Utility of a contemporary digital cognitive-motor biomarker in Huntington's and Parkinson's diseases
Bibliographic record
Abstract
Background Cognitive impairment significantly impacts the quality of life in patients with neurodegenerative disorders, including Huntington's disease (HD), Parkinson's disease (PD), and Alzheimer's disease (AD). Objective This study aims to assess the utility of MemTrax, a contemporary digital continuous recognition task platform originally developed for AD, as an effective tool for revealing cognitive and clinical motor impairments in HD and PD populations as aligned with respective disease staging. Methods A total of 135 healthy controls, 131 HD, and 212 PD participants were included in the study. MemTrax metrics, recognition accuracy (MTx-%C), response time (MTx-RT), and a composite score (MTx-Cp) were correlated with clinical motor and cognition scales and disease staging. Results MemTrax metrics showed stage-dependent declines in both HD and PD. In HD, both MTx-%C and MTx-Cp decreased significantly from pre-HD stage to stage 2 ( p < 0.001), showing negative correlations with motor impairment and cognitive scales. In PD, MTx-Cp declined across Hoehn and Yahr stages (1–4, p < 0.001), with strong negative correlations to Unified Parkinson's Disease Rating Scale Part III (UPDRS III) and positive links to Montreal Cognitive Assessment/Mini-Mental State Examination. Additionally, MTx-RT increased with disease progression and correlated positively with UPDRS III, indicating it could assess psychomotor slowing in PD ( p < 0.01). Conclusions MemTrax effectively captures cognitive-motor decline in HD and PD. The responsivity of MemTrax to the severity of these disorders extends its utility beyond AD, positioning MemTrax performance as a cross-disease digital biomarker for early detection in neurodegenerative diseases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".