Martin Lamoš

Exploring task-related variability in fMRI data using fluctuations in power spectrum of simultaneously acquired EEG

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.

What can be found in scalp EEG spectrum beyond common frequency bands. EEG-fMRI study.

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.

Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA

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.

Intensive repetitive transcranial magnetic stimulation changes EEG microstates in schizophrenia: A pilot study

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.

Multiway Array Decomposition of EEG Spectrum: Implications of Its Stability for the Exploration of Large-Scale Brain Networks

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.

Generalized EEG-FMRI spectral and spatiospectral heuristic models

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 Kilners 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.

Stable EEG Spatiospectral Sources Using Relative Power as Group-ICA Input

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.

Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics

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 components 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.

13-PARAFAC decomposition of evoked potentials in patients treated by STN DBS

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).

04-Local synchrony in EEG as a marker of epileptogenic zone

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).