Association of resting-state EEG with suicidality in depressed patients: a systematic review
Bibliographic record
Abstract
OBJECTIVE: The incidence of suicide is high among adolescents and young adults, especially those suffering from psychiatric diseases. Because of the reported association between depression and suicidality, exploring suicide risk factors in depressed patients is crucial for the identification of those at high risk and preventing suicide. In recent decades, electroencephalography parameters have been considered for identifying biomarkers of suicide ideation and attempts in depressed patients. This study aimed to review the available literature on resting-state EEG for suicidality in depressed patients. METHOD: A systematic search was performed in five electronic databases, including APA PsycINFO, Embase, Medline (via PubMed), Scopus, and Web of Science. Papers with full text available in English in which resting-state EEG was evaluated in depressed patients with suicide ideation or suicide attempts compared to a control group of healthy subjects or non-suicidal depressed patients were included. The risk of bias was assessed by using the Newcastle-Ottawa scale. RESULTS: A total of 4665 references were retrieved from five electronic databases from which eleven studies were included in this systematic review. A meta-analysis was not performed due to the substantial heterogeneity of the studies. Five of the eleven reviewed papers were classified as high-quality, and six had moderate quality. CONCLUSIONS: According to the included studies in this review, the EEG signals of depressed patients with suicide ideation or suicide attempts may be different from patients with low risk of suicidality or healthy subjects. Connectivity measures sound more promising parameters than the power spectral analysis and EEG asymmetry. PROTOCOL REGISTRATION: The protocol of this review was registered in PROSPERO (No. CRD42024502056).
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".