Prediction of seizure outcome following temporal lobectomy: A magnetoencephalography-based graph theory approach - Mukherjee, Joydeep; Kenchaiah, Raghavendra; Gautham, Bhargava K; Narayanan, Chitra; Afsar, Mohammed; Narayanan, Mariyappa; Rajeswaran, Jamuna; Asranna, Ajay; Mundlamuri, Ravindranadh C; Viswanathan, Lakshminarayanapuram G; Mahadevan, Anita; Sadashiva, Nishanth; et al. (Seizure (London, England), 2022)
•Epilepsy is associated with alteration in large-scale functional networks.•We evaluated MEG based multi-band difference in resting sate brain networks between TLE subjects who underwent epilepsy surgery.•TLE-MTS patients with good seizure-outcome demonstrated increased global GT measures in the broadband compared to patients with poor seizure outcome.•Non–MTS lesional-TLE demonstrated distinct pattern of network connectivity in good and poor outcome groups.•MEG based resting state network analysis using graph theory helps in predicting surgical outcome and could emerge as a novel bio-marker in epilepsy surgery.