A Voxel-based Morphometric Study to Determine Individual Differences in Gray Matter Density Associated with Age and Cognitive Change Over Time
Bibliographic record
Abstract
Voxel-based morphometry (VBM) was used to examine the relation between age and gray matter density cross-sectionally and to study the association between gray matter density and longitudinal decline in performance on cognitive tests in healthy, non-demented elderly individuals. Participants were neuropsychologically tested at baseline and again after 3 years. Thirty-seven subjects (mean age 72.5 years) who showed a decline in cognitive test performance at follow-up were compared with 38 individually matched control subjects (mean age 71.8 years) whose performance did not change over time. Magnetic resonance imaging scans were acquired at follow-up and individual differences in regional gray matter density were examined with VBM. The largest age effects were found in various regions in the prefrontal cortex, the (medial) temporal lobes and the striate cortex. Longitudinal cognitive decline was associated with decreased gray matter density in prefrontal areas, the (medial) temporal lobes and the posterior parietal cortex. These findings suggest that prefrontal and temporal cortical regions are of particular relevance both in aging and age-related cognitive decline in healthy elderly individuals.
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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 | 0.000 | 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".