Neuroanatomy in mouse models of Rett syndrome is related to the severity of Mecp2 mutation and behavioral phenotypes
Bibliographic record
Abstract
Rett syndrome (RTT) is a neurodevelopmental disorder that predominantly affects girls. The majority of RTT cases are caused by de novo mutations in methyl-CpG-binding protein 2 (MECP2), and several mouse models have been created to further understand the disorder. In the current literature, many studies have focused their analyses on the behavioral abnormalities and cellular and molecular impairments that arise from Mecp2 mutations. However, limited efforts have been placed on understanding how Mecp2 mutations disrupt the neuroanatomy and networks of the brain. In this study, we examined the neuroanatomy of male and female mice from the Mecp2 tm1Hzo , Mecp2 tm1.1Bird/J , and Mecp2 tm2Bird/J mouse lines using high-resolution magnetic resonance imaging (MRI) paired with deformation-based morphometry to determine the brain regions susceptible to Mecp2 disruptions. We found that many cortical and subcortical regions were reduced in volume within the brains of mutant mice regardless of mutation type, highlighting regions that are susceptible to Mecp2 disruptions. We also found that the volume within these regions correlated with behavioral metrics. Conversely, regions of the cerebellum were differentially affected by the type of mutation, showing an increase in volume in the mutant Mecp2 tm1Hzo brain relative to controls and a decrease in the Mecp2 tm1.1Bird/J and Mecp2 tm2Bird/J lines. Our findings demonstrate that the direction and magnitude of the neuroanatomical differences between control and mutant mice carrying Mecp2 mutations are driven by the severity of the mutation and the stage of behavioral impairments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".