Whole-Brain Dynamic Resting-State Functional Network Analysis in Benign Epilepsy With Centrotemporal Spikes

Area of Science:

• Neuroscience
• Epilepsy Research
• Brain Network Analysis

Background:

• Benign epilepsy with centrotemporal spikes (BECTS) is a common childhood epilepsy, understood as a network disorder.
• Previous fMRI and EEG source imaging (ESI) studies suggest static functional network (SFN) alterations in BECTS, but these are not consistently found using low-density scalp EEG.
• Resting-state EEG recordings show dynamic network reconfigurations in scalp space, suggesting dynamic analysis is crucial.

Purpose of the Study:

• To develop and validate a whole-brain dynamic resting-state functional network (DFN) computation method using low-density EEG.
• To investigate dynamic network alterations in BECTS patients compared to healthy controls across six frequency bands.
• To assess the clinical applicability of the DFN method for BECTS diagnosis.

Main Methods:

• A novel dynamic resting-state functional network (DFN) computation method was developed based on resting-state low-density EEG recordings (19 channels) and four classical EEG microstates.
• DFN alterations were analyzed in six frequency bands (δ, θ, α_low, α_high, β, and γ) in BECTS patients and healthy controls.
• The DFN method was compared to traditional static functional networks (SFNs) and validated against fMRI and ESI findings.

Main Results:

• The proposed DFN method revealed significant differences between BECTS patients and healthy controls, such as lower global efficiency.
• These findings align with those from traditional fMRI and ESI studies conducted in source space.
• The DFN method successfully computed dynamic network changes directly from low-density EEG, avoiding complex ESI computations.

Conclusions:

• Dynamic resting-state functional network (DFN) analysis of low-density EEG is a sensitive method for detecting network alterations in BECTS.
• This DFN approach offers a clinically viable alternative to fMRI and ESI for BECTS diagnosis, particularly in outpatient settings.
• The dynamic network perspective enhances understanding of network changes in BECTS and holds promise for improved clinical applications.