Virtual resection predicts surgical outcome for drug-resistant epilepsy

Abstract

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 desynchronizing regions may further be beneficial. Our findings suggest that data-driven network-based methods can identify patients likely to benefit from resective or ablative therapy, and perhaps prevent invasive interventions in those unlikely to do so.

Removal of Interictal MEG-Derived Network Hubs Is Associated With Postoperative Seizure Freedom

“…Our approach builds on our previous work with intracranial EEG, where we show that patients generally have better seizure outcomes when high-strength nodes are surgically removed (27)(28)(29)(30). Other groups have reported similar findings from intracranial EEG (31)(32)(33)(34)(35)(36)(37). With MEG data, Nissen et al (9) showed in a cohort of 22 patients, that interictal source localized network hubs overlapped with the resection in seizure-free patients only.…”

The value of intra-operative electrographic biomarkers for tailoring during epilepsy surgery: from group-level to patient-level analysis

“…higher value than the threshold) is enough for ictogenesis. The maximum as a statistic may overlook important global network features that go beyond single channel statistics, as critical mass in terms of epileptogenic tissue that is needed to trigger seizures [79][80][81][82][83][84][85][86] . Moreover, choosing only one channel makes the method sensitive to artefacts not recognized during the pre-processing.…”

Temporal Lobe Epilepsy Surgical Outcomes Can Be Inferred Based on Structural Connectome Hubs: A Machine Learning Study

“…There is a robust overlap between abnormally strong connections and increased "centrality" of ictal-onset zones to sites of noncontiguous spread in the structural connectome along with increased amplitude of corticocortical evoked potentials to those same distant sites (effective connectome). 16 Seizure freedom is seen in association with statistical models that virtually resect hyperconnected 17 and hypersynchronized 18 network hubs. Moreover, postoperatively, node-based measures of centrality rather than global network measures are associated with postoperative outcomes.…”