BMC Psychiatry· 2026Q1
EEG microstate dynamics and source-space functional connectivity features for machine learning classification of major depressive disorder
- 1citations
- Q1SCImago
- 2026year
Short summary
A combination of EEG microstate transitions (increased D to B probability) and altered default mode/salience network connectivity achieved 81.0% accuracy in classifying major depressive disorder (MDD) patients from healthy controls using an SVM.
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Key points
- MDD patients showed a significant increase in the transition probability from EEG microstate D to B (PFDR = 0.048) compared to healthy controls.
- Altered functional connectivity was observed within the default mode network (DMN) and between the DMN and salience network (SN) in MDD patients across multiple frequency bands (delta, theta, alpha, beta).
- A support vector machine (SVM) classifier achieved 66.7% accuracy using only microstate features, 76.2% using only functional connectivity features, and 81.0% using a combination of both.
- The findings suggest that integrating EEG microstate dynamics and source-space functional connectivity offers a promising approach for the objective diagnosis of MDD.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract Background: Diagnosis of depressive disorders (DD) primarily heavily relies on clinical interviews and subjective symptom reporting, highlighting the urgent need for objective biomarkers. This study aimed to identify novel electroencephalogram (EEG) features, utilizing high-density EEG and source localization techniques, to aid in the diagnosis of DD. Methods: Resting-state EEG data were collected from 115 patients with DD and 43 healthy controls (HCs). Microstate analysis and functional connectivity (FC) analysis were performed to extract EEG features. Statistical analyses were conducted to compare group differences, and a support vector machine (SVM) algorithm was applied to evaluate the diagnostic accuracy of these features. Results: Compared to HCs, DD patients showed a significant increased transition probability from microstate D to B (PFDR = 0.048). Additionally, significant elevations in FC within specific regions of the default mode network (DMN) were observed in the delta-band, theta-band, and beta-band (P < 0.05), as well as between parts of the DMN and the salience network (SN) in the theta-band and alpha-band (P < 0.05). However, none of these FC features survived after FDR correction (PFDR > 0.05). The classification accuracies for distinguishing DD patients from HCs using the SVM classifier were 66.7%, 76.2%, and 81.0% based on microstate features, FC features, and a combination of both, respectively. Conclusions: Patients with DD exhibited distinctive microstates and atypical alterations in brain network connectivity. Integrating these features with machine learning algorithms offers a promising approach to improving the objective diagnosis of DD. Trial registration: The study was registered on http://www.chictr.org.cn/ and the registration number was ChiCTR2200057365 (registration date: March 9, 2022).
The authors' abstract, as published at the source. BMC Psychiatry, 2026 · DOI ↗
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Field: Cognitive Neuroscience
Cognitive NeuroscienceNeuroscience