Relationship Between Regional Brain Volumes and Cognitive Performance in the Healthy Aging: An MRI Study Using Voxel-Based Morphometry
Bibliographic record
Abstract
The presence of cognitive impairment is a frequent complaint among elderly individuals in the general population. This study aimed to investigate the relationship between aging-related regional gray matter (rGM) volume changes and cognitive performance in healthy elderly adults. Morphometric magnetic resonance imaging (MRI) measures were acquired in a community-based sample of 170 cognitively-preserved subjects (66 to 75 years). This sample was drawn from the "São Paulo Ageing and Health" study, an epidemiological study aimed at investigating the prevalence and risk factors for Alzheimer's disease in a low income region of the city of São Paulo. All subjects underwent cognitive testing using a cross-culturally battery validated by the Research Group on Dementia 10/66 as well as the SKT (applied on the day of MRI scanning). Blood genotyping was performed to determine the frequency of the three apolipoprotein E allele variants (APOE ε2/ε3/ε4) in the sample. Voxelwise linear correlation analyses between rGM volumes and cognitive test scores were performed using voxel-based morphometry, including chronological age as covariate. There were significant direct correlations between worse overall cognitive performance and rGM reductions in the right orbitofrontal cortex and parahippocampal gyrus, and also between verbal fluency scores and bilateral parahippocampal gyral volume (p < 0.05, familywise-error corrected for multiple comparisons using small volume correction). When analyses were repeated adding the presence of the APOE ε4 allele as confounding covariate or excluding a minority of APOE ε2 carriers, all findings retained significance. These results indicate that rGM volumes are relevant biomarkers of cognitive deficits in healthy aging individuals, most notably involving temporolimbic regions and the orbitofrontal cortex.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".