Cerebellar gray matter excess and atrophy in Huntington's disease: a voxel-based morphometry study (P1.059)
Bibliographic record
Abstract
Objective: The aim of our study is detail the cerebellar gray matter (GM) alterations in HD using the tool "spatially unbiased template atlas" (SUIT) for Voxel based morphometry (VBM) on magnetic resonance imaging (MRI). Background: In the last years, the cerebellar role in neurodegenerative diseases has been extensively studied. However, few research related cerebellum and Huntington's disease (HD). This is not only due to cerebellar contribution on motor refinement, but mainly by the discovery of its non-motor functions. Methods: We compared 26 patients matched in gender and age with 26 controls. They underwent neurological (Unified Huntington’s disease rating scale - UHDRS) and cognitive (Montreal cognitive assessment - MOCA) evaluations. SUIT was used to analyze GM alterations. We created a two-sample test to analyze GM differences between both groups and another to correlate the cerebellar GM alterations with UHDRS (mood and motor) and MOCA scores, corrected for age, cytosine-adenine-guanine (CAG) repeats and disease duration. Results: SUIT findings were divided into general: areas of GM excess and atrophy compared with controls, located respectively in the anterior and postero-superior cerebellar lobes; and specific: UHDRS (mood and motor) and MOCA scores. Higher GM density in the postero-superior lobe correlated with mood symptoms. Worse motor function and better cognitive function correlated with GM changes in the posterior cerebellum (p<0.001 and k>100 voxels). Conclusions: The listed areas are responsible for sensorimotor integration, motor planning, visuospatial function and emotional processing. We believe these findings may contribute to a better understanding of the neuropathological process of HD.
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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) | 0.000 | 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) | 0.003 | 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".