A Gray Matter Volume Covariance Network Associated with the Motoric Cognitive Risk Syndrome: A Multicohort MRI Study
Bibliographic record
Abstract
BACKGROUND: Motoric cognitive risk (MCR) syndrome is a predementia syndrome characterized by slow gait and cognitive complaint that predicts both Alzheimer's disease and vascular dementia. Yet, we know very little about the brain structures and brain pathologies associated with MCR. The aim of this study was to identify gray matter (GM) networks associated with MCR. METHODS: We used voxel-based morphometry and multivariate covariance-based statistics to identify GM networks associated with MCR in a pooled sample of 267 older adults without dementia from three different cohorts-two North American cohorts and one French cohort. RESULTS: The mean age of participants was 75.63 years, 50.56% identified as female, 57.68% had ≥13 years of education, and 5.99% had a prior history of stroke. A total of 14.23% participants met criteria for MCR. We identified a significant GM volume covariance pattern that was associated with MCR-even after adjusting for age, sex, education, mild cognitive impairment, stroke, total intracranial volume, and cohort status. This GM volume covariance network was primarily composed of supplementary motor, insular, and prefrontal cortex regions. CONCLUSIONS: These findings suggest that MCR is primarily associated with GM atrophy in brain regions previously linked to the control aspects of gait such as motor planning and modulation rather than the motor aspects of gait such as gait initiation and maintenance.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".