Resting-state EEG dynamic functional connectivity distinguishes non-psychotic major depression, psychotic major depression and schizophrenia

• Published in: Molecular psychiatry Vol. 29; no. 4; pp. 1088 - 1098
• Main Authors: Chen, Hui, Lei, Yanqin, Li, Rihui, Xia, Xinxin, Cui, Nanyi, Chen, Xianliang, Liu, Jiali, Tang, Huajia, Zhou, Jiawei, Huang, Ying, Tian, Yusheng, Wang, Xiaoping, Zhou, Jiansong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.04.2024; Nature Publishing Group
• Subjects: 692/699/476/1414; 692/699/476/1799; Activity patterns; Adult; Behavioral Sciences; Biological Psychology; Biomarkers; Brain - physiopathology; Connectome - methods; Depressive Disorder, Major - physiopathology; EEG; Electroencephalography; Electroencephalography - methods; Female; Humans; Machine Learning; Male; Medicine; Medicine & Public Health; Mental depression; Mental disorders; Middle Aged; Nerve Net - diagnostic imaging; Nerve Net - physiopathology; Neural networks; Neurosciences; Pharmacotherapy; Psychiatry; Psychosis; Psychotic Disorders - diagnosis; Psychotic Disorders - physiopathology; Schizophrenia; Schizophrenia - physiopathology; Theta rhythms; Young Adult
• ISSN: 1359-4184; 1476-5578; 1476-5578
• DOI: 10.1038/s41380-023-02395-3

This study aims to identify dynamic patterns within the spatiotemporal feature space that are specific to nonpsychotic major depression (NPMD), psychotic major depression (PMD), and schizophrenia (SCZ). The study also evaluates the effectiveness of machine learning algorithms based on these network manifestations in differentiating individuals with NPMD, PMD, and SCZ. A total of 579 participants were recruited, including 152 patients with NPMD, 45 patients with PMD, 185 patients with SCZ, and 197 healthy controls (HCs). A dynamic functional connectivity (DFC) approach was employed to estimate the principal FC states within each diagnostic group. Incremental proportions of data (ranging from 10% to 100%) within each diagnostic group were used for variability testing. DFC metrics, such as proportion, mean duration, and transition number, were examined among the four diagnostic groups to identify disease-related neural activity patterns. These patterns were then used to train a two-layer classifier for the four groups (HC, NPMD, PMD, and SCZ). The four principal brain states (i.e., states 1,2,3, and 4) identified by the DFC approach were highly representative within and across diagnostic groups. Between-group comparisons revealed significant differences in network metrics of state 2 and state 3, within delta, theta, and gamma frequency bands, between healthy individuals and patients in each diagnostic group ( p  < 0.01, FDR corrected). Moreover, the identified key dynamic network metrics achieved an accuracy of 73.1 ± 2.8% in the four-way classification of HC, NPMD, PMD, and SCZ, outperforming the static functional connectivity (SFC) approach ( p  < 0.001). These findings suggest that the proposed DFC approach can identify dynamic network biomarkers at the single-subject level. These biomarkers have the potential to accurately differentiate individual subjects among various diagnostic groups of psychiatric disorders or healthy controls. This work may contribute to the development of a valuable EEG-based diagnostic tool with enhanced accuracy and assistive capabilities.