Progress on resting-state electroencephalography functional connectivity based on graph theory in epilepsy
QIN Xiao-xiao
WANG Qun
Abstract：Resting-state electroencephalography(rsEEG)has emerged as a crucial tool in epilepsy research due to its advantages of high temporal resolution and non-invasiveness.Graph theory-based functional connectivity(FC)analysis has revealed common"locally enhanced,globally impaired"characteristics in epilepsy patients,including reduced global efficiency,deviation from small-world properties,and abnormal centrality of key nodes.These topological changes not only facilitate the understanding of the pathological mechanisms of epileptic networks but also assist in seizure detection,epileptogenic focus localization,and treatment outcome prediction.In recent years,studies combining machine learning(ML)and graph neural network(GNN)have further improved the accuracy of rsEEG in seizure prediction and treatment efficacy assessment.However,there are still shortcomings in segmentation strategies,threshold selection,and standardization of analysis procedures.This review summarizes research progress and clinical application prospects based on graph theory,emphasizes its potential value in precise diagnosis and treatment of epilepsy,and proposes that future verification and standardization in large-sample and multi-center studies are necessary.
Keywords：EpilepsyElectroencephalographyGraph theory(not in MeSH)Artificial intelligenceReview
Publication Date：2025-11-25
Online Publishing Date：2025-12-16(First online date of this platform, not the publication date of the document)
Pages：7( 992-998 )