Prefrontal cortex markers of suicidal vulnerability in mood disorders: a model-based structural neuroimaging study with a translational perspective
Bibliographic record
Abstract
The vulnerability to suicidal behavior has been modeled in deficits in both valuation and cognitive control processes, mediated by ventral and dorsal prefrontal cortices. To uncover potential markers of suicidality based on this model, we measured several brain morphometric parameters using 1.5T magnetic resonance imaging in a large sample and in a specifically designed study. We then tested their classificatory properties. Three groups were compared: euthymic suicide attempters with a past history of mood disorders and suicidal behavior (N=67); patient controls with a past history of mood disorders but not suicidal behavior (N=82); healthy controls without any history of mental disorder (N=82). A hypothesis-driven region-of-interest approach was applied targeting the orbitofrontal cortex (OFC), ventrolateral (VLPFC), dorsal (DPFC) and medial (including anterior cingulate cortex; MPFC) prefrontal cortices. Both voxel-based (SPM8) and surface-based morphometry (Freesurfer) analyses were used to comprehensively evaluate cortical gray matter measure, volume, surface area and thickness. Reduced left VLPFC volume in attempters vs both patient groups was found (P=0.001, surviving multiple comparison correction, Cohen's d=0.65 95% (0.33-0.99) between attempters and healthy controls). In addition, reduced measures in OFC and DPFC, but not MPFC, were found with moderate effect sizes in suicide attempters vs healthy controls (Cohen's d between 0.34 and 0.52). Several of these measures were correlated with suicidal variables. When added to mood disorder history, left VLPFC volume increased within-sample specificity in identifying attempters in a significant but limited way. Our study, therefore, confirms structural prefrontal alterations in individuals with histories of suicide attempts. A future clinical application of these markers will, however, necessitate further research.
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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 | 0.001 | 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 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".