H06 Cross sectional and longitudinal 3T magnetic resonance spectroscopy in a TRACK-HD cohort of individuals with premanifest and early Huntington's disease
Bibliographic record
Abstract
Background A potential biomarker role for magnetic resonance spectroscopy (MRS) in Huntington9s disease (HD) is unclear due to conflicting reports in the literature. Aim To investigate MRS as an HD biomarker through cross sectional and longitudinal examinations in a TRACK-HD study cohort. Methods Cross sectional MRS of the left putamen (at 3T field strength) was performed in the University of British Columbia TRACK-HD study cohort. 84 individuals (30 controls (C), 25 pre-HD (P), 29 early HD (E)) were scanned at baseline. 78 individuals (29 (C), 26 (P) and 23 (E)) underwent repeat MRS examination at 1 year. Metabolites of interest were total N-acetyl aspartate (tNAA) and myo-inositol (MI); markers of neuronal health and gliosis. Results Baseline tNAA concentrations in early HD were lower than in controls (mean (SD) 8.6 (0.7) mM (C) vs 7.3 (1.2) mM (E); p<0.001). MI was higher in early HD compared with controls (6.0 (2.6) mM (E) vs 4.3 (1.4) mM (C); p<0.005). After 1 year, MI concentrations remained 40% higher in early HD (6.0 (2.7) mM (E) vs 4.2 (1.1) mM (C); p<0.01). tNAA was still lower in early HD (p<0.001) but also in pre-HD (p<0.05) than controls (7.4 (0.9) mM (E) vs 8.2 (0.8) mM (P) vs 8.7 (0.6) mM (C)). Neither tNAA nor MI exhibited longitudinal change in any group. A novel observation was that of 30% increases in spectral linewidth in early HD at both time points (p<0.01). Conclusions Our data support patterns of higher MI levels and lower tNAA in early HD. The pattern of lower tNAA may begin in premanifest HD. The lack of longitudinal metabolite change is not unusual over such a short duration of follow-up. We also identified for the first time, consistent increases in MRS spectral linewidth in early 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".