The social salience network hypothesis of autism: Disrupted network activity, oxytocin signaling, and implications for social symptoms
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a complex condition characterized by its heterogeneity, with significant variability in symptoms across subtypes and associated comorbidities. Despite the urgent need to develop mechanism-based therapies for the core social symptoms of ASD, progress has been hindered by the heterogeneous etiology of this neurodevelopmental disorder and our still limited understanding of the neural mechanisms underlying social behavior. The evaluation of sociosensory cues and the modulation of motivation to engage socially are fundamental components of social interaction, thought to be coordinated by a network of interconnected brain regions called the social salience network (SSN). This network is strongly modulated by the neurohormone oxytocin (OXT) to facilitate appropriate social responses. It is increasingly recognized that disruptions within the SSN contribute to the atypical social perception and engagement observed in autistic individuals. This review will summarize evidence from current clinical and preclinical literature that provides compelling evidence for SSN disruptions as a possible mechanism that underlies the social symptoms of ASD. Furthermore, we discuss OXT-mediated correction of SSN disruptions at the regional and circuit levels that rescues social phenotypes in preclinical models of ASD-risk factors. These molecular, cellular, and circuit mechanisms within the SSN could serve as promising treatment targets which may propel the development of novel and effective options for alleviating the social difficulties of autistic individuals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".