Neuroimaging Signatures and Cognitive Correlates of the Montreal Cognitive Assessment Screen in a Nonclinical Elderly Sample
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) screen was developed as a brief instrument to identify mild cognitive impairment and dementia among older individuals. To date, limited information is available regarding the neuroimaging signatures associated with performance on the scale, or the relationship between the MoCA and more comprehensive cognitive screening measures. The present study examined performances on the MoCA among 111 non-clinical older adults (ages 51-85) enrolled in a prospective study of cognitive aging. Participants were administered the MoCA, Mini-Mental State Exam (MMSE), and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). A subset of participants (N = 69) underwent structural 3 T magnetic resonance imaging (MRI) to define the volumes of total frontal gray matter, total hippocampus, T2-weighted subcortical hyperintensities (SH), and total brain volume. The results revealed significant correlations between the total score on the MoCA and total score on the RBANS and MMSE, though the strength of the correlations was more robust between the MoCA and the RBANS. Modest correlations between individual subscales of the MoCA and neuroimaging variables were evident, but no patterns of shared variance emerged between the MoCA total score and neuroimaging indices. In contrast, total brain volume correlated significantly with total score on the RBANS. These results suggest that additional studies are needed to define the significance of MoCA scores relative to brain integrity among an older population.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".