Association of Motoric Cognitive Risk Syndrome With Brain Volumes: Results From the GAIT Study
Bibliographic record
Abstract
BACKGROUND: The "motoric cognitive risk" (MCR) syndrome is a newly reported predementia syndrome combining cognitive complaint and slow gait speed. We hypothesized that individuals with MCR syndrome would have lower brain volumes compared with non-MCR individuals. This study aims (i) to compare the cognitive profile of nondemented older community-dwellers with and without MCR syndrome and (ii) to examine association of global and regional brain volumes with MCR syndrome. METHODS: A total of 171 individuals (28 MCR and 143 non-MCR) were included in this cross-sectional study. Total white matter abnormalities, total white matter, total cortical and subcortical gray matters, hippocampus, motor cortex, premotor cortex, and prefrontal cortex were examined. Brain volumes were quantified from a three-dimensional T1-weighted magnetic resonance imaging using semi-automated software. Age, gender, education level, number of drugs taken daily, use of psychoactive drugs, and cognitive profile were also measured. RESULTS: The distribution of cognitively healthy individuals and those with mild cognitive impairment was not different in participants with and without MCR. Multiple logistic regression models showed that smaller volumes of total gray matter (p = .016), total cortical gray matter (p = .010), premotor cortex (p = .018), prefrontal cortex (p = .026), and dorsolateral segment of prefrontal cortex (p = .032) were associated with MCR status. The premotor cortex presented the highest mean difference for brain regional volume between MCR and non-MCR participants (p = .03). CONCLUSIONS: The findings revealed similar cognitive profile in MCR and non-MCR participants, and MCR-related smaller global and regional gray matter volumes involving premotor and prefrontal cortices, suggesting that the MCR syndrome may predict cortical neurodegenerative dementia more than subcortical dementia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".