Directed Graph Analysis on Theta-Gamma Phase-Amplitude Coupling: Insights into Parkinson Disease EEG Data

Abstract

Understanding the complex neural interactions in the brain is crucial for advancing diagnostic and therapeutic strategies. Parkinson disease (PD) is a neurodegenerative disorder caused by dopamine deficit which affects the network-level performance across a large area in the brain. This study introduces a novel electroencephalogram (EEG) data analysis approach examining temporal dynamics of theta-gamma cross-frequency phase-amplitude coupling (PAC) across different brain regions through the use of directed graph networks. The approach is particularly developed to distinguish PD patients from healthy controls. We first measure the PAC between pairs of EEG channels to construct a directed graph which indicates directional interactions between different brain regions. Then, by analyzing the structural characteristics of this graph such as node clustering and effective path lengths across time, we propose graph features as diagnostic markers for classifying PD patients from healthy controls. The results demonstrate significant differences in the directed graphs of PD patients and controls, with altered path lengths and connectivity patterns suggesting disrupted neural communication. These findings underscore the potential for employing directed graph analysis on EEG data based on PAC for uncovering changes in neural mechanisms caused by neurological diseases such as PD.