Identifying subtypes of youth suicidality based on psychopathology: alterations in genetic, neuroanatomical and environmental features
Bibliographic record
Abstract
Summary One of the most complex human behaviours that defies singular explanatory models is suicidal behaviour, especially in the youth. A promising approach to make progress with this conundrum is to parse distinct subtypes of this behaviour. Utilizing 1,624 children with suicidal thoughts and behaviors (STBs) and 3,224 healthy controls from the ABCD Study, we clustered children with STB based on thirty-four cognitive and psychopathological measures which capture suicide-related risk-moderating traits. Environmental and genetic risk factors, as well as neuroanatomical characteristics of each subtype, were then compared with controls. We identified five distinct STB subtypes, each revealing unique neuroanatomy, environmental/genetic risks, and persistence patterns. Subtype 1 (Depressive, 9.6%) exhibited the most severe depressive symptoms. Subtype 2 (Externalizing, 20.1%) displayed anatomical and functional alterations in frontoparietal network and increased genetic risk for ADHD. Subtype 3 (Cognitive Deficit, 20.4%) demonstrated lower cognitive performance and widespread white-matter deficits. Subtype 4 (Mild Psychotic, 22.2%) presented higher prodromal psychotic symptoms, often unnoticed by parents. Subtype 5 (High Functioning, 27.6%) showed larger total brain volume, better cognition, and higher socio-economic status, contrasting subtypes 1-4. Only Subtypes 1 and 2 demonstrate persistent STB features at the 2-year follow-up. Our results suggested that youth suicidal behaviour may result from several distinct bio-behavioral pathways that are identifiable through co-occurring psychopathology, and provide insights into the underlying neural mechanisms and corresponding intervention strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".