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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.

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.

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

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.

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.

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.

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).

Images of ocular fundus are routinely utilized in ophthalmology. Since an examination using fundus camera is relatively fast and cheap procedure, it can be used as a proper diagnostic tool for screening of retinal diseases such as the glaucoma. One of the glaucoma symptoms is progressive atrophy of the retinal nerve fiber layer (RNFL) resulting in variations of the RNFL thickness. Here, we introduce a novel approach to capture these variations using computer-aided analysis of the RNFL textural appearance in standard and easily available color fundus images. The proposed method uses the features based on Gaussian Markov random fields and local binary patterns, together with various regression models for prediction of the RNFL thickness. The approach allows description of the changes in RNFL texture, directly reflecting variations in the RNFL thickness. Evaluation of the method is carried out on 16 normal (healthy) and 8 glaucomatous eyes. We achieved significant correlation (normals: R=0.72, p

Traditional approach to epilepsy localization using functional magnetic resonance imaging (fMRI) technique utilizes timings of spike EEG events in so called spike-informed general linear model. Simultaneous fMRI-EEG examination represents a challenging problem in terms of both technical equipment and signal processing. Main goals of our work were to show that haemodynamic changes related to epilepsy can be detected in the blood oxygen level dependent (BOLD) signal without using EEG, and to find optimal settings for the used independent component analysis (ICA) decomposition. In our retrospective study, we compared spatial maps of independent components (IC) derived from preoperative BOLD recordings (24 sessions) with a spatial masks of surgically removed tissue from patients with focal epilepsy. At each patient, there was one component selected as epilepsy-related (ER-) candidate based on spatial similarity criteria. Each ER-candidate IC was visually inspected by a neurologist and either classified as being epilepsy-related or marked as artifact. A dataset of 17 healthy controls was used to evaluate ER-candidate selection process (for each resection mask we tested belonging patient’s data against healthy controls using Wilcoxon test). In the patient group, we found a significantly better fit with the respective resection masks than the healthy control group, in 15 of 24 cases. The cases, where ER-candidate IC was classified as artifact (related to motion or large blood vessel), corresponds with cases of statistically insignificant results, which indicates validity of our approach. Several data decomposition/reduction methods were tested. Best fit between the ER-candidate IC and resection mask was obtained when performing ICA decomposition based on 98% of variability contained in the original data. After identifying possible typical properties of the ER-candidate component, ICA of fMRI data may become a suitable method for improving epilepsy localization, thus becoming a potentially valuable tool with future application in clinics.