Prediction of seizure outcome following temporal lobectomy: A magnetoencephalography-based graph theory approach

• Published in: Seizure (London, England) Vol. 97; pp. 73 - 81
• Main Authors: 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, Arivazhagan, A, Karthik, K, Bharath, Rose D., Saini, Jitendra, Kandavel, Thennarasu, Rao, Malla Bhaskara, Sinha, Sanjib
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.04.2022
• Subjects: Anterior Temporal Lobectomy - adverse effects; Epilepsy, Temporal Lobe - surgery; Humans; Magnetoencephalography; Seizures - surgery; Treatment Outcome
• ISSN: 1059-1311; 1532-2688; 1532-2688
• DOI: 10.1016/j.seizure.2022.03.012

•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.