Changes in oscillatory patterns of microstate sequence in patients with first-episode psychosis

• Published in: Scientific data Vol. 11; no. 1; p. 38
• Main Authors: Zhou, Dong-Dong, Li, Hong-Zhi, Wang, Wo, Kuang, Li
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 05.01.2024; Nature Portfolio
• Subjects: Fc receptors; Humans; Learning algorithms; Machine Learning; Mental disorders; Patients; Psychosis; Psychotic Disorders; Time Factors; Time series
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-023-02892-8

We aimed to utilize chaos game representation (CGR) for the investigation of microstate sequences and explore its potential as neurobiomarkers for psychiatric disorders. We applied our proposed method to a public dataset including 82 patients with first-episode psychosis (FEP) and 61 control subjects. Two time series were constructed: one using the microstate spacing distance in CGR and the other using complex numbers representing the microstate coordinates in CGR. Power spectral features of both time series and frequency matrix CGR (FCGR) were compared between groups and employed in a machine learning application. The four canonical microstates (A, B, C, and D) were identified using both shared and separate templates. Our results showed the microstate oscillatory pattern exhibited alterations in the FEP group. Using oscillatory features improved machine learning performance compared with classical features and FCGR. This study opens up new avenues for exploring the use of CGR in analyzing EEG microstate sequences. Features derived from microstate sequence CGR offer fine-grained neurobiomarkers for psychiatric disorders.