A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks

Abstract

This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at the level of electroencephalogram (EEG) sources. The framework combines the use of common spatial patterns to select the EEG components which maximize the variance between two experimental conditions, simultaneous implementation of vector autoregressive modeling (VAR) with independent component analysis to describe the joint source dynamics and their projection to the scalp, and computation of information dynamics measures (information storage, information transfer, statistically significant network links) from the source VAR parameters. The proposed framework was tested on simulated EEGs obtained mixing source signals generated under different coupling conditions, showing its ability to retrieve source information dynamics from the scalp signals. Then, it was applied to investigate scalp and source brain connectivity in a group of children manifesting episodes of focal and generalized epilepsy; the analysis was performed on EEG signals lasting 5 s, collected in two consecutive windows preceding and one window following each ictal episode. Our results show that generalized seizures are associated with a significant decrease from pre-ictal to post-ictal periods of the information stored in the signals and of the information transferred among them, reflecting reduced self-predictability and causal connectivity at the level of both scalp and source brain dynamics. On the contrary, in the case of focal seizures the scalp EEG activity was not discriminated across conditions by any information measure, while source analysis revealed a tendency of the measures of information transfer to increase just before seizures and to decrease just after seizures. These results suggest that focal epileptic seizures are associated with a reorganization of the topology of EEG brain networks which is only visible analyzing connectivity among the brain sources. Our findings emphasize the importance of EEG modeling approaches able to deal with the adverse effects of volume conduction on brain connectivity analysis, and their potential relevance to the development of strategies for prediction and clinical treatment of epilepsy.

Multivariate Correlation Measures Reveal Structure and Strength of Brain–Body Physiological Networks at Rest and During Mental Stress

“…However, neural time series obtained starting from the oscillations recorded on the scalp—even if affected by confounding factors—can still represent a starting point for estimating brain network interactions ( Reid et al, 2019 ). From this point of view, the analysis carried out in this work represents a first step to be confirmed in the future using source-reconstructed signals ( Van de Steen et al, 2019 ), or even exploiting frameworks for the computation of source connectivity measures directly from scalp recordings ( Kotiuchyi et al, 2020 ). Other limitations of the current study consist in the relatively small number of subjects analyzed, in the possibility of a not so-clear distinction between the elicited level stress evoked by GAME and MENTAL situations which may affect the obtained results and in the fact that blood pressure was not acquired on the subjects, which could give additional useful physiological indications.…”

“…Such protocol should also include intermediate resting phases between stressful situations to assess whether elicited stress still produces effects during time in a consequent resting phase. Future methodological work is also envisaged regarding: (a) a thorough validation on simulations of the MI measures presented here performed also through a direct comparison with more sophisticated analysis techniques including the use of time-delayed techniques employing tools of information dynamics to retrieve directional information ( Faes et al, 2014 ) and of non-linear model free entropy estimators ( Faes et al, 2015a ); (b) the frequency-specific decomposition of the proposed measures (e.g., following Faes et al, 2020 ) to investigate how MIs can reflect oscillatory rhythms with specific physiological meaning; and (c) the analysis on source-reconstructed signals to obtain better anatomically-localized estimates of the strength and topology of brain–body interactions ( Lai et al, 2018 ; Van de Steen et al, 2019 ; Kotiuchyi et al, 2020 ).…”

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

“…In CDAS based on ML, the most important feature extraction techniques include the time domain, frequency, and nonlinear features [18]. Choosing different feature extraction algorithms together to reach a high diagnosis accuracy demands a fair amount of knowledge in the field of ML [19,20].…”

Extending the spectral decomposition of Granger causality to include instantaneous influences: application to the control mechanisms of heart rate variability

“…Future studies are envisaged to compare directly the patterns of time and frequency-domain causality between cardiovascular and respiratory variability provided by the present framework and previous works modelling directed instantaneous effects [9,10], as well as to explore the potential of the GC measures proposed here in contexts where instantaneous effects are known to impact significantly on causality analysis; the latter issue is typical in the study of brain connectivity, e.g. in functional magnetic resonance imaging where the acquired signal represents a smoothed hemodynamic response and is severely undersampled [30][31][32] and in electroencephalography where the volume conduction effect is a main confounding source of zero-lag correlations [33,34].…”