Molecular Pathways, Neural Circuits and Emerging Therapies for Self-Injurious Behaviour
Bibliographic record
Abstract
Nonsuicidal self-injurious behaviour (SIB) is a debilitating manifestation of physical aggression commonly observed across neurodevelopmental, psychiatric, and genetic disorders. This behaviour arises from a multifactorial aetiology involving genetic predispositions, epigenetic modifications, neurotransmitter dysregulation, and environmental stressors. Dysregulation in dopaminergic, serotonergic, glutamatergic, and GABAergic systems has been implicated in the pathophysiology of SIB, alongside structural and functional abnormalities within fronto-limbic-striatal circuits. These disruptions impair key processes, such as emotional regulation, reward processing, and behavioural inhibition, contributing to the emergence and reinforcement of SIB. Advances in preclinical research using genetic, lesion-based, pharmacological, and environmental animal models have been instrumental in elucidating the molecular and neurocircuitry underpinnings of SIB. Emerging neuromodulation therapies targeting critical nodes within the fronto-limbic-striatal network, particularly deep brain stimulation, have shown promise in treating severe, refractory SIB and improving quality of life. This review integrates current evidence from clinical studies, molecular research, and preclinical models to provide a comprehensive overview of the pathophysiology of SIB and therapeutic approaches. By focusing on the molecular mechanisms and neural circuits underlying SIB, we highlight the translational potential of emerging pharmacological and neuromodulatory therapies. A deeper understanding of these pathways will pave the way for precision-based interventions, bridging the gap between molecular research and clinical applications in SIB and related conditions.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".