Long-term seizure recurrence after temporal lobe epilepsy surgery is linked to disruption in brain network hubs, notably the hippocampi and dorsal attention network.
Combining structural and functional MRI-derived connectomes enhances the accuracy of predicting seizure recurrence over clinical variables alone.
Machine learning models using normative hub disruption metrics demonstrate high specificity and have potential for clinical risk stratification post-surgery.
