E32 A 36 Month Longitudinal Magnetic Resonance Spectroscopy Study In Pre-manifest And Early Huntington Disease Subjects From The Track-hd Study
Bibliographic record
Abstract
Background TRACK-HD is a multi-national longitudinal observational study of Huntington Disease (HD). Magnetic Resonance Spectroscopy (MRS) utilises MRI technology to measure brain metabolites. Aims Using MRS we aimed to define patterns of change in specific metabolite concentrations that reflect disease onset or progression in brain regions affected in HD. Methods Age and gender-matched subjects from a single TRACK-HD site (Vancouver) were evaluated by MRS at four visits over 36 months. Early HD was defined as UHDRS-TFC Stage 1 or 2, Pre-manifest HD (Pre-HD) individuals scored <5 on UHDRS-TMS and controls were gene negative spouses or family members. The MRS protocol involved single voxel spectroscopy in the LEFT putamen using a 3T Philips Achieva MRI Scanners. Metabolites reported include: N-acetyl Aspartate (NAA) a marker of neuronal integrity; Creatine, a marker of brain energy metabolism; Choline, a marker for neuronal membrane turnover; Glutamate, a major CNS excitatory neurotransmitter; and Myo-inositol (mI), an astrocyte marker. MRS spectra were fit using the LCModel program, and metabolite concentrations were normalised to the unsuppressed water signal. Results At all time points, decreased putaminal NAA concentration was observed in Pre-HD and Early HD; the decrease in NAA concentration was independent of grey matter volume loss (putamen atrophy), showed longitudinal consistency, and correlated with motor performance at baseline. Putaminal mI concentration was increased in early HD at all time points. Additional findings for other metabolites will be presented. Conclusions We identify here a potentially useful role for MRS in measuring disease progression and disease onset in HD. These MRS measures are biologically relevant endpoints that should be further evaluated in large multi-site 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".