A Hierarchical Random Effects State-space Model for Modeling Brain Activities from Electroencephalogram Data

TL;DR

This paper introduces a hierarchical random effects state-space model (RESSM) for analyzing multi-channel EEG data, capturing heterogeneity and nonstationarity in brain connectivity patterns to improve understanding and prediction of mental disorders like MDD.

Contribution

The novel RESSM model accounts for heterogeneity and temporal dynamics in EEG data using multi-level random effects within a Bayesian framework, addressing limitations of previous models.

Findings

RESSM uncovers significant differences in brain dynamics between MDD patients and healthy controls.

EEG features derived from RESSM better predict treatment effects than traditional frequency band power.

Simulation studies validate the model's ability to accurately estimate complex brain connectivity patterns.

Abstract

Mental disorders present challenges in diagnosis and treatment due to their complex and heterogeneous nature. Electroencephalogram (EEG) has shown promise as a potential biomarker for these disorders. However, existing methods for analyzing EEG signals have limitations in addressing heterogeneity and capturing complex brain activity patterns between regions. This paper proposes a novel random effects state-space model (RESSM) for analyzing large-scale multi-channel resting-state EEG signals, accounting for the heterogeneity of brain connectivities between groups and individual subjects. We incorporate multi-level random effects for temporal dynamical and spatial mapping matrices and address nonstationarity so that the brain connectivity patterns can vary over time. The model is fitted under a Bayesian hierarchical model framework coupled with a Gibbs sampler. Compared to previous mixed-effects state-space models, we directly model high-dimensional random effects matrices without structural constraints and tackle the challenge of identifiability. Through extensive simulation studies, we demonstrate that our approach yields valid estimation and inference. We apply RESSM to a multi-site clinical trial of Major Depressive Disorder (MDD). Our analysis uncovers significant differences in resting-state brain temporal dynamics among MDD patients compared to healthy individuals. In addition, we show the subject-level EEG features derived from RESSM exhibit a superior predictive value for the heterogeneous treatment effect compared to the EEG frequency band power, suggesting the potential of EEG as a valuable biomarker for MDD.