High-Dimensional Directional Brain Network Analysis for Focal Epileptic Seizures
Yaotian Wang, Guofen Yan, Seiji Tanabe, Chang-Chia Liu, Shayan Moosa,, Mark S. Quigg, Tingting Zhang

TL;DR
This paper introduces a novel high-dimensional directional brain network analysis method using MARSS to study focal epileptic seizures, enabling precise SOZ localization and understanding seizure dynamics.
Contribution
The study develops a clustering-enabled multivariate autoregressive state-space model (MARSS) for high-dimensional directional brain network analysis in epilepsy, addressing previous low-dimensional limitations.
Findings
Increased directional connections in SOZ after seizure onset
Regions outside SOZ show no change in directional connections
Achieved 100% true positive and less than 3% false positive SOZ localization
Abstract
The brain is a high-dimensional directional network system consisting of many regions as network nodes that influence each other. The directional influence from one region to another is referred to as directional connectivity. Epilepsy is a directional network disorder, as epileptic activity spreads from a seizure onset zone (SOZ) to many other regions after seizure onset. However, directional network studies of epilepsy have been mostly limited to low-dimensional directional networks between the SOZ and contiguous regions due to the lack of efficient methods for analyzing high-dimensional directional brain networks. To address this gap, we study high-dimensional directional networks in epileptic brains by using a clustering-enabled multivariate autoregressive state-space model (MARSS) to analyze multi-channel intracranial EEG recordings of focal seizures. This new MARSS characterizes…
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Taxonomy
TopicsNeural dynamics and brain function · Functional Brain Connectivity Studies · Fractal and DNA sequence analysis
