Disentangling the Gordian Knot of Drug-Resistant Epilepsy

• Type of Material: Article
• Authors: Ioannis Karakis, Emory University
• Language: English
• Date: 2021-06-03
• Publisher: SAGE PUBLICATIONS INC
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2021
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1177/15357597211021020
• Title of Journal or Parent Work: EPILEPSY CURRENTS
• Volume: 21
• Issue: 5
• Start Page: 323
• End Page: 325
• Keywords: Science & Technology; Life Sciences & Biomedicine; Neurosciences & Neurology; Clinical Neurology
• Research Categories: Biology, Neuroscience; Psychology, Physiological; Chemistry, Pharmaceutical

Abstract

Structural Brain Network Abnormalities and the Probability of Seizure Recurrence After Epilepsy Surgery Sinha N, Wang Y, Moreira da Silva N, et al. Neurology. 2021;96(5):e758-e771. doi:10.1212/WNL.0000000000011315 Objective: We assessed preoperative structural brain networks and clinical characteristics of patients with drug-resistant temporal lobe epilepsy (TLE) to identify correlates of postsurgical seizure recurrences. Methods: We examined data from 51 patients with TLE who underwent anterior temporal lobe resection (ATLR) and 29 healthy controls. For each patient, using the preoperative structural, diffusion, and postoperative structural magnetic resonance imaging, we generated 2 networks: presurgery network and surgically spared network. Standardizing these networks with respect to controls, we determined the number of abnormal nodes before surgery and expected to be spared by surgery. We incorporated these 2 abnormality measures and 13 commonly acquired clinical data from each patient into a robust machine learning framework to estimate patient-specific chances of seizures persisting after surgery. Results: Patients with more abnormal nodes had a lower chance of complete seizure freedom at 1 year, and, even if seizure-free at 1 year, were more likely to relapse within 5 years. The number of abnormal nodes was greater and their locations more widespread in the surgically spared networks of patients with poor outcome than in patients with good outcome. We achieved an area under the curve of 0.84 ± 0.06 and specificity of 0.89 ± 0.09 in predicting unsuccessful seizure outcomes (International League Against Epilepsy [ILAE] 3-5) as opposed to complete seizure freedom (ILAE 1) at 1 year. Moreover, the model-predicted likelihood of seizure relapse was significantly correlated with the grade of surgical outcome at year 1 and associated with relapses up to 5 years after surgery. Conclusion: Node abnormality offers a personalized, noninvasive marker that can be combined with clinical data to better estimate the chances of seizure freedom at 1 year and subsequent relapse up to 5 years after ATLR. Classification of evidence: This study provides class II evidence that node abnormality predicts postsurgical seizure recurrence.