Neuroligin 1 Regulates Autistic‐Like Repetitive Behavior through Modulating the Activity of Striatal D2 Receptor‐Expressing Medium Spiny Neurons
Bibliographic record
Abstract
Restricted and repetitive behavior (RRB) is a primary symptom of autism spectrum disorder (ASD), which poses a significant risk to individuals' health and is becoming increasingly prevalent. However, the specific cellular and neural circuit mechanisms underlying the generation of RRB remain unclear. In this study, it is reported that the absence of the ASD-related protein Neuroligin 1 (NLGN1) in dopamine receptor D2-expressing medium spiny neurons (D2-MSNs) in the dorsal striatum is associated with the duration and frequency of self-grooming and digging behaviors. The Nlgn1-deficient D2-MSNs are hyperactivated, which correlates with excessive self-grooming and digging behaviors. Inhibiting the activity of D2-MSNs reduces the duration and frequency of these RRBs. Furthermore, it is demonstrated that the generation of self-grooming and digging behaviors depends on distinct patterns of D2-MSN activity. Finally, through single-nucleus RNA sequencing (sn-RNAseq) and protein detection verification, it is revealed that the overactivation of protein kinase C (PKC) in Nlgn1-deficient mice contributes to excessive repetitive behaviors and increased neuronal excitability. In this study, potential mechanisms are proposed for the generation of self-grooming and digging behaviors, as well as suggest possible treatments and interventions 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) | 0.000 | 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) | 0.000 | 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".