Identifying Seizure Onset Zone from the Causal Connectivity Inferred Using Directed Information

TL;DR

This paper introduces new estimators for directed information to infer causal brain connectivity from ECoG signals, enabling more accurate seizure onset zone identification, which could improve epilepsy treatment.

Contribution

It develops both model-based and data-driven estimators for directed information and demonstrates their effectiveness in identifying seizure onset zones from ECoG data.

Findings

Data-driven SOZ identification outperforms model-based methods.

Proposed estimators are almost surely convergent.

Causal connectivity analysis aids in non-surgical epilepsy treatments.

Abstract

In this paper, we developed a model-based and a data-driven estimator for directed information (DI) to infer the causal connectivity graph between electrocorticographic (ECoG) signals recorded from brain and to identify the seizure onset zone (SOZ) in epileptic patients. Directed information, an information theoretic quantity, is a general metric to infer causal connectivity between time-series and is not restricted to a particular class of models unlike the popular metrics based on Granger causality or transfer entropy. The proposed estimators are shown to be almost surely convergent. Causal connectivity between ECoG electrodes in five epileptic patients is inferred using the proposed DI estimators, after validating their performance on simulated data. We then proposed a model-based and a data-driven SOZ identification algorithm to identify SOZ from the causal connectivity inferred using model-based and data-driven DI estimators respectively. The data-driven SOZ identification outperforms the model-based SOZ identification algorithm when benchmarked against visual analysis by neurologist, the current clinical gold standard. The causal connectivity analysis presented here is the first step towards developing novel non-surgical treatments for epilepsy.