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

• Published in: Brain sciences Vol. 10; no. 9; p. 657
• Main Authors: Kotiuchyi, Ivan, Pernice, Riccardo, Popov, Anton, Faes, Luca, Kharytonov, Volodymyr
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.09.2020; MDPI
• Subjects: Algorithms; Brain mapping; Causality; Convulsions & seizures; Diagnosis; EEG; Electroencephalography; Epilepsy; Health aspects; Information storage; information theory; information transfer; Neural circuitry; Neural networks; Physiological aspects; Principal components analysis; Seizures; Sensors; Signal processing; Statistical analysis; Time series; vector autoregressive modeling; Ukraine; epilepsy; independent component analysis; EEG; common spatial patterns; information theory; information transfer; information storage; vector autoregressive modeling
• ISSN: 2076-3425; 2076-3425
• DOI: 10.3390/brainsci10090657

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.