Evolution of Graph Theory in Dynamic Functional Connectivity for Lateralization of Temporal Lobe Epilepsy

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Epilepsy Research

Background:

• Resting-state functional magnetic resonance imaging (rsfMRI) reveals brain functional architecture without tasks.
• Functional connectivity (FC) is inherently non-stationary and varies over time.
• Static FC analysis may not fully distinguish between left and right temporal lobe epilepsy (TLE).

Purpose of the Study:

• To compare graph theoretical characteristics using dynamic functional connectivity between left and right TLE.
• To investigate temporal trends of these characteristics as potential markers for TLE laterality.
• To assess the efficacy of dynamic versus static FC analysis in TLE laterality detection.

Main Methods:

• Utilized dynamic functional connectivity analysis on rsfMRI data.
• Measured six graph theoretical characteristics.
• Compared these characteristics and their temporal trends between left and right TLE patient groups.

Main Results:

• Dynamic functional connectivity analysis successfully identified laterality in TLE, outperforming static analysis for certain characteristics.
• Temporal trends of specific graph theoretical characteristics showed promise in distinguishing TLE laterality.
• Static connectivity analysis demonstrated limitations in fully separating left and right TLE.

Conclusions:

• Dynamic functional connectivity is a more sensitive method for detecting TLE laterality compared to static analysis.
• Temporal dynamics of graph theoretical measures offer a novel biomarker for TLE laterality.
• This approach enhances the understanding of brain network alterations in TLE.