Virtual resection predicts surgical outcome for drug-resistant epilepsy

作者：Lohith G. Kini, John M. Bernabei, Fadi Mikhail, Peter Hadar, Preya Shah, Ankit N. Khambhati, Kelly Oechsel, Ryan Archer, Jacqueline Boccanfuso, Erin C. Conrad, Russell T. Shinohara, Joel M Stein, Sandhitsu R. Das, Ammar Kheder, Timothy H. Lucas, Kathryn A. Davis, Danielle S. Bassett, Brian Litt · 发表于：Brain · 年份：2019 · DOI：10.1093/brain/awz303 · 被引用次数：144 · 研究领域：Epilepsy research and treatment、Functional Brain Connectivity Studies、EEG and Brain-Computer Interfaces

Patients with drug-resistant epilepsy often require surgery to become seizure-free. While laser ablation and implantable stimulation devices have lowered the morbidity of these procedures, seizure-free rates have not dramatically improved, particularly for patients without focal lesions. This is in part because it is often unclear where to intervene in these cases. To address this clinical need, several research groups have published methods to map epileptic networks but applying them to improve patient care remains a challenge. In this study we advance clinical translation of these methods by: (i) presenting and sharing a robust pipeline to rigorously quantify the boundaries of the resection zone and determining which intracranial EEG electrodes lie within it; (ii) validating a brain network model on a retrospective cohort of 28 patients with drug-resistant epilepsy implanted with intracranial electrodes prior to surgical resection; and (iii) sharing all neuroimaging, annotated electrophysiology, and clinical metadata to facilitate future collaboration. Our network methods accurately forecast whether patients are likely to benefit from surgical intervention based on synchronizability of intracranial EEG (area under the receiver operating characteristic curve of 0.89) and provide novel information that traditional electrographic features do not. We further report that removing synchronizing brain regions is associated with improved clinical outcome, and postulate that sparing...