A longitudinal study of magnetic resonance spectroscopy Huntington's disease biomarkers
Bibliographic record
Abstract
Putaminal metabolites examined using cross-sectional magnetic resonance spectroscopy (MRS) can distinguish pre-manifest and early Huntington's Disease (HD) individuals from controls. An ideal biomarker, however, will demonstrate longitudinal change over short durations. The objective here was to evaluate longitudinal in vivo brain metabolite profiles in HD over 24 months. Eighty-four participants (30 controls, 25 pre-manifest HD, 29 early HD) recruited as part of TRACK-HD were imaged at baseline, 12 months, and 24 months using 3T MRS of left putamen. Automated putaminal volume measurement was performed simultaneously. To quantify partial volume effects, spectroscopy was performed in a second, white matter voxel adjacent to putamen in six subjects. Subjects underwent TRACK-HD motor assessment. Statistical analyses included linear regression and one-way analysis of variance (ANOVA). At all time-points N-acetyl aspartate and total N-acetyl aspartate (NAA), neuronal integrity markers, were lower in early HD than in controls. Total NAA was lower in pre-manifest HD than in controls, whereas the gliosis marker myo-inositol (MI) was robustly elevated in early HD. Metabolites were stable over 24 months with no longitudinal change. Total NAA was not markedly different in adjacent white matter than putamen, arguing against partial volume confounding effects in cross-sectional group differences. Total NAA correlations with disease burden score suggest that this metabolite may be useful in identifying neurochemical responses to therapeutic agents. We demonstrate almost consistent group differences in putaminal metabolites in HD-affected individuals compared with controls over 24 months. Future work establishing spectroscopy as an HD biomarker should include multi-site assessments in large, pathologically diverse cohorts.
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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 | 0.000 | 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) | 0.001 | 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".