E02 Standardising and observing atrophy and cognitive patterns across the lifetime of Huntington’s disease using data from the HD-YAS and TRACK-HD and TrackOn-HD studies
Bibliographic record
Abstract
Background Validating neuroimaging biomarkers that linearly track clinical progression over the lifetime of a HD patient is essential for clinical trials to test drug efficacy and ultimately develop a cure. Different image processing techniques can introduce variability and standardisation is required for cross-study comparison. Aim This study aims to investigate image analysis across three large observational datasets, TRACK-HD, TrackOn-HD and HD-YAS, in order to standardise a processing pipeline for grey and white matter segmentation. In this way, the progression of HD pathology across the entire lifespan can be investigated. Methods Neuroimaging data from 497 participants from the TRACK-HD, TrackOn-HD and HD-YAS datasets were combined. Original processing with SPM5, SPM8 and standardised processing using the CAT12 tool in SPM12 were compared. Grey and white matter volumes were plotted to observe the development of atrophy in HD. Statistical parametric maps correlating cognitive and motor outcomes with regional atrophy were also assessed. Results Comparison of SPM5 and SPM8 with SPM12 standardised brain segmentations showed significant differences. The CAT12 pre-processing tool was validated as an accurate method for standardised brain segmentation across both data sets. In the premanifest cohort, caudate volume was the earliest biomarker detected, followed by grey and white matter volumes. Examination of the cognitive tests highlighted a need for more sensitive measures for early premanifest individuals. Conclusions We demonstrate a systematic difference between different SPM software versions, which was removed when the CAT12 tool was used. This emphasises the need for standardisation of image analysis processing when combining multiple datasets.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".