Genetic Effects on Cerebellar Structure Across Mouse Models of Autism Using a Magnetic Resonance Imaging Atlas
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) of autism populations is confounded by the inherent heterogeneity in the individuals' genetics and environment, two factors difficult to control for. Imaging genetic animal models that recapitulate a mutation associated with autism quantify the impact of genetics on brain morphology and mitigate the confounding factors in human studies. Here, we used MRI to image three genetic mouse models with single mutations implicated in autism: Neuroligin-3 R451C knock-in, Methyl-CpG binding protein-2 (MECP2) 308-truncation and integrin β3 homozygous knockout. This study identified the morphological differences specific to the cerebellum, a structure repeatedly linked to autism in human neuroimaging and postmortem studies. To accomplish a comparative analysis, a segmented cerebellum template was created and used to segment each study image. This template delineated 39 different cerebellar structures. For Neuroligin-3 R451C male mutants, the gray (effect size (ES) = 1.94, FDR q = 0.03) and white (ES = 1.84, q = 0.037) matter of crus II lobule and the gray matter of the paraflocculus (ES = 1.45, q = 0.045) were larger in volume. The MECP2 mutant mice had cerebellar volume changes that increased in scope depending on the genotype: hemizygous males to homozygous females. The integrin β3 mutant mouse had a drastically smaller cerebellum than controls with 28 out of 39 cerebellar structures smaller. These imaging results are discussed in relation to repetitive behaviors, sociability, and learning in the context of autism. This work further illuminates the cerebellum's role in autism.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".