EEG microstate and functional connectivity analyses for differentiating suicide attempt from suicidal ideation in major depressive disorder
Bibliographic record
Abstract
Background: Suicide remains a critical public health issue, with self-report-based clinical assessments often failing to detect imminent risk. This study aimed to identify objective electroencephalography (EEG)-based neurobiological markers for differentiating a suicide attempt (SA) from suicidal ideation (SI) using EEG microstate and microstate-based functional connectivity (FC) analyses. Methods: From 2017 to 2020, this study enrolled 130 medication-naïve major depressive disorder patients (68 SA, evaluated within 7 days of the attempt; 62 SI) at Soonchunhyang University Cheonan Hospital. Resting-state EEG data were analyzed using microstate analysis to explore temporal dynamics of brain topography and microstate-based FC to assess connectivity in theta, alpha, and beta bands. Correlations between EEG features and psychological measures (e.g., suicidal ideation, depression, emotion regulation) were examined. Results: Compared with the SI group, the SA group showed a marginally lower frequency of occurrence for microstates A (auditory/language processing) and B (visual processing) ( p = 0.078 for both). The SA group demonstrated significantly higher alpha-band FC during microstate E (linked to the default mode network (DMN)) for several electrode pairs (e.g., F7–C5, p = 0.009; FC5–C5, p = 0.005). The SA group also exhibited marginally higher FC in the alpha band during microstates C (DMN-related) and B, and in the theta band during microstate E. A subsequent within-group analysis revealed that in the SI group, alpha-band FC during microstate E positively correlated with scores for difficulties in emotion regulation ( r = 0.433, p = 0.017). Limitations: Findings are limited by potential physiological confounds in the SA group and by the limited anatomical specificity inherent in sensor-space EEG analysis. Conclusion: EEG microstate dynamics and microstate-based FC differ between patients with SA and SI. Specifically, enhanced alpha-band connectivity during microstate E in the SA group potentially reflects condition-specific DMN functions. These EEG-based measures show promise as objective markers that complement clinical suicide risk assessment and inform early intervention strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".