Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography.

ABSTRACT: Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesia

SUBMITTER: Sip V 

PROVIDER: S-EPMC7920393 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature