Brain size regulations by cbp haploinsufficiency evaluated by in-vivo MRI based volumetry
Bibliographic record
Abstract
The Rubinstein-Taybi Syndrome (RSTS) is a congenital disease that affects brain development causing severe cognitive deficits. In most cases the disease is associated with dominant mutations in the gene encoding the CREB binding protein (CBP). In this work, we present the first quantitative analysis of brain abnormalities in a mouse model of RSTS using magnetic resonance imaging (MRI) and two novel self-developed automated algorithms for image volumetric analysis. Our results quantitatively confirm key syndromic features observed in RSTS patients, such as reductions in brain size (-16.31%, p < 0.05), white matter volume (-16.00%, p < 0.05), and corpus callosum (-12.40%, p < 0.05). Furthermore, they provide new insight into the developmental origin of the disease. By comparing brain tissues in a region by region basis between cbp(+/-) and cbp(+/+) littermates, we found that cbp haploinsufficiency is specifically associated with significant reductions in prosencephalic tissue, such us in the olfactory bulb and neocortex, whereas regions evolved from the embryonic rhombencephalon were spared. Despite the large volume reductions, the proportion between gray-, white-matter and cerebrospinal fluid were conserved, suggesting a role of CBP in brain size regulation. The commonalities with holoprosencephaly and arhinencephaly conditions suggest the inclusion of RSTS in the family of neuronal migration disorders.
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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 | 0.001 | 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) | 0.001 | 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".