Back-translating behavioral intervention for autism spectrum disorders to mice with blunted reward restores social abilities
Bibliographic record
Abstract
Abstract The mu opioid receptor (MOR) plays a critical role in modulating social behavior in humans and animals. Accordingly, MOR null mice display severe alterations in their social repertoire as well as multiple other behavioral deficits, recapitulating core and secondary symptoms of autism spectrum disorder (ASD). Such behavioral profile suggests that MOR dysfunction, and beyond this, altered reward processes may contribute to ASD etiopathology. Interestingly, the only treatments that proved efficacy in relieving core symptoms of ASD, early behavioral intervention programs, rely principally on positive reinforcement to ameliorate behavior. The neurobiological underpinnings of their beneficial effects, however, remain poorly understood. Here we back-translated applied behavior analysis (ABA)-based behavioral interventions to mice lacking the MOR ( Oprm1 −/− ), as a model of autism with blunted reward processing. By associating a positive reinforcement, palatable food reward, to daily encounter with a wild-type congener, we were able to rescue durably social interaction and preference in Oprm1 −/ − mice. Along with behavioral improvements, the expression of marker genes of neuronal activity and plasticity as well as genes of the oxytocin/vasopressin system were remarkably normalized in the reward/social circuitry. Our study provides further evidence for a critical involvement of reward processes in driving social behavior and opens new perspectives regarding therapeutic intervention in ASD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".