Bick, Sarah K

Abstract How do brain networks limit seizure activity? In the interictal suppression hypothesis, we recently postulated that high inward connectivity to seizure onset zones (SOZs) from non-involved zones (NIZs) is a sign of broader network suppression. If broad networks appear to be responsible for interictal SOZ suppression, what changes during seizure initiation, spread and termination? For patients with drug-resistant epilepsy, intracranial monitoring offers a view into the electrographic networks that organize around and in response to the SOZ. In this manuscript, we investigate network dynamics in the peri-ictal periods to assess possible mechanisms of seizure suppression and the consequences of this suppression being overwhelmed. Peri-ictal network dynamics were derived from stereo electroencephalography recordings from 75 patients with drug-resistant epilepsy undergoing presurgical evaluation at Vanderbilt University Medical Center. We computed directed connectivity from 5 s windows in the periods between, immediately before, during and after 668 seizures. We aligned connectivity matrices across seizures and patients, then calculated net connectivity changes from the SOZ, propagative zone and NIZ. Across all seizure types, we observed two phases: a rapid increase in directed communication towards the SOZ, followed by a collapse in network connectivity. During the first phase, SOZs could be distinguished from all other regions (one-way ANOVA, P = 8.32 × 10−19–2.22 × 10−7). In the second phase and post-ictal period, SOZ inward connectivity decreased yet remained distinct (one-way ANOVA, P = 2.58 × 10−10–1.66 × 10−2). NIZs appeared to drive increased SOZ connectivity while intra-NIZ connectivity decreased concordantly. Stratifying by seizure subtype, we found that consciousness-impairing seizures decrease inward connectivity from the NIZ earlier than consciousness-sparing seizures (one-way ANOVA, P < 0.01 after false discovery correction). Tracking network reorganization against a surrogate for seizure involvement highlighted a possible antagonism between seizure propagation and the ability of the NIZ to maintain high connectivity to the SOZ. Finally, we found that inclusion of peri-ictal connectivity improved the accuracy of SOZ classification from previous models to a combined area under the curve of 93%. Overall, NIZs appear actively to increase inhibitory signaling towards the SOZ during seizure onset, possibly to thwart seizure activity. Although inhibition appears insufficient to prevent seizure onset, the inability to restrict seizure propagation might contribute to loss of consciousness during larger seizures. Dynamic connectivity patterns uncovered in this work might: (i) facilitate accurate delineation of surgical targets in focal epilepsy; (ii) reveal why interictal suppression of SOZs might be insufficient to prevent all seizures; and (iii) provide insight into mechanisms of loss of consciousness during certain seizures.

AbstractObjectiveEpilepsy is a debilitating disorder affecting more than 50 million people worldwide, and one third of patients continue to have seizures despite maximal medical management. If patients' seizures localize to a discrete brain region, termed a seizure onset zone, resection may be curative. Localization is often confirmed with stereotactic electroencephalography; however, this may require patients to stay in the hospital for weeks to capture spontaneous seizures. Automated localization of seizure onset zones could therefore improve presurgical evaluation and decrease morbidity.MethodsUsing more than 1 000 000 interictal stereotactic electroencephalography segments collected from 78 patients, we performed five‐fold cross‐validation and testing on a multichannel, multiscale, one‐dimensional convolutional neural network to classify seizure onset zones.ResultsAcross held‐out test sets, our models achieved a seizure onset zone classification sensitivity of .702 (95% confidence interval [CI] = .549–.805), specificity of .741 (95% CI = .652–.835), and accuracy of .738 (95% CI = .687–.795), which was significantly better than models trained on random labels. The models performed well across the entire brain, with top five region performance demonstrating accuracies between 70.0% and 88.4%. When split by outcomes, the models performed significantly better on patients with favorable Engel outcomes after resection or who were responsive neurostimulation responders. Finally, SHAP (Shapley Additive Explanation) value analysis on median‐normalized input data assigned consistently high feature importance to interictal spikes and large deflections, whereas similar analyses on histogram‐equalized data revealed differences in feature importance assignments to low‐amplitude segments.SignificanceThis work serves as evidence that deep learning on brief interictal intracranial data can classify seizure onset zones across the brain. Furthermore, our findings corroborate current understandings of interictal epileptiform discharges and may help uncover novel interictal morphologies. Clinical application of our models may reduce dependence on recorded seizures for localization and shorten presurgical evaluation time for drug‐resistant epilepsy patients, reducing patient morbidity and hospital costs.