Connectome-based prediction of cognitive performance in patients with temporal lobe epilepsy.

Abstract

Temporal lobe epilepsy (TLE) patients often exhibit varying degrees of cognitive impairments. This study aims to predict cognitive performance in TLE patients by applying a connectome-based predictive model (CPM) to whole-brain resting-state functional connectivity (RSFC) data. A CPM was established and leave-one-out cross-validation was employed to decode the cognitive performance of patients with TLE based on the whole-brain RSFC. Our findings indicate that cognitive performance in TLE can be predicted through the internal and network connections of the parietal lobe, limbic lobe, and cerebellum systems. These systems play crucial roles in cognitive control, emotion processing, and social perception and communication, respectively. In the subgroup analysis, CPM successfully predicted TLE patients with and without focal to bilateral tonic-clonic seizures (FBCTS). Additionally, significant differences were noted between the two TLE patient groups and the normal control group. This data-driven approach provides evidence for the potential of predicting brain features based on the inherent resting-state brain network organization. Our study offers an initial step towards an individualized prediction of cognitive performance in TLE patients, which may be beneficial for diagnosis, prognosis, and treatment planning.