Substantia nigra degeneration in spinocerebellar ataxia 2 and 7 using neuromelanin‐sensitive imaging
Bibliographic record
Abstract
OBJECTIVE: Spinocerebellar ataxias (SCA) are neurodegenerative diseases with widespread lesions across the central nervous system. Ataxia and spasticity are usually predominant, but patients may also present with parkinsonism. We aimed to characterize substantia nigra pars compacta (SNc) degeneration in SCA2 and 7 using neuromelanin-sensitive imaging. METHODS: Ataxic and preataxic expansion carriers with SCA2 (n=15) and SCA7 (n=15) and healthy controls (n=10) were prospectively recruited. Volume and signal-to-noise ratio (SNR) values of the SNc were extracted from neuromelanin-sensitive images. ROC curves were used to determine the metrics that best differentiated SCA participants. Correlations between imaging measurements, clinical variables, and plasma neurofilaments light chain (NfL) levels were investigated. RESULTS: SCA2 participants had lower SNR values in the SNc than controls (110.2 ± 1.3 versus 113.2 ± 1.4; p < 0.001) and those with SCA7 (112.5 ± 2.1; p < 0.01). SNR in SCA7 participants and controls did not differ. In ataxic patients, SNc volumes were lower in SCA2 (0.13 ± 0.04; p = 0.06) and SCA7 (0.10 ± 0.03, p = 0.02) patients compared to controls (0.17 ± 0.04). Signal decrease was detected at the preataxic stage in SCA2, but not in SCA7. SCA2 participants showed prominent involvement of the associative and limbic nigral territories. SNR discriminated ataxic and preataxic SCA2 participants from controls (AUC ≥0.94). SNc volume differentiated ataxic SCA7 participants from controls (AUC = 1), but not preataxic ones. In SCA7, correlations were observed between SNc volume and time to onset, CAG repeats, clinical severity scores, and NfL. CONCLUSIONS: Neuromelanin-sensitive imaging provides biomarkers of nigral degeneration in SCAs, detectable from the preataxic stage in SCA2, which could potentially serve as outcome measures in clinical trials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".