White Matter but not Gray Matter Volumes Are Associated with Cognition in Community-Dwelling Chinese Populations
Bibliographic record
Abstract
BACKGROUND: Few studies have investigated the association between cognition and brain volume associated with cerebral small vessel disease (CSVD). OBJECTIVE: We investigated the association between cognition and brain volume and neuroimaging markers of CSVD in a community-dwelling population. METHODS: Participants (n = 993, age≥35 years) from the community-based Shunyi Study were included to investigate the association between neuroimaging markers and cognition cross-sectionally. Magnetic resonance imaging markers included brain volume measurements of the total cerebrum, white matter, gray matter, and CSVD imaging markers. Cognitive performance was assessed using neuropsychological tests of the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Fuld Object Memory, digit span, Trail Making Test (TMT)-A, and TMT-B. RESULTS: For brain volume measurement, subcortical white matter fraction was positively associated with MMSE score (β= 0.034, p = 0.0062) and MoCA score (β= 0.034, p = 0.0174), and negatively associated with TMT-A and TMT-B completion time (β= -2.319, p = 0.0002; β= -2.827, p = 0.0073, respectively). For evaluation of CSVD imaging markers, the presence of lacunes was positively associated with TMT-B completion time (β= 17.241, p = 0.0028). CONCLUSION: In community-dwelling populations, reduced white matter volumes, as a consequence of aging and vascular damage, are associated with worse global cognition and executive function. Our findings provide potential insights into the correlation between cognition and CSVD-associated subcortical white matter injury.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".