Three‐dimensional quantification of oxytocin neurons in the hypothalamic paraventricular nucleus reveals sex‐ and subregion‐specific differences in two genetic mouse models of autism
Bibliographic record
Abstract
Oxytocin (OXT), a neuropeptide hormone essential to a wide range of social functions, has drawn increasing attention as a crucial contributor to the neurobiology of autism spectrum disorder (ASD). Central OXT system disruptions have been reported in several genetic mouse models of ASD; however, a detailed and systematic characterization of these phenotypes, and cross-model identification of shared and distinct features, are presently lacking. We integrated whole-brain OXT immunolabeling, SHIELD tissue clearing, light-sheet microscopy, and three-dimensional (3D) machine learning-based cell detection to establish a high-throughput, intact-tissue pipeline and quantified OXT immunopositive (OXT+) neurons across subregions of the paraventricular nucleus of the hypothalamus (PVN) in two genetic mouse models of ASD: Cntnap2 and Fmr1 knockout (KO) mice. We validated this pipeline alongside conventional immunohistochemistry using tissue sections. We show subregion- and sex-specific differences in PVN OXT+ cell counts in the two KO models. Notably, whole-PVN analysis revealed additional subregion- and sex-specific differences that were not evident in section-based quantification. These results identify subregion- and sex-specific differences in PVN OXT+ neuronal distribution as a shared phenotype in two genetic mouse models of ASD. This work highlights the importance of region-specific, high-resolution 3D approaches in intact tissue for quantifying cell populations within anatomically complex brain regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".