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The human brain is a directional network system of brain regions involvingdirectional connectivity. Seizures are a directional network phenomenon asabnormal neuronal activities start from a seizure onset zone (SOZ) andpropagate to otherwise healthy regions. To localize the SOZ of an epilepticpatient, clinicians use iEEG to record the patient's intracranial brainactivity in many small regions. iEEG data are high-dimensional multivariatetime series. We build a state-space multivariate autoregression (SSMAR) foriEEG data to model the underlying directional brain network. To producescientifically interpretable network results, we incorporate into the SSMAR thescientific knowledge that the underlying brain network tends to have a clusterstructure. Specifically, we assign to the SSMAR parameters astochastic-blockmodel-motivated prior, which reflects the cluster structure. Wedevelop a Bayesian framework to estimate the SSMAR, infer directionalconnections, and identify clusters for the unobserved network edges. The newmethod is robust to violations of model assumptions and outperforms existingnetwork methods. By applying the new method to an epileptic patient's iEEGdata, we reveal seizure initiation and propagation in the patient's brainnetwork. Our method can also accurately localize the SOZ. Overall, this paperprovides a tool to study the human brain network.

A Bayesian State-Space Approach to Mapping Directional Brain Networks