation to situational demands. Furthermore, epinephrine levels indicate a difference in arousal state between EC and FC and thus lend additional support for their relation to attention. Functional connectivity of a robust visual component identified via ICA during a resting state scan also showed differences between EC and FC. The visual component was more strongly coupled to a network of brain areas in the parietal, frontal and temporal gyrus as well as in the insula in EC compared to FC. FC compared to EC on the other hand, is associated with a stronger coupling to the inferior temporal cortex. One explanation is related to differences in processing mode and related to attentional processes, as we would argue that psychologically the elevated BGL condition is related to an improved attentional state, which is reflected by stronger coupling of visual areas to areas implicated in the attentional processing stream (Corbetta and Shulman 2002). In the fasting state, stronger coupling with inferior temporal cortex may reflect a focus on (more basal) object-oriented visual processing and implicate the ventral visual processing stream (Goodale and Milner 1992). These associations may essentially stem from the direct comparison of EC and FC and may arise, because the body switches to a mode of relatively lower energy consumption in a fasting state. In the direct comparison of a fasting state to an elevated BGL condition, this may reflect a relatively stronger internal focus in the absence of external stimulation. This more pronounced internal focus leads to a diminished connectivity with areas in the attentional processing stream and thus gives rise to an elevated connectivity to areas implicated in the ventral visual stream. In consequence, our results point to an influence on design sensitivity in any fMRI study involving visual (and/ or attentional) processes if blood glucose levels are not controlled. Statistically, even small, systematic influences, such as a decrease of the BOLD response after prolonged fasting, lead to a decreased effect size and thereby to a lower sensitivity to detect an effect, especially in studies with small sample sizes. Thus, we would suggest measuring blood glucose levels prior to inclusion in an fMRI

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Author's Personal copy experiment (possible cutoff 80 mg/dl). Should a subject show a low BGL, this person could either be excluded or asked to consume a carbohydrate rich snack to stabilize BGL prior to the measurement. Additionally, researchers could advise subjects beforehand to refrain from fasting prior to the experiment. This interpretation would additionally have implications for tasks that require a high level of (visual) attention or sustained (visual) attention over time, such as in surgery, flight control and any type of surveillance. Our results give rise to the possibility that performance in such tasks requiring a high level of sustained attention prolonged fasting may lead to a small, but critical deterioration of visual attention. Additionally, this process may critically destabilize (visual) attentional performance in diseases in which attentional processes already are disturbed (such as Parkinson or schizophrenia). Limitations One possible confound is that we have no non-fasting control condition; therefore, we cannot exclude the possibility that glucagon administration directly leads to brain activation differences. Nevertheless, due to the mirroring of hormonal values after glucagon administration of prescreening values, the association with hormonal state after refeeding and the lack of significant correlation to any of the hormones sampled in this study, we feel rather confident that the design does not solely measure glucagon effects, but rather displays a refeeding situation after prolonged fasting. Sleep deprivation is known to be linked to regional homogeneity patterns of brain activation (Dai et al. 2012) and to glucose tolerance (Herzog et al. 2013). As we did not systematically control sleep deprivation, we cannot rule out the possibility that our results may be influenced by this factor, although probability is low, that this took place in a systematic group-specific way. Nevertheless, we would argue that it seems unlikely that sleep would be systematically impaired in the FC condition given our placebocontrolled, crossover design. Additionally, we are not able to assess the possible influence of the time of measurement as we aimed to keep the time of measurement constant to control diurnal metabolic variation. Nevertheless, the anticipation of food intake may influence the results. Further studies could test the stability of the effect depending on the time of the day. Differentiation of the mediating sources for the observed effect remains largely narrative as we are not able to directly test neuronal activation or hormonal influence on the cellular level. We are not able to directly test the association of brain activation with behavior, as no behavioral measure can be taken during checkerboard

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stimulation, which only allows for reverse inferences, which are in our opinion nevertheless theoretically and empirically plausible. The mediating role of attention remains unresolved: It is not clear from the present study if low normal BGL/decreased arousal lead to declines in cognitive processing which in turn produce differences in neuronal activity; or if a low normal BGL/decreased arousal is responsible for an attenuated neuronal firing which then comes along with decreases in cognitive performance.

Conclusion We demonstrated in a double-blind, placebo-controlled, randomized study the influence of prolonged fasting on the BOLD signal in reaction to basal visual stimulation. We were able to demonstrate that prolonged overnight fasting leads to a diminished BOLD signal in higher-order occipital processing areas when comparing to an elevated blood glucose condition. These areas show associations with a broad range of attentional processes are affected by maturation and aging and associated with functionality in psychiatric disease and intoxication, which highlights their role in dynamic attentional adaptation. Thus, despite comparatively small effects we conclude that the effects of prolonged fasting may decrease fMRI design sensitivity in task activation studies involving attentional processes and may also influence functional connectivity patterns, when fasting status or blood glucose is not controlled. Acknowledgments This work was supported by the Faculty of Medicine, RWTH Aachen University (START program 138/09) and by the German Research Foundation (DFG, IRTG 1328, International Research Training Group). UH is supported by a grant from the IZKF Aachen (Interdisciplinary Center for Clinical Research within the faculty of Medicine at the RWTH Aachen University, N4-4). We thank the IZKF Aachen (David Weyer, Andre Schu¨ppen) for technical support. This work is also subject to the medical doctoral thesis of AW.

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