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Many methods applied to data acquired by various imaging modalities have been evaluated for their benefit in localizing lesions in magnetic resonance (MR) negative epilepsy patients. No approach has proven to be a stand‐alone method with sufficiently high sensitivity and specificity. The presented study addresses the potential benefit of the automated fusion of results of individual methods in presurgical evaluation. We collected electrophysiological, MR, and nuclear imaging data from 137 patients with pharmacoresistant MR‐negative/inconclusive focal epilepsy. A subgroup of 32 patients underwent surgical treatment with known postsurgical outcomes and histopathology. We employed a Gaussian mixture model to reveal several classes of gray matter tissue. Classes specific to epileptogenic tissue were identified and validated using the surgery subgroup divided into two disjoint sets. We evaluated the classification accuracy of the proposed method at a voxel‐wise level and assessed the effect of individual methods. The training of the classifier resulted in six classes of gray matter tissue. We found a subset of two classes specific to tissue located in resected areas. The average classification accuracy (i.e., the probability of correct classification) was significantly higher than the level of chance in the training group (0.73) and even better in the validation surgery subgroup (0.82). Nuclear imaging, diffusion‐weighted imaging, and source localization of interictal epileptic discharges were the strongest methods for classification accuracy. We showed that the automatic fusion of results can identify brain areas that show epileptogenic gray matter tissue features. The method might enhance the presurgical evaluations of MR‐negative epilepsy patients., There is no known stand‐alone imaging method for epileptogenic zone identification in nonlesional epilepsy. We examined the potential benefit of the automated fusion of results from individual methods. The proposed method can identify epileptogenic tissue with high accuracy at the voxel level, that is, at a millimeters scale.

During social interactions, humans tend to imitate one another involuntarily. To investigate the neurocognitive mechanisms driving this tendency, researchers often employ stimulus-response compatibility (SRC) tasks to assess the influence that action observation has on action execution. This is referred to as automatic imitation (AI). The stimuli used frequently in SRC procedures to elicit AI often confound action-related with other nonsocial influences on behaviour; however, in response to the rotated hand-action stimuli employed increasingly, AI partly reflects unspecific up-right/down-left biases in stimulus-response mapping. Despite an emerging awareness of this confounding orthogonal spatial-compatibility effect, psychological and neuroscientific research into social behaviour continues to employ these stimuli to investigate AI. To increase recognition of this methodological issue, the present study measured the systematic influence of orthogonal spatial effects on behavioural and neurophysiological measures of AI acquired with rotated hand-action stimuli in SRC tasks. In Experiment 1, behavioural data from a large sample revealed that complex orthogonal spatial effects exert an influence on AI over and above any topographical similarity between observed and executed actions. Experiment 2 reproduced this finding in a more systematic, within-subject design, and high-density electroencephalography revealed that electrocortical expressions of AI elicited also are modulated by orthogonal spatial compatibility. Finally, source localisations identified a collection of cortical areas sensitive to this spatial confound, including nodes of the multiple-demand and semantic-control networks. These results indicate that AI measured on SRC procedures with the rotated hand stimuli used commonly might reflect neurocognitive mechanisms associated with spatial associations rather than imitative tendencies. Supplementary Information The online version contains supplementary material available at 10.3758/s13415-020-00860-y.

This study investigated whether differences between personality styles in the processing of social stimuli reflect variability in underlying general-purpose or social-specific neurocognitive mechanisms. Sixty-five individuals classified previously into two distinct personality profiles underwent high-density electroencephalography whilst performing tasks that tap into both aspects of cognitive processing - namely, two distinct facets of general-purpose response inhibition (interference resolution and action withholding) during social information processing. To determine the stage of processing at which personality differences manifest, we assessed event-related components associated with the early visual discrimination of social stimuli (N170, N190) and later more general conflict-related processes (N2, P3). Although a performance index of interference resolution was comparable between the personality profiles, differences were detected in action withholding. Specifically, individuals expressing a wider repertoire of personality styles and more adaptive emotion regulation performed significantly better at withholding inappropriate actions to neutral faces presented in emotional contexts compared with those exhibiting stronger preferences for fewer and less adaptive personality styles and more ruminative affective tendencies. At the neurophysiological level, however, difference between the profiles was observed in brain responses elicited to the same stimuli within the N170. These results indicate that neural processes related to early visual discrimination might contribute to differences in the suppression of inappropriate responses towards social stimuli in populations with different personality dispositions.

Although deep brain stimulation of the subthalamic nucleus (STN-DBS) in Parkinson's disease (PD) is generally a successful therapy, adverse events and insufficient clinical effect can complicate the treatment in some patients. We studied clinical parameters and cortical oscillations related to STN-DBS to identify patients with suboptimal responses. High-density EEG was recorded during a visual oddball three-stimuli paradigm in DBS "off" and "on" conditions in 32 PD patients with STN-DBS. Pre-processed data were reconstructed into the source space and the time-frequency analysis was evaluated. We identified a subgroup of six patients with longer reaction times (RT) during the DBS "on" state than in the DBS "off" state after target stimuli. These subjects had lower motor responsiveness to DBS and decreased memory test results compared to the other subjects. Moreover, the alpha and beta power decrease (event-related desynchronizations, ERD), known as an activation correlate linked to motor and cognitive processing, was also reduced in the DBS "on" condition in these patients. A subgroup of PD patients with a suboptimal response to STN-DBS was identified. Evaluation of RT could potentially serve as a biomarker for responsiveness to STN-DBS.

Social touch seems to modulate emotions, but its brain correlates are poorly understood. Here, we investigated if frontal power band activity in the electroencephalogram (EEG) during aversive mental imagery is modulated by social touch from one's romantic partner and a stranger. We observed the highest theta and beta power when imaging alone, next so when being touched by a stranger, with lowest theta and beta activity during holding hands with the loved one. Delta power was higher when being alone than with a stranger or a partner, with no difference between the two. Gamma power was highest during the stranger condition and lower both when being alone and with the partner, while alpha power did not change as a function of social touch. Theta power displayed a positive correlation with electrodermal activity supporting its relation to emotional arousal. Attachment style modulated the effect of touch on the EEG as only secure but not insecure partner bonding was associated with theta power reductions. Because theta power was sensitive to the experimental perturbations, mapped onto peripheral physiological arousal and reflected partner attachment style we suggest that frontal theta power might serve as an EEG derived bio-marker for social touch in emotionally significant dyads.

Research on dance interventions (DIs) in the elderly has shown promising benefits to physical and cognitive outcomes. The effect of DIs on resting-state functional connectivity (rs-FC) varies, which is possibly due to individual variability. In this study, we assessed the moderation effects of residual cognitive reserve (CR) on DI-induced changes in dynamic rs-FC and their association on cognitive outcomes. Dynamic rs-FC (rs-dFC) and cognitive functions were evaluated in non-demented elderly subjects before and after a 6-month DI (n = 36) and a control group, referred to as the life-as-usual (LAU) group (n = 32). Using linear mixed models and moderation, we examined the interaction effect of DIs and CR on changes in the dwell time and coverage of rs-dFC. Cognitive reserve was calculated as the residual difference between the observed memory performance and the performance predicted by brain state. Partial correlations accounting for CR evaluated the unique association between changes in rs-dFC and cognition in the DI group. In subjects with lower residual CR, we observed DI-induced increases in dwell time [t(58) = –2.14, p = 0.036] and coverage [t(58) = –2.22, p = 0.030] of a rs-dFC state, which was implicated in bottom-up information processing. Increased dwell time was also correlated with a DI-induced improvement in Symbol Search (r = 0.42, p = 0.02). In subjects with higher residual CR, we observed a DI-induced increase in coverage [t(58) = 2.11, p = 0.039] of another rs-dFC state, which was implicated in top-down information processing. The study showed that DIs have a differential and behaviorally relevant effect on dynamic rs-dFC, but these benefits depend on the current CR level.

The degree of response to subthalamic nucleus deep brain stimulation (STN‐DBS) is individual and hardly predictable. We hypothesized that DBS‐related changes in cortical network organization are related to the clinical effect. Network analysis based on graph theory was used to evaluate the high‐density electroencephalography (HDEEG) recorded during a visual three‐stimuli paradigm in 32 Parkinson's disease (PD) patients treated by STN‐DBS in stimulation “off” and “on” states. Preprocessed scalp data were reconstructed into the source space and correlated to the behavioral parameters. In the majority of patients (n = 26), STN‐DBS did not lead to changes in global network organization in large‐scale brain networks. In a subgroup of suboptimal responders (n = 6), identified according to reaction times (RT) and clinical parameters (lower Unified Parkinson's Disease Rating Scale [UPDRS] score improvement after DBS and worse performance in memory tests), decreased global connectivity in the 1–8 Hz frequency range and regional node strength in frontal areas were detected. The important role of the supplementary motor area for the optimal DBS response was demonstrated by the increased node strength and eigenvector centrality in good responders. This response was missing in the suboptimal responders. Cortical topologic architecture is modified by the response to STN‐DBS leading to a dysfunction of the large‐scale networks in suboptimal responders., Network analysis based on graph theory was used to evaluate the high‐density electroencephalography (HDEEG) recorded during a visual three‐stimuli paradigm in 32 Parkinson's disease (PD) patients treated by STN‐DBS in stimulation “off” and “on” states. Cortical topologic architecture is modified by the response to STN‐DBS leading to a dysfunction of the large‐scale networks in suboptimal responders.

Background Electrophysiological markers of prodromal dementia with Lewy bodies were described in the spectral domain. The sub-second temporal resolution may provide additional information. Objective To evaluate electroencephalography (EEG) microstates in patients with mild cognitive impairment with Lewy bodies and to assess the association between their temporal dynamics and the spectral marker. Methods Temporal parameters of microstates were compared between 21 patients with mild cognitive impairment with Lewy bodies and 21 healthy controls. The dominant alpha frequency was correlated with microstate parameters. Results Microstates A-D showed higher occurrence in the patient group. Microstate B additionally revealed shorter mean duration and increased time coverage; its occurrence correlated with the dominant alpha frequency in the patient group. Conclusions Temporal dynamics of all EEG microstates were altered in medication-naive subjects with prodromal dementia with Lewy bodies. Longitudinal follow-up may reveal how EEG microstates reflect progression of brain function deficits and effects of treatment manipulations. © 2021 International Parkinson and Movement Disorder Society.

Impulse control is crucial for everyday functioning in modern society. People with borderline personality disorder (BPD) suffer from impulse control impairment. According to the theory of dual mechanisms of control, motor impulse control can be divided into proactive and reactive modes. Proactive inhibition is involved before an event that might require inhibitory control. Reactive inhibition is initiated after the occurrence of an event that requires inhibitory control. Few studies have focused on proactive inhibition in relation to impaired impulse control, moreover electrophysiological evidence is scarce. Therefore, in the search for electrophysiological correlates of proactive and reactive inhibitions, we assessed event-related potentials elicited during a modified emotionally neutral visual Go/NoGo task in 28 clinically impulsive BPD patients and 35 healthy control (HC) subjects. In both groups, proactive inhibition was associated with enhanced late prestimulus activity and a suppressed poststimulus N2 component. In both groups, reactive inhibition was associated with enhanced poststimulus N2 and P3 components. We found no electrophysiological differences between HC subjects and BPD patients and both groups performed similarly in the task. Hence, the clinically observed impulse control impairment in BPD might act through different mechanisms other than altered inhibitory control in an emotionally neutral task.

Background Spatial and temporal resolution of brain network activity can be improved by combining different modalities. Functional Magnetic Resonance Imaging (fMRI) provides full brain coverage with limited temporal resolution, while electroencephalography (EEG), estimates cortical activity with high temporal resolution. Combining them may provide improved network characterization. New Method We examined relationships between EEG spatiospectral pattern timecourses and concurrent fMRI BOLD signals using canonical hemodynamic response function (HRF) with its 1st and 2nd temporal derivatives in voxel-wise general linear models (GLM). HRF shapes were derived from EEG-fMRI time courses during “resting-state”, visual oddball and semantic decision paradigms. Results The resulting GLM F-maps self-organized into several different large-scale brain networks (LSBNs) often with different timing between EEG and fMRI revealed through differences in GLM-derived HRF shapes (e.g., with a lower time to peak than the canonical HRF). We demonstrate that some EEG spatiospectral patterns (related to concurrent fMRI) are weakly task-modulated. Comparison with existing method(s) Previously, we demonstrated 14 independent EEG spatiospectral patterns within this EEG dataset, stable across the resting-state, visual oddball and semantic decision paradigms. Here, we demonstrate that their time courses are significantly correlated with fMRI dynamics organized into LSBN structures. EEG-fMRI derived HRF peak appears earlier than the canonical HRF peak, which suggests limitations when assuming a canonical HRF shape in EEG-fMRI. Conclusions This is the first study examining EEG-fMRI relationships among independent EEG spatiospectral patterns over different paradigms. The findings highlight the importance of considering different HRF shapes when spatiotemporally characterizing brain networks using EEG and fMRI.

We hypothesized that local/regional properties of stimulated structure/circuitry contribute to the effect of deep brain stimulation (DBS). We analyzed intracerebral electroencephalographic (EEG) recordings from externalized DBS electrodes targeted bilaterally in the anterior nuclei of the thalamus (ANT) in 12 patients (six responders, six nonresponders) with more than 1 year of follow-up care. In the bipolar local field potentials of the EEG, spectral power (PW) and power spectral entropy (PSE) were calculated in the passbands 1-4, 4-8, 8-12, 12-20, 20-45, 65-80, 80-200 and 200-500 Hz. The most significant differences between responders and nonresponders were observed in the BRIDGE area (bipolar recordings with one contact within the ANT and the second contact in adjacent tissue). In responders, PW was significantly decreased in the frequency bands of 65-80, 80-200, and 200-500 Hz (p

It has been suggested that slow oscillations in the subthalamic nucleus (STN) reflect top-down inputs from the medial prefrontal cortex, thus implementing behavior control. It is unclear, however, whether the STN oscillations are related to cortical activity in a bottom-up manner. To assess resting-state subcortico-cortical interactions, we recorded simultaneous scalp electroencephalographic activity and local field potentials in the STN (LFP-STN) in 11 patients with Parkinson’s disease implanted with deep brain stimulation electrodes in the on-medication state during rest. We assessed the cross-structural phase-amplitude coupling (PAC) between the STN and cortical activity within a wide frequency range of 1 to 100 Hz. The PAC was dominant between the δ/θ STN phase and β/γ cortical amplitude in most investigated scalp regions and between the δ cortical phase and θ/α STN amplitude in the frontal and temporal regions. The cross-frequency linkage between the slow oscillations of the LFP-STN activity and the amplitude of the scalp-recorded cortical activity at rest was demonstrated, and similar involvement of the left and right STNs in the coupling was observed. Our results suggest that the STN plays a role in both bottom-up and top-down processes within the subcortico-cortical circuitries of the human brain during the resting state. A relative left–right symmetry in the STN-cortex functional linkage was suggested. Practical treatment studies would be necessary to assess whether unilateral stimulation of the STN might be sufficient for treatment of Parkinson’s disease.

Intentional motor action is typically characterized by the decision about the timing, and the selection of the action variant, known as the “what” component. We compared free action selection with instructed action, where the movement type was externally cued, in order to investigate the action selection and action representation in a Libet’s task. Temporal and spatial locus of these processes was examined using the combination of high-density electroencephalography, topographic analysis of variance, and source reconstruction. Instructed action, engaging representation of the response movement, was associated with distinct negativity at the parietal and centro-parietal channels starting around 750 ms before the movement, which has a source particularly in the bilateral inferior parietal lobule. This suggests that in delayed-action tasks, the process of action representation in the inferior parietal lobule may play an important part in the larger parieto-frontal activity responsible for movement selection.

Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.

Objective We investigated cognitive task-related functional connectivity (FC) in patients with temporal lobe epilepsy (TLE). Using a visual three-stimulus paradigm (VTSP), we studied cognitive large-scale networks and the impact of TLE on connectivity outside the temporal lobe. Methods High-density electroencephalography (EEG) was recorded during the paradigm from nineteen patients with epilepsy with hippocampal sclerosis (HS) and ten healthy controls (HCs). Scalp data were reconstructed into the source space, and FC was computed. Correlating with the neuropsychological data, possible compensatory mechanisms were investigated. Results Significant changes were found in the FC of regions outside the epileptogenic network, particularly in the attentional network. These changes were more widespread in left TLE (LTLE). There were no significant differences in task performance (accuracy, time response) in comparison with HCs, implying that there must be some mechanism reducing the impact of connectivity changes on brain functions. When correlated with neuropsychological data, we found stronger compensatory mechanisms in right TLE (RTLE). Significance Our findings confirm the hypothesis that LTLE is the more pervasive form of the disease. Even though the network alterations in TLE are severe, some mechanisms reduce the impact of epilepsy on cognitive functions; these mechanisms are more potent in RTLE. We also suggest that there are maladaptive mechanisms in LTLE.

Two experiments were performed to investigate the principles by which emotional stimuli are classified on the dimensions of valence and arousal. In Experiment 1, a large sample of healthy participants rated emotional stimuli according to both broad dimensions. Hierarchical cluster analyses performed on these ratings revealed that stimuli were clustered according to their semantic content at the beginning of the agglomerative process. Example semantic themes include food, violence, nudes, death, and objects. Importantly, this pattern occurred in a parallel fashion for ratings on both dimensions. In Experiment 2, we investigated if the same semantic clusters were differentiated at the neurophysiological level. Intracerebral EEG was recorded from 18 patients with intractable epilepsy who viewed the same set of stimuli. Not only did electrocortical responses differentiate between these data-defined semantic clusters, they converged with the behavioral measurements to highlight the importance of categories associated with survival and reproduction. These findings provide strong evidence that the semantic content of affective material influences their classification along the broad dimensions of valence and arousal, and this principle of categorization exerts an effect on the evoked emotional response. Future studies should consider data-driven techniques rather than normative ratings to identify more specific, semantically related emotional images.

Within the last decade, various blind source separation algorithms (BSS) isolating distinct EEG oscillations were derived and implemented. Group Independent Component Analysis (group-ICA) is a promising tool for decomposing spatiospectral EEG maps across multiple subjects. However, researchers are faced with many preprocessing options prior to performing group-ICA, which potentially influences the results. To examine the influence of preprocessing steps, within this article we compare results derived from group-ICA using the absolute power of spatiospectral maps and the relative power of spatiospectral maps. Within a previous study, we used K-means clustering to demonstrate group-ICA of absolute power spatiospectral maps generates sources which are stable across different paradigms (i.e. resting-state, semantic decision, visual oddball) Within the current study, we compare these maps with those obtained using relative power of spatiospectral maps as input to group-ICA. We find that relative EEG power contains 10 stable spatiospectral patterns which were similar to those observed using absolute power as inputs. Interestingly, relative power revealed two γ-band (20–40 Hz) patterns which were present across 3 paradigms, but not present using absolute power. This finding suggests that relative power potentially emphasizes low energy signals which are obscured by the high energy low frequency which dominates absolute power measures.

Objective Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. Approach The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component's time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. Main results We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. Significance Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.

Brain functional connectivity measured by functional magnetic resonance imaging was shown to be influenced by preprocessing procedures. We aim to describe this influence separately for different preprocessing factors and in 20 different most used preprocessing pipelines. We evaluate the effects of slice-timing correction and physiological noise filtering by RETROICOR, diverse levels of motion correction, and white matter, cerebrospinal fluid, and global signal filtering. With usage of three datasets, we show the impact on global metrics of resting-state functional brain networks and their reliability. We show negative effect of RETROICOR on reliability of metrics and disrupting effect of global signal regression on network topology. We do not support the use of slice-timing correction because it does not significantly influence any of the measured features. We also show that the selected types of preprocessing may affect averaged node strength, normalized clustering coefficient, normalized characteristic path length and modularity.

Background The therapy in Parkinson’s disease (PD) by deep brain stimulation (DBS) is sometimes complicated because of adverse neuropsychiatric side effects. In our study with three-stimulus visual oddball paradigm (Bockova et al., in prep.) we identified three subgroups of PD patients. Same subgroups can be found in changes of microstates parameters in EEG resting-state data during DBS ON and DBS OFF state. Methods We acquired scalp 256-channel EEG resting-state data from 18 PD patients with DBS of subthalamic nucleus (STN) and 18 age-matched HC. We analyzed resting-state data of 5 min (PD DBS ON, PD OFF, HC). The data were preprocessed in MATLAB (bandpass filtering 1–40 Hz, independent component analysis for cardiac and eye-blinking artefact suppression, interpolation of bad channels). Cartool was used for EEG microstates analysis. Differences in calculated microstate parameters between subject groups was tested by ANOVA. Results Four template maps (A, B, C, D; Britz et al., 2010) explaining 75.21% of global variance were identified. ANOVA testing revealed statistically significant differences in parameters mean duration, time coverage and occurrences per second for C map between HC and PD. Moreover, all parameters of A and D map were significantly different in the context of PD subgroups identified in Bockova et al., in prep. Conclusions We showed that the diversity related to motor-cognitive performance in PD patients with STN-DBS observed during performance of oddball paradigm is reflected also in significant differences in microstates parameters in the resting-state data. Significant differences in parameters can be found also between PD and HC.

Background The paper deals with joint analysis of fMRI and scalp EEG data, simultaneously acquired during event-related oddball experiment. The analysis is based on deriving temporal sequences of EEG powers in individual frequency bands for the selected EEG electrodes and using them as regressors in the general linear model (GLM). New method Given the infrequent use of EEG spectral changes to explore task-related variability, we focused on the aspects of parameter setting during EEG regressor calculation and searched for such parameters that can detect task-related variability in EEG-fMRI data. We proposed a novel method that uses relative EEG power in GLM. Results Parameter, the type of power value, has a direct impact as to whether task-related variability is detected or not. For relative power, the final results are sensitive to the choice of frequency band of interest. The electrode selection also has certain impact; however, the impact is not crucial. It is insensitive to the choice of EEG power series temporal weighting step. Relative EEG power characterizes the experimental task activity better than the absolute power. Absolute EEG power contains broad spectrum component. Task-related relative power spectral formulas were derived. Comparison with existing methods For particular set of parameters, our results are consistent with previously published papers. Our work expands current knowledge by new findings in spectral patterns of different brain processes related to the experimental task. Conclusions To make analysis to be sensitive to task-related variability, the parameters type of power value and frequency band should be set properly.

Parcellation-based approaches are an important part of functional magnetic resonance imaging data analysis. They are a necessary processing step for sorting data in structurally or functionally homogenous regions. Real functional magnetic resonance imaging datasets usually do not cover the atlas template completely; they are often spatially constrained due to the physical limitations of MR sequence settings, the inter-individual variability in brain shape, etc. When using a parcellation template, many regions are not completely covered by actual data. This paper addresses the issue of the area coverage required in real data in order to reliably estimate the representative signal and the influence of this kind of data loss on network analysis metrics. We demonstrate this issue on four datasets using four different widely used parcellation templates. We used two erosion approaches to simulate data loss on the whole-brain level and the ROI-specific level. Our results show that changes in ROI coverage have a systematic influence on network measures. Based on the results of our analysis, we recommend controlling the ROI coverage and retaining at least 60% of the area in order to ensure at least 80% of explained variance of the original signal.

Multiway array decomposition methods have been shown to be promising statistical tools for identifying neural activity in the EEG spectrum. They blindly decompose the EEG spectrum into spatial-temporal-spectral patterns by taking into account inherent relationships among signals acquired at different frequencies and sensors. Our study evaluates the stability of spatial-temporal-spectral patterns derived by one particular method, parallel factor analysis (PARAFAC). We focused on patterns’ stability over time and in population and divided the complete data set containing data from 50 healthy subjects into several subsets. Our results suggest that the patterns are highly stable in time, as well as among different subgroups of subjects. Further, we show with simultaneously acquired fMRI data that power fluctuations of some patterns have stable correspondence to hemodynamic fluctuations in large-scale brain networks. We did not find such correspondence for power fluctuations in standard frequency bands, the common way of dealing with EEG data. Altogether, our results suggest that PARAFAC is a suitable method for research in the field of large-scale brain networks and their manifestation in EEG signal.

We report our pilot study results, which show for the first time that intensive repetitive transcranial magnetic stimulation (I-rTMS) may induce specific EEG microstate changes in patients with negative symptoms of schizophrenia and these changes are linked to the stimulation treatment aftereffects.

Objective. The scalp EEG spectrum is a frequently used marker of neural activity. Commonly, the preprocessing of EEG utilizes constraints, e.g. dealing with a predefined subset of electrodes or a predefined frequency band of interest. Such treatment of the EEG spectrum neglects the fact that particular neural processes may be reflected in several frequency bands and/or several electrodes concurrently, and can overlook the complexity of the structure of the EEG spectrum. Approach. We showed that the EEG spectrum structure can be described by parallel factor analysis (PARAFAC), a method which blindly uncovers the spatial-temporal-spectral patterns of EEG. We used an algorithm based on variational Bayesian statistics to reveal nine patterns from the EEG of 38 healthy subjects, acquired during a semantic decision task. The patterns reflected neural activity synchronized across theta, alpha, beta and gamma bands and spread over many electrodes, as well as various EEG artifacts. Main results. Specifically, one of the patterns showed significant correlation with the stimuli timing. The correlation was higher when compared to commonly used models of neural activity (power fluctuations in distinct frequency band averaged across a subset of electrodes) and we found significantly correlated hemodynamic fluctuations in simultaneously acquired fMRI data in regions known to be involved in speech processing. Further, we show that the pattern also occurs in EEG data which were acquired outside the MR machine. Two other patterns reflected brain rhythms linked to the attentional and basal ganglia large scale networks. The other patterns were related to various EEG artifacts. Significance. These results show that PARAFAC blindly identifies neural activity in the EEG spectrum and that it naturally handles the correlations among frequency bands and electrodes. We conclude that PARAFAC seems to be a powerful tool for analysis of the EEG spectrum and might bring novel insight to the relationships between EEG activity and brain hemodynamics.

The aim of the current study is visualization of task-related variability in EEG-fMRI data, performed as a blind-search analysis without stimulus timings, using a methodology that is based on Kilner's et al. heuristic approach [2]. We show that filters of the relative EEG spectra with different frequency responses visualize different task-related brain networks. The effect is more pronounced within an event-related oddball paradigm (i.e. detecting rare visual targets) than within a block-design semantic decision paradigm (i.e. detecting semantic errors). The mutual information between different EEG-fMRI activation maps calculated with filters of different frequency responses appears stable between the different paradigms. We also introduce preliminary results implementing the heuristic analysis with spatiospectral EEG components, where the filter response has two dimensions and depends on frequency and channels.

IntroductionDeficits in behavioral inhibition leading to impulsivity occur frequently in many otherwise different psychiatric diseases, mainly ADHD and borderline personality disorder (BPD). However, the research is complicated by using of different tests and their parameters. Further, the role of frontoparietal network in behavioral inhibition has been questioned recently.ObjectivesThe aims of our studies were:– to present the influence of differences in inhibition tasks parameters;– to describe neural correlates of behavioral inhibition in healthy people;– to compare them with BPD and ADHD patients.MethodsWe implemented two different variants of Go/NoGo Task, one designed for behavioral research and the second for neuroimaging. Thirty healthy participants (37% of women, age range 15 to 33 years) underwent behavioral and fMRI measurement. Further, groups of patients with BPD, ADHD and their healthy controls underwent the Go/NoGo Task under both fMRI and EEG.ResultsThe results show differences in behavioral performance based on different task parameters. The fMRI results in healthy people show specific activation patterns within the frontoparietal network associated with inhibition trials (mainly inferior frontal gyrus, insula, cingulate gyrus, SMA, inferior parietal lobule). Further, we present differences between patients with BPD, ADHD and controls in BOLD signal and ERPs.ConclusionsGo/NoGo Task design substantially influences the subjects’ behavioral performance. Our results with methodologically upgraded Go/NoGo Task design provide support for the inhibition frontoparietal brain network and its different activations in BPD and ADHD patients. The research was supported by Ministry of Health of the Czech Republic, grant nr. 15-30062A.Disclosure of interestThe authors have not supplied their declaration of competing interest.

The effect of STN DBS (Deep Brain Stimulation of Subthalamic Nucleus) on the somatomotor network may differ from the effects on the cognitive networks ( Rektor et al., 2015 ). Thus, we acquired high-density (HD) scalp EEG data from 10 Parkinson’s disease patients with STN DBS during DBS ON and OFF state while performing 3-stimulus visual oddball task and we employed blind 3-way decomposition method called PARAFAC. We performed PARAFAC on 3-way data array ( Morup et al., 2008 ) composed by preprocessed averaged trials from all patients, both states (DBS ON/OFF) and all stimulus types. The resulting estimated PARAFAC components have 3 signatures - topography, time series and trial strength loadings for particular averaged trials. Finally, we compared loadings between trial types during both states by Wilcoxon test. PARAFAC revealed evoked activity which showed significant difference between loadings of frequent and target stimuli in the DBS ON state and no difference in DBS OFF. We transformed the topography of the component into the source space, which points to areas of the fronto-parietal attention network. PARAFAC decomposition of evoked potentials seems to be a helpful exploratory tool for HD EEG data. Our results also support a hypothesis that the DBS improves not only motor control but also affects cognitive networks. Acknowledgement: The research was supported by AZV grant 16-33798A and by CF MAFIL of CEITEC (supported by the CzechBI large RI project LM2015062, MEYS CR).

Approximately a 1/3 of epileptic patients suffer from pharmacoresistant epilepsy. The surgery is often the only possible treatment, which brings a need for finding epileptogenic zone. The task is intricate in non-lesional patients in whom magnetic resonance imaging is uninformative. Our goal is to find a set of non-invasive imaging methods that would find the epileptogenic zone. We show first results with Local Synchrony (LS) method, that evaluates functional connectivity between cortical areas in short distances. We acquired high-density electroencephalography (HD-EEG) data from a set of 25 healthy control subjects (HC) and from a set of epileptic patients. We computed the Corrected Imaginary Coherence (CIC) in source space among all possible pairs of adjacent solution points. The CIC image from each patient was compared with the HC images to reveal regions with increased LS. We performed the analysis for 5 patients. In one patient, the revealed regions matched the neurologist’s opinion based on clinical evaluation. In others, increased LS showed regions that might be potential candidates for epileptogenic zone according to clinical evaluation. The evaluation of LS seems to be a useful method that might bring valuable information in the stage of finding the epileptogenic zone in epilepsy patients. Acknowledgement This study was supported by the project 17-32292A of the Czech Health Research Council and We also acknowledge the core facility MAFIL of CEITEC supported by the Czech-BioImaging large RI project (LM2015062 funded by MEYS CR).

Two experiments were performed to investigate the principles by which emotional stimuli are classified on the dimensions of valence and arousal. In Experiment 1, a large sample of healthy participants rated emotional stimuli according to both broad dimensions. Hierarchical cluster analyses performed on these ratings revealed that stimuli were clustered according to their semantic content at the beginning of the agglomerative process. Example semantic themes include food, violence, nudes, death, and objects. Importantly, this pattern occurred in a parallel fashion for ratings on both dimensions. In Experiment 2, we investigated if the same semantic clusters were differentiated at the neurophysiological level. Intracerebral EEG was recorded from 18 patients with intractable epilepsy who viewed the same set of stimuli. Not only did electrocortical responses differentiate between these data-defined semantic clusters, they converged with the behavioral measurements to highlight the importance of categories associated with survival and reproduction. These findings provide strong evidence that the semantic content of affective material influences their classification along the broad dimensions of valence and arousal, and this principle of categorization exerts an effect on the evoked emotional response. Future studies should consider data-driven techniques rather than normative ratings to identify more specific, semantically related emotional images.