A Bayesian State-Space Approach to Mapping Directional Brain Networks

TL;DR

This paper introduces a Bayesian state-space model with a cluster-informed prior to analyze directional brain networks from iEEG data, aiding in localizing seizure onset zones and understanding seizure propagation.

Contribution

It develops a novel Bayesian state-space autoregression model incorporating cluster structure for directional brain network inference from high-dimensional iEEG data.

Findings

Successfully localizes seizure onset zones in patient data

Reveals seizure propagation pathways

Outperforms existing network analysis methods

Abstract

The human brain is a directional network system of brain regions involving directional connectivity. Seizures are a directional network phenomenon as abnormal neuronal activities start from a seizure onset zone (SOZ) and propagate to otherwise healthy regions. To localize the SOZ of an epileptic patient, clinicians use iEEG to record the patient's intracranial brain activity in many small regions. iEEG data are high-dimensional multivariate time series. We build a state-space multivariate autoregression (SSMAR) for iEEG data to model the underlying directional brain network. To produce scientifically interpretable network results, we incorporate into the SSMAR the scientific knowledge that the underlying brain network tends to have a cluster structure. Specifically, we assign to the SSMAR parameters a stochastic-blockmodel-motivated prior, which reflects the cluster structure. We develop a Bayesian framework to estimate the SSMAR, infer directional connections, and identify clusters for the unobserved network edges. The new method is robust to violations of model assumptions and outperforms existing network methods. By applying the new method to an epileptic patient's iEEG data, we reveal seizure initiation and propagation in the patient's brain network. Our method can also accurately localize the SOZ. Overall, this paper provides a tool to study the human brain network.