Neuroimaging-informed phenotypes of suicidal behavior: a family history of suicide and the use of a violent suicidal means
Bibliographic record
Abstract
The identification of brain markers of suicidal risk is highly expected. However, neuroimaging studies have yielded mixed results, possibly due to phenotypic heterogeneity. In the present study, we addressed this issue using structural brain imaging. First, two independent samples of suicide attempters (n = 17 in Montreal, 32 in Jena), patient controls (n = 26/34), and healthy controls (n = 66/34) were scanned with magnetic resonance imaging. Groups were compared with FSL. We then reviewed the literature and run a GingerALE meta-analysis of 12 structural imaging studies comparing suicide attempters and patient controls with whole-brain analyses (n = 693). Finally, we explored the potential contribution of two variables previously associated with biological/cognitive deficits: a family history of suicide (FHoS), and the use of a violent suicidal means (VSM). Here, we added two groups of healthy first-degree biological relatives of suicide victims and depressed patients (n = 32). When comparing all suicide attempters and controls, very limited between-group differences were found in the two samples, and none in the meta-analysis. In contrast, a FHoS was associated with reduced volumes in bilateral temporal regions, right dorsolateral prefrontal cortex, and left putamen, several of these differences being observed across groups. VSM was associated with increased bilateral caudate (and left putamen) volumes. Some morphometric variations in cortico-subcortical networks may therefore be endophenotypes increasing the suicidal vulnerability, while others (notably in striatum) may modulate action selection. These results therefore confirm at the neural level two phenotypes at high lethal risk with a strong biological background, and uncover motives of heterogeneous findings in neuroimaging studies of suicidal behavior.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".