Network phenotypes and their clinical significance in temporal lobe epilepsy using machine learning applications to morphological and functional graph theory metrics.

ABSTRACT: Machine learning analyses were performed on graph theory (GT) metrics extracted from brain functional and morphological data from temporal lobe epilepsy (TLE) patients in order to identify intrinsic network phenotypes and characterize their clinical significance. Participants were 97 TLE and 36 healthy controls from the Epilepsy Connectome Project. Each imaging modality (i.e., Resting-state functional Magnetic Resonance Imaging (RS-fMRI), and structural MRI) rendered 2 clusters: one comparable to controls and one deviating from controls. Participants were minimally overlapping across the identified clusters, suggesting that an abnormal functional GT phenotype did not necessarily mean an abnormal morphological GT phenotype for the same subject. Morphological clusters were associated with a

SUBMITTER: Garcia-Ramos C 

PROVIDER: S-EPMC9402557 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature