Brain age gap is associated with cognitive abilities in captive chimpanzees
Bibliographic record
Abstract
Brain age gap refers to the difference between chronological and brain age based on computational models derived from various neuroimaging phenotypes. Studies in humans have reported that brain age gap is a biological measure that is sensitive to the effects of genetic, environmental and health-related variables on the pace of aging. Here, for the first time, we tested whether estimates of brain age gap could be derived from neuroimaging data obtained in chimpanzees and whether they were associated with different cognitive and motor phenotypes. Archived measures of cortical thickness and surface area were obtained from 34 brain regions in a sample of 215 chimpanzees from the National Chimpanzee Brain Resource. Brain age gap values were computed and tested for their association with individual variation in cognition and motor function. The mean absolute average age gap was ~ 6 years in chimpanzees, a value that overlaps with reports in human subjects. Chimpanzees with "older" brain ages performed more poorly on a measure of cognition compared to individuals with "younger" brains, after controlling for the sex and rearing effects. Like in humans, brain age gap can be used as a valid biomarker of brain aging in chimpanzees and is sensitive to individual differences in cognition.
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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 | 0.001 |
| 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".