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Updated: Jun 9, 2026

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

EEG functional connectivity methods for seizure-related disorders.

Lise Cottin¹, Charlotte Damien², Luca Anzalone²

¹BEAMS-BioMechatronics Department of École Polytechnique de Bruxelles, Université libre de Bruxelles (ULB), Avenue Franklin Roosevelt 50 1050 Brussels, Belgium.

|June 7, 2026

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Standardizing EEG functional connectivity analysis is crucial for neurological disorder research. This study identifies optimal methods for seizure-related conditions, guiding clinical translation.

Area of Science:

Neuroscience
Computational Neuroscience
Biomedical Engineering

Background:

Electroencephalography (EEG) functional connectivity analysis is vital for understanding neurological disorders.
Lack of standardized methods hinders the clinical application of EEG connectivity.
Guidance is needed for designing robust EEG connectivity analysis pipelines.

Purpose of the Study:

To evaluate numerous EEG connectivity estimation techniques for clinical translation.
To identify optimal pipeline parameters for discriminating between patient subgroups in seizure-related disorders.
To provide quantitative guidelines for EEG functional connectivity pipeline design.

Main Methods:

Analyzed retrospective EEG data from two cohorts: acutely ill adults with seizures and children with self-limited focal epilepsy with centro-temporal spikes (SeLECTS).

Keywords:

EEG Functional connectivity Graph theory Intensive care unit Self-limited epilepsy with centro-temporal spikes

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Last Updated: Jun 9, 2026

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Evaluated 960 distinct connectivity pipelines by systematically varying re-referencing, frequency bands, association measures, and network features.

Utilized statistical testing and logistic regression to assess pipeline performance in discriminating between subgroups.

Main Results:

Significant functional connectivity differences were observed between subgroups in both cohorts.
Pipelines employing corrected association measures (e.g., corrected correlation, weighted phase lag index) and network features reflecting integration/segregation (e.g., characteristic path length, clustering coefficient) performed best.
Optimal pipeline parameters varied significantly between the two disorders, highlighting the importance of clinical context.

Conclusions:

EEG functional connectivity shows potential as a biomarker for seizure-related conditions.
Methodological choices critically impact the results and clinical utility of EEG connectivity analysis.
This study offers quantitative guidelines to support the development of automated EEG connectivity analyses for clinical practice.