The experience of social exclusion in women with a history of suicidal acts: a neuroimaging study
Bibliographic record
Abstract
Suicidal behaviors result from a complex interaction between social stressors and individual vulnerability. However, little is known of the specific neural network supporting the sensitivity to social stressors in patients at risk of suicidal acts. Using functional Magnetic Resonance Imaging, we investigated brain processing of social rejection in suicide attempters. Thirty-six euthymic women with a history of depression and suicidal behavior were compared to 41 euthymic women with a history of depression but no suicidal attempt, and 28 healthy controls. The Cyberball Game was used as a validated social exclusion paradigm. Relative to healthy controls, both patient groups reported higher levels of social distress related to the task, without significant differences according to suicidal status. Compared to patients without any history of suicide attempt and healthy controls, suicide attempters showed decreased contrast in the left insula and supramarginal gyrus during the exclusion vs. inclusion condition, after controlling for number of depressive episodes, medication, mood disorder type or social phobia. Our study highlights impaired brain response to social exclusion in euthymic female suicide attempters in regions previously implicated in pain tolerance and social cognition. These findings suggest sustained brain dysfunctions related to social perception in suicide attempters.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".