Predicting Surgical Outcome in Patients With Drug-Resistant Epilepsy Using Autoregressive Connectivity and Virtual Resection.

Abstract

Epilepsy is a brain network disorder that manifests through recurrent seizures. In cases of drug-resistant epilepsy, surgical removal of pivotal nodes within the epileptic brain network can lead to seizure freedom. Virtual resection on patient-specific brain network models can aid in the prediction of surgical outcomes. Some studies have investigated the virtual resection on undirected brain connectivity networks, such as using Pearson correlation or structural connectivity. We hypothesize that the directed functional connectivity enhances prediction performance. This study proposes a new approach for surgical outcome prediction by applying virtual resection of autoregressive (AR) connectivity networks in epilepsy patients. Intracranial EEG recordings from 16 drug-resistant epilepsy patients were analyzed. The performance of the proposed approach was evaluated based on patients' surgical volumes and prognosis outcome. We compared the performance of the AR connectivity with six other measures and concurrently explored three distinct neural mass models. The results show that virtual resection on AR connectivity demonstrated predictive accuracy at 87.5% when paired with the bistable neural mass model. Notably, all eight patients with poor outcomes were accurately identified. In addition, our data shows that the estimated epileptic network is relatively stable during the interictal interval. Leveraging the AR model results in estimated directional connectivity among epileptic brain regions, which can then be used effectively for virtual resection. Our approach offers a promising avenue for clinicians in preoperative evaluation and augments existing clinical methodologies.