Regional Brain and Spinal Cord Volume Loss in Spinocerebellar Ataxia Type 3
Bibliographic record
Abstract
BACKGROUND: Given that new therapeutic options for spinocerebellar ataxias are on the horizon, there is a need for markers that reflect disease-related alterations, in particular, in the preataxic stage, in which clinical scales are lacking sensitivity. OBJECTIVE: The objective of this study was to quantify regional brain volumes and upper cervical spinal cord areas in spinocerebellar ataxia type 3 in vivo across the entire time course of the disease. METHODS: We applied a brain segmentation approach that included a lobular subsegmentation of the cerebellum to magnetic resonance images of 210 ataxic and 48 preataxic spinocerebellar ataxia type 3 mutation carriers and 63 healthy controls. In addition, cervical cord cross-sectional areas were determined at 2 levels. RESULTS: The metrics of cervical spinal cord segments C3 and C2, medulla oblongata, pons, and pallidum, and the cerebellar anterior lobe were reduced in preataxic mutation carriers compared with controls. Those of cervical spinal cord segments C2 and C3, medulla oblongata, pons, midbrain, cerebellar lobules crus II and X, cerebellar white matter, and pallidum were reduced in ataxic compared with nonataxic carriers. Of all metrics studied, pontine volume showed the steepest decline across the disease course. It covaried with ataxia severity, CAG repeat length, and age. The multivariate model derived from this analysis explained 46.33% of the variance of pontine volume. CONCLUSION: Regional brain and spinal cord tissue loss in spinocerebellar ataxia type 3 starts before ataxia onset. Pontine volume appears to be the most promising imaging biomarker candidate for interventional trials that aim at slowing the progression of spinocerebellar ataxia type 3. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".