Mapping Cerebellar Morphology in 15q11.2 CNV Carriers Using Normative Modeling
Bibliographic record
Abstract
Copy number variations (CNVs) at the 15q11.2 locus of the human genome have been associated with altered brain structure and increased risk for neurodevelopmental and neuropsychiatric disorders. The cerebellum is increasingly seen as a crucial brain region for neurodevelopmental conditions, yet the effects of 15q11.2 CNVs on cerebellar morphology remain largely unclear. Importantly, 15q11.2 CNVs shows reduced or incomplete penetrance (meaning that not all CNV carriers are affected) and variable expressivity (meaning that symptoms may differ between individuals with the same genetic alteration). Thus, there is a need to not only assess group differences, but also to quantify anatomical variability at the individual level. Here, we address these issues using normative models of brain anatomy trained on large datasets (n > 52k, age range: 3-85) to assess both group and individual-level deviations in cerebellar anatomy in carriers of 15q11.2 deletions (n = 120, mean [SD] age= 64.95 [7.58]) and duplications (n = 149, mean [SD] age=64.31 [7.21]), compared to non-carriers (n = 19,028, mean [SD] age=64.31 [7.58]). Group-level case-control analyses revealed significantly smaller total and regional cerebellar volumes in both deletion and duplication carriers, though with small effect sizes. Individual-level deviation analyses, capturing pronounced alterations in specific individuals, revealed a heterogeneous pattern among carriers. Overall, our findings suggest that CNVs at the 15q11.2 locus exert modest and highly individualized effects on cerebellar morphology.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".