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

Publisher: Nature Publishing Group Specialist Journals Country of Publication: England NLM ID: 9607835 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-5578 (Electronic) Linking ISSN: 13594184 NLM ISO Abbreviation: Mol Psychiatry Subsets: MEDLINE

Publication: 2000- : Houndmills, Basingstoke, UK : Nature Publishing Group Specialist Journals
Original Publication: Houndmills, Hampshire, UK ; New York, NY : Stockton Press, c1996-

Depressive Disorder, Major*/physiopathology ; Schizophrenia*/physiopathology ; Electroencephalography*/methods ; Brain*/physiopathology; Humans ; Male ; Female ; Adult ; Middle Aged ; Machine Learning ; Psychotic Disorders/physiopathology ; Psychotic Disorders/diagnosis ; Connectome/methods ; Young Adult ; Nerve Net/physiopathology ; Nerve Net/diagnostic imaging

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.
(© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

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202103091470 Human Health Foundation (HHF); 82071543 National Natural Science Foundation of China (National Science Foundation of China); 82171509 National Natural Science Foundation of China (National Science Foundation of China)

Date Created: 20240124 Date Completed: 20240613 Latest Revision: 20240613

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10.1038/s41380-023-02395-3

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