Distinguishing patients with temporal lobe epilepsy from normal controls with the directed graph measures of...

Changes in the brain networks of patients with temporal lobe epilepsy (TLE) have been extensively explored, but the biological mechanisms underlying these alterations remain unclear. Here, we aim to identify changes in brain networks in patients with TLE and provide an accurate algorithm for distinguishing these patients from normal controls (NC) with graph-theoretical approach and advanced machine learning methods.

Directed network construction was applied to resting-state functional magnetic resonance imaging (rs-fMRI) data from 55 subjects (23 TLE patients and 32 NC), and 13 directed graph measures were calculated. Two-sample t-test selected features were used as inputs to a support vector machine (SVM). The leave-one-out cross-validation method was used in measuring classification performance.

An accuracy of 94.55% (sensitivity = 91.30%, f1-score = 93.33%, Cohen's kappa = 0.9345) was achieved for the classification of TLE patients and NC with optimal features and SVM classifier. According to the results of the two-sample t-test results, TLE disease impacted several areas of the brain, including the temporal, parietal, occipital, posterior cingulate, angular gyrus, superior frontal gyrus, and cerebellum regions in degree centrality, flow coefficient and node efficiency. There was a significant correlation between performance IQ and the flow coefficient of the left posterior cerebellum lobe in TLE group.

The study confirmed the validity of Granger causality analysis in constructing directed brain networks. The proposed machine learning approach based on directed graph measures may serve as a biomarker for the diagnosis of TLE to assist in the early diagnosis of TLE patients and intervention in treatment plans.