YNIMG-11402; No. of pages: 8; 4C: 3, 5 NeuroImage xxx (2014) xxx–xxx

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NeuroImage journal homepage: www.elsevier.com/locate/ynimg

Spatiotemporal dynamics of early cortical gesture processing

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Nicole Möhring, Christina Shen, Andres H. Neuhaus ⁎

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Department of Psychiatry, Charité University Medicine Berlin, Germany

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Article history: Accepted 19 May 2014 Available online xxxx

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Keywords: Gesture Hand sign Event-related potential Inferior parietal lobe Repetition suppression Adaptation

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Gesture processing has been consistently shown to be associated with activation of the inferior parietal lobe (IPL); however, little is known about the integration of IPL activation into the temporal dynamics of early sensory areas. Using a temporally graded repetition suppression paradigm, we examined the activation and time course of brain areas involved in hand gesture processing. We recorded event-related potentials in response to stimulus pairs of static hand images forming gestures of the popular rock–paper–scissors game and estimated their neuronal generators. We identified two main components associated with adaptive patterns related to stimulus repetition. The N190 component elicited at temporo-parietal sites adapted to repetitions of the same gesture and was associated with right-hemispheric extrastriate body area activation. A later component at parieto-occipital sites demonstrated temporally graded adaptation effects for all gestures with a left-hemispheric dominance. Source localization revealed concurrent activations of the right extrastriate body area, fusiform gyri bilaterally, and the left IPL at about 250 ms. The adaptation pattern derived from the graded repetition suppression paradigm demonstrates the functional sensitivity of these sources to gesture processing. Given the literature on IPL contribution to imitation, action recognition, and action execution, IPL activation at about 250 ms may represent the access into specific cognitive routes for gesture processing and may thus be involved in integrating sensory information from cortical body areas into subsequent visuo-motor transformation processes. © 2014 Published by Elsevier Inc.

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Introduction

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Social interaction is central to our lives. Important aspects of social interactions include sending and responding to verbal and non-verbal communicative signals. Non-verbal communication can be further broken down into at least two domains, facial expressions and gestures. Extensive research is dedicated to spatiotemporally investigate central neuronal processing of verbal material and of facial expression, as reflected, e.g., in literature on the N400 and N170 event-related potential (ERP) components, respectively (Bentin et al., 1996; Kutas and Hillyard, 1980). In contrast, much less is known about cognitive processes related to the perception of gestures. This is all the more surprising, as gestures do not only accompany speech or facial expressions, but also may be used as a stand-alone form of communication. Although dedicated hand movements are used in everyday life (e.g. ‘thumb up’), research has only quite recently begun to study the neuronal resources involved in processing gestures. The inferior parietal lobe (IPL) has repeatedly been found to be crucially involved in processing gestures. Evidence originates from neuropsychological studies in patients with lesions to the left IPL and concurrent deficits in imitating manual gestures (Dovern et al., 2011; Goldenberg and Hagmann, 1997; Goldenberg and Karnath, 2006;

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⁎ Corresponding author at: Department of Psychiatry, Charité University Medicine, Campus Benjamin Franklin, Eschenallee 3, 14050 Berlin, Germany. Fax: +49 30 8445 8393.

Haaland et al., 2000; Tessari et al., 2007). This finding was corroborated by neuroimaging studies using positron emission tomography (PET; Decety et al., 2002; Hermsdörfer et al., 2001; Rumiati et al., 2005) and functional magnetic resonance tomography (fMRI; Dick et al., 2014; Dinstein et al., 2007; Hamilton and Grafton, 2009; Mühlau et al., 2005). A recent study reported on facilitated gesture matching by applying anodal transcranial direct current stimulation to the left IPL (area PFm) in healthy controls, thereby extending existing evidence into a potential therapeutic context (Weiss et al., 2013). Together, these studies provide firm topographic evidence for a central role of IPL in gesture processing; however, the temporal embedding into the gesture processing stream has not been addressed so far. On the perceptual level, an activation of the extrastriate body area (EBA; Downing et al., 2001; see also Peelen and Downing, 2007, for a review) can be expected when presenting stimuli that portray gesturing bodies, arms, or hands. While earlier ERP studies found inconsistencies regarding amplitude modulation and latency when comparing face and body perception by utilizing the N170 component (Gliga and Dehaene-Lambertz, 2005; Kovács et al., 2006; Stekelenburg and de Gelder, 2004), subsequent studies clarified those ambiguous results by demonstrating a body-selective ERP component, referred to as N190 (Thierry et al., 2006). Co-registering ERP and fMRI, Taylor et al. (2010) replicated this finding and provided conclusive evidence for EBA as neuronal generator of the N190 component. Another structure in the medial temporal lobe, the fusiform body area (FBA), seems to be related to configural body processing (Hodzic et al., 2009; Taylor et al., 2007;

http://dx.doi.org/10.1016/j.neuroimage.2014.05.061 1053-8119/© 2014 Published by Elsevier Inc.

Please cite this article as: Möhring, N., et al., Spatiotemporal dynamics of early cortical gesture processing, NeuroImage (2014), http://dx.doi.org/ 10.1016/j.neuroimage.2014.05.061

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Eighteen healthy volunteers with a mean age of 25.72 ± 3.9 years and without history (including family history) of neurological or psychiatric disorders participated in this study. All participants had normal or corrected-to-normal vision and were right-handed, as evidenced by a mean laterality index of 72.78 ± 22.7 (Edinburgh Handedness

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Experimental design

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The experiment was carried out in a windowless, dimly lit, electromagnetically shielded, and sound attenuated room. Participants were asked to take a seat in a comfortable chair in front of the screen and to direct their gaze towards the monitor. Standardized instructions for the experimental task were given by the experimenter verbally and visually on the screen. During the whole experimental session subjects were visually monitored by the experimenter through a window from a neighboring room. A repetition suppression paradigm was applied, where participants were instructed to passively observe the static image of a hand forming gestures of the popular rock–paper–scissors game and to respond only to rare target trials. Stimuli were presented on a 24″ TFT computer screen with a viewing distance of approximately 60 cm and a visual angle of approximately 15 × 10° for the outer stimulus contour. Three naturalistic photographs of a right male hand forming rock, paper, or scissors symbols were displayed on a light gray background using Presentation (Neurobehavioral Systems, Albany, CA). Stimulus duration was 2000 ms and stimulus presentation was organized in pairs (S1 = adapter stimulus; S2 = test stimulus) with varying ISIs of 200 ms, 500 ms, 800 ms, or 1200 ms that were evenly distributed across blocks. Inter-trial intervals pseudo-randomly varied between 3000 ms and 4000 ms. Stimulus pairs showed either identical hand figures (categorized as repetition trials) or different hand figures (categorized as non-repetition trials). Additional stimuli with a red frame served as target trials that had to be responded to by a button press. A black fixation cross was visible in the center of the screen when no stimulus was present. Fig. 1 gives an overview of the task. In total, 106 stimuli were presented per block, consisting of 36 repetition trials, 12 non-repetition trials, and 5 target trials that only served as behavioral control for attention and were excluded from ERP analysis. A total of five blocks were presented, each lasting 7 min. The duration of the experiment was thus approximately 40 min including short breaks between each block.

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EEG data acquisition and ERP analysis

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EEG was recorded with a 64-channel DC QuickAmp (Advanced Neuro Technology, Enschede, The Netherlands) with a sampling rate of 512 Hz. An elastic cap equipped with sixty-four sintered Ag–AgCl electrodes according to the extended International 10/10 system and a ground electrode positioned on the forehead was applied. In order to monitor eye movements an additional electrode was placed on the outer canthus of the left eye. Electrode impedances were kept below 5 kΩ. Average reference was used for the online recording. A Brain Vision Analyzer 1.05 (Brain Products, Munich, Germany) was employed for EEG offline analysis. Raw data were first digitally filtered at 0.1 Hz high-pass and 20 Hz low-pass with 24 dB/octave and a notch filter was set at 50 Hz. Ocular artifacts were corrected by using an independent component analysis approach (Jung et al., 2000). Sampling rate was changed to 500 Hz. Data was then segmented to a length of 1100 ms starting 100 ms before stimulus onset and ending 1000 ms after stimulus onset. Segmentation was performed for each experimental condition (S1 and S2; hand figure; ISI), resulting in 48 different segments for each EEG file. In the next steps, data were baseline corrected and segments that were contaminated by artifacts exceeding

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Inventory; Oldfield, 1971). The main intelligence quotient was 105.94 ± 10.1, as measured with a multiple choice vocabulary test (Lehrl et al., 1995). The study protocol was approved by the ethics committee of the Charité University Medicine Berlin and was conducted in accordance with the Declaration of Helsinki and its amendments. All subjects gave written informed consent before participating and received monetary reimbursement for their efforts.

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see also Minnebusch and Daum, 2009, for a review). Of note, body and face area are to be separated within fusiform gyrus, as they seem to represent distinct cortical regions that are specifically sensitive to bodies and faces, respectively (Schwarzlose et al., 2005). We thus hypothesized that activation of lower level areas in the extrastriate visual cortex and of fusiform gyrus precedes the activation of the left IPL during gesture processing. In order to assess the sensitivity of IPL for gesture processing, we employed a repetition suppression paradigm. Repetition suppression is a ubiquitous phenomenon that is based on the reduction of a neuronal response occurring after repeated presentation of the same stimulus (see Grill-Spector et al., 2006, for a review) and that has been observed in single-cell recordings (Desimone, 1996), fMRI studies (Henson et al., 2000), and in magnetic/electrical field changes recorded with magnetoencephalography (MEG; Harris and Nakayama, 2007) and electroencephalography (EEG; Kuehl et al., 2013), respectively. Functionally, repetition suppression is considered a powerful tool to infer neuronal stimulus sensitivity. In a paired stimulus design, neuronal activity in response to an identical repeated stimulus can be compared with the response to a non-identical repeated stimulus that is modified along a certain feature dimension. If the neuronal response amplitude for identical stimuli is attenuated relative to the non-identical pairs, involved neurons are considered to be sensitive to the repeated stimulus feature. Likewise, comparable responses to identical and non-identical repeats indicate that neurons are not sensitive to that feature. Following this rationale, repetition suppression paradigms have been commonly used to investigate the motor system (Hohlefeld et al., 2011), mirror neurons (Chong et al., 2008; Kilner et al., 2009), or face processing (Caharel et al., 2009; Eimer et al., 2010; Kovács et al., 2006). Moreover, rather than targeting a categorical adaptation effect (i.e. adaptation present vs. absent), we pursued a dimensional approach by employing a temporally graded design with different inter-stimulus intervals (ISIs). The duration of the ISI seems to be of critical importance for establishing adaptation effects across sensory domains, as these effects show an exponential decay as a function of time between stimulus pairs (auditory: Lanting et al., 2013; visual: Harris and Nakayama, 2007; Kuehl et al., 2013). In retrospect, negative repetition suppression studies can, at least partially, be explained by adaptation decay due to long ISIs, e.g. in face processing studies (positive, ISI = 200 ms: Caharel et al., 2009; Eimer et al., 2010; Kovács et al., 2006; negative, ISI = 1000 ms; Schweinberger et al., 2002) or in mirror neuron studies (positive, ISI = 500 ms: Kilner et al., 2009; negative, ISI = 1000 ms: Lingnau et al., 2009). As another, potentially serious confounder, it has been shown that adapter duration also affects magnitude and properties of potential adaptation effects (Fang et al., 2007; Kovács et al., 2007; Leopold et al., 2005). Thus, demonstrating IPL adaptation as a function of time seems to provide more reliable evidence for its sensitivity to gesture stimuli compared with categorical designs. Last, we chose to assess passive activity to repeated hand gestures (rock–paper–scissors game) while controlling for attention with an oddball-like task. The rationale behind using passive repetition suppression is to avoid potentially overlapping activity from mirror neuron system areas in IPL, which is a robust finding in tasks that require imitation of motor acts or that compare action observation and execution (Gazzola and Keysers, 2009; Goldenberg and Karnath, 2006; Lindenberg et al., 2012; Nishitani and Hari, 2000).

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Please cite this article as: Möhring, N., et al., Spatiotemporal dynamics of early cortical gesture processing, NeuroImage (2014), http://dx.doi.org/ 10.1016/j.neuroimage.2014.05.061

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Fig. 1. Experimental design and stimuli. Trials always consisted of adapter (S1) and test stimuli (S2) that were presented for 2000 ms each. The inter-stimulus interval (ISI) was variable with durations of 200, 500, 800, and 1200 ms. Each trial was followed by an inter-trial interval (ITI) that pseudo-randomly varied between 3000 and 4000 ms. Static images of a hand with rock, paper, or scissors gestures served as passive stimuli. Experimental trials were categorized as repetitions (same gesture) or non-repetitions (different gesture).

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peak within a time window of 60 ms around the mean corresponding peak of the grand average, i.e. from 94 ms to 154 ms after stimulus onset. The N190 component was scored at electrodes TP7 and TP8 as the most negative peak within 60 ms around the mean corresponding peak of the grand average, i.e. from 166 ms to 226 ms. The P2 component displayed a broader positivity and was thus quantified at electrodes PO5, PO6, PO7, PO8, P5, P6, P7, and P8 within 80 ms around the mean corresponding peak of the grand average, i.e. from 214 ms to

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amplitudes ≥ 80 μV at any electrode were removed. Finally, averages were constructed for each experimental condition. An exploratory butterfly plot was calculated that included averages of all adapter stimuli, three distinct ERP components were identified. To define regions of interest for further analyses, current density scalp maps were used to select only those electrodes that were located in the center of gravity of each ERP component (see Fig. 2). Thus, a P1 component was quantified at O2, PO4, PO6, and PO8 as the most positive

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Fig. 2. Butterfly plot of grand average event-related potentials (ERPs) in response to adapter stimuli (onset at 0 ms) depicting mean ERP traces for all channels across all participants. Butterfly plots revealed a distinct triphasic componentry with principal components labeled as P1, N190, and P2. Latency jitters for each component are highlighted in gray; current source density scalp maps indicate the topographic distribution of the identified ERP components. The thick black line indicates standard deviation pooled across all channels.

Please cite this article as: Möhring, N., et al., Spatiotemporal dynamics of early cortical gesture processing, NeuroImage (2014), http://dx.doi.org/ 10.1016/j.neuroimage.2014.05.061

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SPSS for Windows version 19.0 (IBM, Armonk, NY) was used for statistical analysis. In a first step, electrodes of interest within each hemisphere were averaged for the identified ERP components to avoid circular analyses as outlined by Kriegeskorte et al. (2009). In the second step, final repeated measures analyses of variance (ANOVAs) were conducted with the following factors: ‘repetition’ (adapter vs. repeated vs. nonrepeated test stimulus) × ‘hand figure’ (rock vs. paper vs. scissors) × ‘ISI’ (200 ms vs. 500 ms vs. 800 ms vs. 1200 ms) × ‘hemisphere’ (left vs. right). For all tests, Mauchly's test ascertained that the sphericity assumption was not violated. Partial eta squared (η2) served as an estimator of effect size, i.e. the proportion of data variance accounted for by the statistical model. Post hoc, t-tests for paired samples were calculated for all significant main effects and interactions. All post hoc tests were Bonferroni corrected by multiplying the specific p value with the number of comparisons in that specific post hoc test. The alpha level was set at p b .05 for all tests.

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Mean accuracy to interspersed target trials was 97.78 ± 5.4%, indicating sufficient allocation of attention towards the task and the stimuli. Mean reaction time for target trials was 653.5 ± 129 ms.

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In an exploratory butterfly plot (see Fig. 2) computed for all adapter stimuli, three distinct ERP components were identified that followed a componentry consisting of occipital P1, temporo-occipital N190, and parieto-occipital P2 (P2) ERP-components.

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A three-way ANOVA with the factors ‘repetition’ × ‘hand figure’ × ‘ISI’ revealed a main effect of ‘repetition’ (F2,34 = 7.690; p = .007; η2 = .311). Post-hoc analysis demonstrated that non-repeated test stimuli elicited higher amplitudes (6.85 ± 3.6 μV) compared to amplitudes in response to adapter stimuli (5.82 ± 2.9 μV; T17 = 2.941;

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N190 ANOVA showed a significant main effect of ‘repetition’ (F2,34 = 14.206; p b .001; η2 = .455) and a trend-level main effect of ‘hemisphere’ (F1,17 = 3.562 p = .076; η2 = .173). The factor ‘repetition’ was driven by an adaptation effect. Here, adapter stimuli (− 3.74 ± 2.1 μV) elicited higher N190 amplitudes than repeated test stimuli (− 2.48 ± 1.31 μV; T17 = 3.80; p = .001), but not significantly higher than non-repeated test stimuli (−3.65 ± 1.6 μV; T17 = 0.375; p = .524). N190 amplitudes to non-repeated test stimuli were significantly more negative than N190 amplitudes to repeated test stimuli (T17 = 6.001; p b .001), hence demonstrating an adaptation effect due to identical stimulus repetition (see Fig. 3A). The trend-level main effect of ‘hemisphere’ was driven by a significantly smaller (i.e. more suppressed) right-hemispheric N190 response (− 2.77 ± 2.1 μV) compared to the left side (− 3.82 ± 1.9 μV; T17=; p = 0.40). Consistently, source localization during the N190 time frame estimated the main neuronal generator in response to averaged adapter hand stimuli in right-hemispheric EBA (Brodmann Area (BA) 39; MNI coordinates X, Y, Z: 40, −60, 15; exact t = 5.09; significance threshold for p = .01 at t = 3.074; see Fig. 3B).

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The cortical distribution of electrical activity recorded from scalp electrodes in response to averaged adapters was computed with Standardized Low Resolution Brain Electromagnetic Tomography (sLORETA; Pascual-Marqui, 2002). sLORETA employs a realistic three-shell head model registered to the Talairach atlas of the human brain (Talairach and Tournoux, 1998) with a three-dimensional solution space that is restricted to the cortical gray matter and the hippocampus. The intracerebral volume comprises a total of 6239 voxels at 5 mm spatial resolution. Without a priori assumptions on number and location of active sources, this solution to the inverse problem computes the standardized current density at each voxel as the weighted sum of the scalp electric potentials. The time frames of interest for statistical imaging were selected on a data-driven base following ERP analysis, i.e. the time frames were identical to those used for peak detection. Statistical imaging of current density differences was done based on non-parametric voxelby-voxel t-tests (Holmes et al., 1996). This maximum t-statistic offers a procedure of bootstrap resampling (5000 randomly created groups across conditions) and produces non-corrected threshold values for single voxel p's.

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p = .027) and to repeated test stimuli (6.15 ± 3.4 μV; T17 = 3.349; p = .009). However, comparison of P1 amplitude in response to adapter stimuli and repeated test stimuli revealed no significant differences (T17 = 1.454; p = .492), thus indicating insensitivity of the P1 component to stimulus repetition.

ANOVA yielded main effects of ‘repetition’ (F2,34 = 10.770; p b .001; η2 = .388), ‘ISI’ (F3,51 = 11.755; p b .001; η2 = .409) as well as significant two-way interactions of ‘repetition’ × ‘ISI’ (F6,102 = 10.424; p b .001; η2 = .380) and ‘repetition’ × ‘hemisphere’ (F2,34 = 5.616; p = .008; η2 = .248). Post-hoc analysis of the factor ‘repetition’ revealed that adapter stimuli (5.23 ± 2.2 μV) evoked significantly higher P2 amplitudes than did repeated (3.99 ± 1.8 μV; T17 = 4.226; p = .002) and nonrepeated test stimuli (4.24 ± 1.9 μV; T17 = 3.381; p = 0.008), respectively, which corresponds to a repetition effect irrespective of gesture. Subsequent analysis of the main effect ‘ISI’ showed that an ISI of 200 ms led to a significant reduction of P2 amplitude in response to test stimuli (3.56 ± 1.8 μV) compared to an ISI of 500 ms (4.63 ± 2.1 μV; T17 = 3.497; p = .018), 800 ms (4.81 ± 2.1 μV; T17 = 3.468; p = .018), or 1200 ms (4.96 ± 1.9 μV; T17 = 5.656; p = .001). Most important within the scope of our study, a post-hoc analysis of the two-way interaction ‘repetition’ × ‘ISI’ demonstrated a greater P2 amplitude suppression in response to repeated test stimuli preceded by a short ISI of 200 ms (1.98 ± 1.7 μV) compared to ISIs of 500 ms (4.20 ± 2.0 μV; T17 = 5.349; p b .001), 800 ms (4.68 ± 2.2 μV; T17 = 5.708; p b .001), and 1200 ms (5.09 ± 2.3 μV; T17 = 6.361; p b .001; see Fig. 4A). Graphics of the S2/S1 ratio illustrates that repetition suppression effect of the P2 component follows an asymptotic function of ISI (see Fig. 4B). Moreover, P2 amplitudes in response to repeated and non-repeated test stimuli were significantly different when preceded by an ISI of 200 ms (repeated 1.98 ± 1.7 μV; non-repeated 3.29 ± 2.3 μV; T17 = 3.023; p = .008), which is in line with the idea of an asymptotic decay function of repetition suppression effects. Paired t-tests further showed that the interaction of ‘repetition’ × ‘hemisphere’ relied on a stronger RS effect for the left-hemispheric P2 component. On the left side, there were significant differences between adapters (5.08 ± 2.8 μV) and repetitions (3.48 ± 2.3 μV; T17 = 5.503; p b .001) as well as between adapters and non-repetitions (3.55 ± 2.5 μV; T17 = 5.739; p b .001). On the right hemisphere, there was only a difference between adapters (5.39 ± 2.2) and repetitions (4.49 ± 2.0; T17 = 2.594; p = .019).

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parts. Specifically, we used a temporally graded repetition suppression protocol and aimed at characterizing event-related neuronal responses of IPL associated with processing of hand gestures. We identified a significant interaction effect of repetition and ISI on the adaptation magnitude of a mid-latency ERP component, i.e. a P2 component exhibited systematic adaptation effects as a function of both stimulus timing and repetition condition. From a methodological perspective, this interaction along with the typical adaptation decay over inter-stimulus time emphasizes the sensitivity of this ERP component, but not earlier processing stages, to processing of gestures. In neuronal source estimation, the left IPL as well as the

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Source localization estimated the main neuronal generators in the left fusiform gyrus (BA 37; MNI: −35, −45, −5; t = 4.92; significance threshold for p = .05 at t = 3.212; see Fig. 4C), in the right-hemispheric EBA (BA 39; MNI: 55, −45, 5; t = 4.770), in the left inferior parietal lobule (BA 40; MNI: − 25, − 65, 45; t = 4.68), and in the right fusiform gyrus (BA 37; MNI: 50, −45, −10; t = 4.59).

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Fig. 3. Scalp topography, ERP traces, and source localization of the N190 component. (A) ERP traces stratified for the significant main effect of ‘repetition’, as indicated by repeated measures ANOVA. Repetition stimuli (red line) lead to significantly reduced amplitudes in the N190 time frame, which is highlighted in gray. Scalp current density map indicates the cortical topography during that time frame; electrodes used for ERP construction are indicated in black. (B) Current source density analysis indicates right EBA as the main neuronal generator of scalp EEG during the N190 time frame. Yellow color indicates significantly greater activation than in the comparator condition (baseline).

Fig. 4. Scalp topography, ERP traces, adaptation as a function of ISI, and source localization of the P2 component. (A) ERP trace stratified for adapter/test stimuli and different ISIs. Test stimuli show adaptation effects as a function of ISI during the P2 time frame, which is highlighted in gray. Scalp current density map indicates the cortical topography during that time frame; electrodes used for ERP construction are indicated in black. (B) Adaptation ratios of P2 amplitudes following different ISIs. Means of test stimuli (S2) were divided by the means of the immediately preceding adapters (S1). Error bars represent standard error of the mean. (C) Current source density analysis indicates the right EBA, left IPL, and fusiform gyri bilaterally as the main neuronal generators of scalp EEG during the P2 time frame. Yellow color indicates significantly greater activation than in the comparator condition (baseline).

Please cite this article as: Möhring, N., et al., Spatiotemporal dynamics of early cortical gesture processing, NeuroImage (2014), http://dx.doi.org/ 10.1016/j.neuroimage.2014.05.061

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into more specific processing operations, i.e. gesture processing. In this context, it has to be noted that most studies assessed IPL activation through experimental designs that involve imitation and are thus likely to assess mirror neuron activity. For example, Lindenberg et al. (2012) showed movie clips of emblematic gestures and found a fronto-parietal activation pattern that topographically matches the mirror neuron system including the ventral premotor cortex, inferior frontal gyrus, and IPL (see Rizzolatti and Craighero, 2004, for a review). Similarly, lesions to the left fronto-parietal cortex areas in close proximity to the mirror neuron system have been shown to lead to reduced behavioral imitation performance (Goldenberg and Karnath, 2006). We tried to reduce the probability of mirror neuron activation by applying a purely passive paradigm, while assessing functional sensitivity through neuronal response adaptation. Consequently, we did not observe activity in frontal mirror areas, such as the inferior frontal gyrus or premotor cortex. On the other hand, a recent meta-analysis on motor imagery found that imagined intransitive (i.e. without tool use) upper limb movements already resulted in fronto-parietal activation of the mirror neuron system (Hetu et al., 2013). Alternatively, frontal activation patterns may be harder to assess via EEG due to neuronal generator orientation or generator location in the depth of the cortical convolution. Frontal activation of the mirror neuron system is usually associated with the opercular region of the inferior frontal gyrus (Iacoboni et al., 1999) and may continuously extend into the anterior insula, which is sometimes also found in studies on mirror neurons, albeit most strongly in the context of empathic concern (Wicker et al., 2003; Zaki et al., 2009). Nevertheless, frontal mirror neuron generators generally might be located more to the sulcal depth than in the parietal cortex. IPL is also part of the mirror network and it has also been suggested that EBA may be linked to the human mirror neuron system by automatically matching static and dynamic body representations. Observation of moving body parts, especially goal-directed actions lead to greater activation of EBA compared to perception of static images (Takahashi et al., 2008). Further, Astafiev et al. (2004) demonstrated that EBA is activated by self-generated overt and covert limb movement. These results tentatively suggest that body-specific areas might contribute to action-specific areas by providing a spatial framework onto which actions are then mapped. In other words, extrastriate and fusiform body areas may help integrating sensory information into subsequent visuo-motor transformation processes that are initiated in IPL. The hypothesis of an IPL/body area co-activation for body/action mapping is supported by previous studies that reported such coactivations during identification of human bodies (Hodzic et al., 2009), identification of goal-directed actions (Marsh et al., 2010), and identification of perspective during hand actions (Jackson et al., 2006). Another study found that activation of a left-sided hand area in the lateral occipito-temporal cortex was functionally connected with activation of the left intraparietal sulcus and left premotor cortex, thus further strengthening the idea of a contribution of body areas to action mapping circuits (Bracci et al., 2012). Definitely disentangling pure perception from the automatic perception/action mapping system, however, remains a fascinating challenge for future studies. Several limitations have to be acknowledged. Although repetition suppression seems to be a ubiquitous phenomenon and there is a strong rationale for its use in determining functional involvement of brain areas in domain-specific processing, it cannot be assumed that all neurons respond to repetitions in the same way. Even more complicating this issue, repetition effects in a given brain region might affect downstream areas and might thus produce effects in region that are usually insensitive to the investigated functional domain (see Grill-Spector et al., 2006, for a review). In this regard, our study is best considered in the light of findings from other studies that employed different techniques and paradigmatic approaches. Here, our results are consistent with the literature; nevertheless, other studies using repetition suppression and ERPs are needed to confirm our results. Next, our paradigm

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right extrastriate and bilateral fusiform body areas were activated during the P2 time frame. The neuronal processing stages identified here are well in line with previous research. The P1 component was not further explored, as it did not show robust adaptation effects. Its cortical sources in the extrastriate visual cortex are well-known (Clark and Hillyard, 1996; Gomez Gonzalez et al., 1994). Regarding the N190, and consistent with the ERP results obtained here, Thierry et al. (2006) were the first to isolate this component by showing that both its latency and topography (194 ms at P8) in response to images of body parts significantly differed from face-sensitive N170 properties (174 ms at PO10). Later studies into the cortical dynamics of body perception with MEG essentially replicated this finding by using double pulse presentation of scrambled vs. unscrambled versions of body parts and faces (Ishizu et al., 2010) and by analyzing event-related field components while participants had to observe headless bodies or faces (Meeren et al., 2013). In both studies, the M190 component was localized in the posterior temporal cortex areas including EBA (Ishizu et al., 2010; Meeren et al., 2013). Our localization results are also in good agreement with previous fMRI studies describing EBA activation in response to static and moving images of body parts (Bracci et al., 2010; Chan et al., 2004; Downing et al., 2001; Myers and Sowden, 2008; Saxe et al., 2006). Another piece of evidence is provided by a recent study applying functional lesions to the right EBA via transcranial magnetic stimulation, thereby disrupting behavioral performance in a delayed matching-to-sample task involving body stimuli (Pitcher et al., 2012). Similarly, and more closely related to our result, a recent study by Sadeh et al. (2011) found that transcranial magnetic stimulation to EBA affected the N1 to bodies, but not the N1 response to faces, while stimulation of the occipital face area affected the N1 response to faces, but not to bodies. Our data are in good agreement with these studies in that we found N190 adaptation to identical stimuli, suggesting sensitivity to physical stimulus identity of hand gestures. The maximum P2 activity was found at about 250 ms and was associated with an exponential decay function as a result of the temporal repetition suppression grading and is thus fully in line with our hypothesis. Repetitions of both identical and non-identical hand gestures resulted in significant and temporally graded amplitude reduction, thus indicating sensitivity to hand gestures per se, irrespective of the specific hand figure or its connotation. This dynamic adaptation pattern of the P2 response strongly suggests functional sensitivity to hand gesture processing according to the rationale of repetition suppression in general (Grill-Spector et al., 2006) and to the repeatedly observed exponential decay function of adaptive neuronal response patterns over time (Harris and Nakayama, 2007; Kuehl et al., 2013; Lanting et al., 2013). We identified main generators in the right EBA, fusiform gyri bilaterally, and the left IPL. An MEG study on cortical processing of lip forms found left IPL activity at a comparable latency (Nishitani and Hari, 2002) and is thus in good agreement with our results. Another MEG study on meaningful vs. meaningless gestures also found bilateral IPL activity at a comparable latency (Nakamura et al., 2004); however, in that study, specificity to hand gestures as such remains unclear, because no unspecific control condition or repetition suppression was applied. Our finding also matches previous behavioral lesion and functional imaging studies describing left IPL activation to gestures across meaningful and meaningless stimulus material (Decety et al., 2002; Dick et al., 2014; Dinstein et al., 2007; Dovern et al., 2011; Goldenberg and Hagmann, 1997; Goldenberg and Karnath, 2006; Haaland et al., 2000; Hamilton and Grafton, 2009; Hermsdörfer et al., 2001; Mühlau et al., 2005; Rumiati et al., 2005; Tessari et al., 2007). The co-activation of the right extrastriate and bilateral fusiform body areas might indicate overlapping activation patterns from N190 activity; however, the N190 itself was not associated with fusiform activation. Alternatively, and from a general perspective, EBA and FBA may be coactivated together with the left IPL to integrate perceptive processes, i.e. configural body processing (Hodzic et al., 2009; Taylor et al., 2007),

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The authors wish to thank all participants of this study. We also thank Emily Brandt for her technical support.

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was not balanced with respect to repeated and non-repeated test stimuli. The probability of a repetition trial was three times higher than the probability of a non-repetition trial, thus inducing an inherent expectation for a repetition trial. Summerfield et al. (2008) have shown that expectation affects repetition suppression, which may have produced a confound in our data. However, when specifically investigating ERPs, Summerfield et al. (2011) found that the influence of expectation on ERPs was not present in all electrodes and did only attenuate repetition suppression effects. To fully exclude the impact of expectation, however, a balanced task design would have been favorable. Additionally, according to the study by Larsson and Smith (2012), diverting attention away from the stimulus material may allow for experimentally dissociating expectation and repetition suppression. The authors found that expectation interfered with repetition suppression, which, however, was not the case when subjects were engaged in a sustained attention task presented in the center of the screen. Experimental manipulation of attentional engagement may thus further aid in isolating the neuronal response sensitivity to a given stimulus class. In conclusion, the present study demonstrates temporally graded repetition suppression effects of the IPL associated with cortical processing of hand gestures at about 250 ms post stimulus. The adaptation pattern derived from the graded repetition suppression paradigm demonstrates the functional sensitivity of IPL to gesture processing. Bodyspecific sensory cortex areas that are co-activated may represent the entrance into specific cognitive routes for gesture processing. Together, IPL and body areas seem to be involved in integrating sensory information into subsequent visuo-motor transformation processes in close temporal proximity.

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There is no conflict of interest, financial or otherwise, related to this work for any of the authors.

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