The neuroscience of suicidal behaviors: what can we expect from endophenotype strategies?
Bibliographic record
Abstract
Vulnerability to suicidal behavior (SB) is likely mediated by an underlying genetic predisposition interacting with environmental and probable epigenetic factors throughout the lifespan to modify the function of neuronal circuits, thus rendering an individual more likely to engage in a suicidal act. Improving our understanding of the neuroscience underlying SBs, both attempts and completions, at all developmental stages is crucial for more effective preventive treatments and for better identification of vulnerable individuals. Recent studies have characterized SB using an endophenotype strategy, which aims to identify quantitative measures that reflect genetically influenced stable changes in brain function. In addition to aiding in the functional characterization of susceptibility genes, endophenotypic research strategies may have a wider impact in determining vulnerability to SB, as well as the translation of human findings to animal models, and vice versa. Endophenotypes associated with vulnerability to SB include impulsive/aggressive personality traits and disadvantageous decision making. Deficits in realistic risk evaluation represent key processes in vulnerability to SB. Serotonin dysfunction, indicated by neuroendocrine responses and neuroimaging, is also strongly implicated as a potential endophenotype and is linked with impulsive aggression and disadvantageous decision making. Specific endophenotypes may represent heritable markers for the identification of vulnerable patients and may be relevant targets for successful suicide prevention and treatments.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| 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".