Neural correlates of affective theory of mind in medication-free nonsuicidal self-injury: An fMRI study
Bibliographic record
Abstract
Emerging evidence indicates that emotion processing deficits are associated with nonsuicidal self-injury (NSSI). However, limited attention has been paid to the socio-affective functions of NSSI. In this study, we aimed to investigate the affective theory of mind (ToM) in medication-free individuals engaging in NSSI at both behavioral and neural levels. Twenty-eight individuals (mean age = 22.96 years) who engaged in NSSI and 38 age-, sex-, and IQ-matched controls (mean age = 22.79 years) underwent functional magnetic resonance imaging while performing the "Reading the Mind in the Eyes Test" (RMET). All participants also completed the Difficulties in Emotion Regulation Scale (DERS), Toronto Alexithymia Scale (TAS-20), and Beck Scale for Suicide Ideation (BSI). Although we did not find significant group differences in the RMET performance, the NSSI group, relative to the controls, exhibited significantly greater left medial superior frontal lobe activation and decreased right angular gyrus activation than did the control group. Reduced right angular gyrus activity was related to higher DERS and TAS scores across all participants. Our findings provide new evidence for aberrant neural processing of affective ToM in self-injurers. Future studies in developing intervention protocols for NSSI should focus on the multifaceted phases of socio-affective processing.
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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 | 0.000 | 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".