Graph theory analysis of functional connectivity combined with machine learning approaches demonstrate widespread network differences and predict clinical variables in temporal lobe epilepsy.

Understanding how global brain networks are affected in epilepsy may elucidate the pathogenesis of seizures and its accompanying neurobehavioral comorbidities. We investigated functional changes within neural networks in temporal lobe epilepsy (TLE) using graph theory analysis of resting-state connectivity. 27 TLE presurgical patients (age 41.0 ± 12.3 years), and 85 age, gender and handedness equivalent healthy controls (HC) (age 39.7±16.9 years) were enrolled. Eyes-closed resting-state fMRI scans were analyzed to compare network properties and functional connectivity changes. TLE subjects showed significantly higher global efficiency, lower clustering coefficient ratio and lower shortest path lengths ratio compared to HC, as an indication of a more synchronized, yet less segregated network. A trend of functional reorganization with a shift of network hubs to the contralateral hemisphere was noted in TLE subjects. Support vector machine (SVM) with linear kernel was trained to separate between neural networks in TLE and HC subjects based on graph measurements. SVM analysis allowed separation between TLE and HC networks with 80.66% accuracy using 8 features of graph measurements. Support vector regression (SVR) was used to predict neurocognitive performance from graph metrics. A SVR linear predictor showed discriminative prediction accuracy for 4 key neurocognitive variables in TLE (absolute R-value range: 0.61 - 0.75) Despite temporal onset of epilepsy, our results showed both local and global network topology differences that reflect widespread alterations in functional connectivity in TLE. Network differences are discriminative between TLE and HCs using data driven analysis and predicted severity of neurocognitive sequelae in our cohort.