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Record W4408151889 · doi:10.1503/jpn.240129

Impulse control deficits among patients with nonsuicidal self-injury: a mediation analysis based on structural imaging

2025· article· en· W4408151889 on OpenAlexvenueno aff
Ya Xie, Sichu Wu, Jian Li, Congjie Zhang, Yumin Zhang, Yaming Hang, Zhangwei Lv, Pei Zhang, Minlu Liang, Bo Yu, Jing Long, Yuan Liu, Su-Hong Wang, Lichen Ouyang, Liping Zhang, Yun Wu, Chun Wang

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2025
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMediationImpulse controlImpulse (physics)PsychologyClinical psychologyMedicinePsychotherapistPhysicsPolitical science

Abstract

fetched live from OpenAlex

Background: Nonsuicidal self-injury (NSSI) is posited to arise from a complex interaction of biopsychosocial factors, with impulsivity playing a critical role. Given that current research on the neural mechanisms underlying this hypothesis remains inconsistent and limited in scope, we sought to explore how NSSI behaviours are associated with impulsivity resulting from structural brain alterations. Methods: We recruited patients with NSSI behaviours and healthy controls from 11 psychiatric hospitals. We assessed the differences in impulse control between the 2 groups using the Barratt Impulsiveness Scale version 11 and the Attention Network Test. We also conducted T1-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging. Finally, we analyzed the associations among brain structure, psychological characteristics, and self-injurious behaviour among patients with NSSI. Results: We included 293 patients with NSSI behaviours and 140 healthy controls. Among them, 182 patients with NSSI and 95 controls underwent the T1-weighted MRI and diffusion tensor imaging. Patients with NSSI showed increased impulsivity and alerting function, with the strongest correlation between NSSI frequency and motor impulsivity. Compared with controls, patients with NSSI exhibited decreased grey matter volume and increased white matter volume, with no significant difference in cortical thickness. Pathway analysis demonstrated that motor impulsivity significantly mediated the association between white matter volume and the NSSI frequency in the right superior frontal gyrus and right inferior parietal lobe. When examining the connecting fibre tracts in the right frontoparietal area, patients with NSSI showed decreased integrity of white matter microstructure in the right cingulum, right superior corona radiata, and the splenium of the corpus callosum. Limitations: Accurately measuring executive control linked to NSSI is challenging in cognitive behavioural tasks, as impulsive tendencies during NSSI occurrence are not effectively captured. Conclusion: Our findings suggested that motor impulsivity, a prominent psychopathological characteristic of NSSI, is primarily modulated by the frontoparietal regions. These results provide empirical neuroimaging evidence for the impaired impulse control observed in NSSI.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.005
GPT teacher head0.269
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2025
Admission routes1
Has abstractyes

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