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

by Viktor Sip, Meysam Hashemi, Anirudh N. Vattikonda, Marmaduke M. Woodman, Huifang Wang, Julia Scholly, Samuel Medina Villalon, Maxime Guye, Fabrice Bartolomei, Viktor K. Jirsa

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 incomplet e 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 Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the perfo rmed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual ep...

Source: PLoS Computational Biology - February 17, 2021 Category: Biology Authors: Viktor Sip Source Type: research