Brain abnormalities in a Neuroligin3 R451C knockin mouse model associated with autism
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) has been used quite extensively for examining morphological changes in human and animal brains. One of the many advantages to examining mouse models of human autism is that we are able to examine single gene targets, like that of Neuroligin3 R451C knockin (NL3 KI), which has been directly implicated in human autism. The NL3 KI mouse model has marked volume differences in many different structures in the brain: gray matter structures, such as the hippocampus, the striatum, and the thalamus, were all found to be smaller in the NL3 KI. Further, many white matter structures were found to be significantly smaller, such as the cerebral peduncle, corpus callosum, fornix/fimbria, and internal capsule. Fractional anisotropy measurements in these structures were also measured, and no differences were found. The volume changes in the white matter regions, therefore, are not due to a general breakdown in the microstructure of the tissue and seem to be caused by fewer axons or less mature axons. A larger radial diffusivity was also found in localized regions of the corpus callosum and cerebellum. The corpus callosal changes are particularly interesting as the thinning (or reduced volume) of the corpus callosum is a consistent finding in autism. This suggests that the NL3 KI model may be useful for examining white matter changes associated with 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".