Cognitive engagement may slow clinical progression and brain atrophy in Huntington’s disease
Bibliographic record
Abstract
Lifelong cognitive engagement conveys benefits in Huntington's disease (HD) and may positively affect non-cognitive domains in other populations. However, the effect of lifelong cognitive engagement on the progression of motor and psychiatric domains in HD remains unknown, as is its neurobiological basis. Forty-five HD individuals completed the Cognitive Reserve Questionnaire (CRQ) and longitudinal clinical evaluation (maximum total of six visits, mean inter-assessment duration of 13.53 ± 4.1 months). Of these, thirty-three underwent longitudinal neuroimaging (18 ± 6 months follow-up). Generalized linear mixed-effects models were executed to predict the effect of individual differences in lifelong cognitive engagement on HD clinical progression and voxel-based morphometry to explore the impact of lifelong cognitive engagement on whole-brain gray matter volume atrophy. Controlling for age, disease stage, and sex, higher CRQ scores were associated with reduced overall severity and longitudinal progression across cognitive, motor, and psychiatric domains. Those with higher CRQ scores demonstrated reduced gray matter volume loss in the middle frontal gyrus, supplementary motor area, and middle cingulate. This putative impact on HD clinical progression may be conferred by preservation of brain volume in neural hubs that integrate executive function with action initiation and behavioral regulation, providing support for early cognitive engagement, even prior to diagnosis.
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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 | 0.002 |
| 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 | 0.001 | 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".