TMS-EEG Biomarkers of Suicidal Ideation
Bibliographic record
Abstract
Suicidal ideation (SI) has a lifetime prevalence of 10%, is associated with severe psychiatric illness, and is a leading risk factor for suicide.Despite this societal burden, the neurophysiological basis of SI remains poorly understood.In a recent systematic review exploring interventions for SI, we found preliminary evidence suggesting an association between changes in SI and activity in the prefrontal and anterior cingulate cortices.However, included studies lacked methodological standardization and averaged 35 participants.To expand our understanding of SI, we are presently conducting a large analysis of neurophysiological biomarkers of SI utilizing concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG).For this ongoing study, cross-sectional singlepulse TMS-EEG targeting the left dorsolateral prefrontal cortex (LDLPFC) were aggregated from five clinical trials across three sites.Data preprocessing was standardized using an automated pipeline.Of 239 patients diagnosed with treatment-resistant depression (TRD), 25 were excluded from the analysis following independent quality control of the TMSevoked potentials (TEP) by three researchers.SI was quantified using the suicide item derived from either the Hamilton Depression Rating Scale or the Mongomery-Asberg Depression Rating Scale.Patients were grouped into: 1) no suicidal ideation (n77), 2) wish to die without active suicidal thoughts (n77), and 3) thoughts of suicide and/or suicide plans (n60).Preliminary analysis of the area under the curve (AUC) from 25 to 275 ms demonstrated no significant differences between groups for both the Global Mean Field Amplitude (GMFA) and signal in the LDLPFC (F3, F5, FC3, and FC5), according to a one-way analysis of variance (ANOVA).Future planned analyses include TEP peak, time-frequency dynamics, and connectivity patterns.The results of this large-scale, multi-site secondary analysis comparing TMS-evoked EEG potentials in TRD patients with varying levels of SI may inform the development of objective, neurophysiologically-informed diagnostic tools, and novel targets for precision treatments for SI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".