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

Abstract

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.

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Fig. 1: Brain state results.
Fig. 2: Validation analysis results for reproducibility of the estimated distinct states within diagnosis and across diagnosis.
Fig. 3: The importance score of DFC features.
Fig. 4: Classification results.
Fig. 5: Dynamic functional characteristics of brain states.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors are grateful to the participants for contributing to this research.

Funding

This work was supported by the National Natural Science Foundation of China (82071543, 82171509), the Key Research and Development Program of Hunan Province (2023SK2028), the Key Guiding Project of Hunan Health Committee (202103091470), STI2030-Major Projects-2021ZD0200700 and the Fundamental Research Funds for the Central Universities of Central South University (2022ZZTS0858).

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HC, YL and JZ conceived and designed the study. XC, JL, HT, YT, YG and JZ participated in the acquisition of data. YL, XX, and NC analyzed the data. HC and YL drafted the manuscript, RL, NC, JZ and XW revised the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xiaoping Wang or Jiansong Zhou.

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Yanqin Lei, Xinxin Xia, and Nanyi Cui report salary from TeleBrain Medical Technology. The authors declare no conflict of interest. All the funding sources listed had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

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Chen, H., Lei, Y., Li, R. et al. Resting-state EEG dynamic functional connectivity distinguishes non-psychotic major depression, psychotic major depression and schizophrenia. Mol Psychiatry 29, 1088–1098 (2024). https://doi.org/10.1038/s41380-023-02395-3

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