Frontal Lobe Volume, Function, and β-Amyloid Pathology in a Canine Model of Aging
Bibliographic record
Abstract
Application of magnetic resonance imaging (MRI) techniques reveals that human brain aging varies across cortical regions. One area particularly sensitive to normal aging is the frontal lobes. In vitro neuropathological studies and behavioral measures in a canine model of aging previously suggested that the frontal lobes of the dog might be sensitive to aging. In the present study, MRI scans were acquired to compare age-related changes in frontal lobe volume with changes in executive functions and beta-amyloid pathology in the frontal cortex of beagle dogs aged 3 months to 15 years. Decreases in total brain volume appeared only in senior dogs (aged 12 years and older), whereas frontal lobe atrophy developed earlier, appearing in the old dogs (aged 8-11 years). Hippocampal volume also declined with age, but not occipital lobe volume past maturity. Reduced frontal lobe volume correlated with impaired performance on measures of executive function, including inhibitory control and complex working memory, and with increased beta-amyloid accumulation in the frontal cortex. Age-related hippocampal atrophy also correlated with complex working memory but not inhibitory control, whereas occipital lobe volume did not correlate with any cognitive measure. These findings are consistent with the frontal lobe theory of aging in humans, which suggests that the frontal lobes and functions subserved by this region are compromised early in aging.
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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 | 0.000 |
| 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) | 0.001 | 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".