Men show increased brain aging with respect to women among Cognitively Normal Individuals
Bibliographic record
Abstract
Abstract Background There is an increasing interest in using machine learning and artificial intelligence to estimate chronological age using neuroimaging data. The gap between chronological age and estimated brain age (brain age gap, BAG) is used as a measure of accelerated/resilient brain aging. Previously, BAG has been associated with cognitive status. However, whether the BAG varies across sex and cognitive status have been less explored. The present study examines these associations and validates a voxel‐based machine learning approach based on the elastic net regression (ENR) for BAG calculation. Method Using data from the Atherosclerosis Risk in Communities Study (ARIC) study, the Wake Forest School of Medicine Alzheimer’s Disease Research Center (WFSM‐ADRC) clinical cohort and Alzheimer’s Disease Neuroimaging Initiative (ADNI), we examined associations of BAG across cognitive status and sex. We used structural MRI scans from 1853 ARIC participants (ages 67‐90, 60% females), 508 from the WFSM‐ADRC (55‐95 yo., 66% females) and 584 ADNI cognitively normal (CN) participants (55‐90 yo., 57% females). All images were aligned into a common template and the derived gray matter (GM) probability maps from ADNI MRIs were used as input to train the machine learning algorithm. Once the model was fitted the ARIC and WFSM‐ADRC GM probability maps were provided as input to the algorithm to estimate the BAG values. Finally, an age bias correction was applied. Linear regression methods were used to investigate differences between groups. Age, race, education, sex, and cognitive status were included in the model. Result We found in both ARIC and WFSM‐ADRC participants that differences in BAG values between CN‐MCI and MCI‐Dementia participants were highly significant. In addition, when we examined differences in BAG values across sex per cognitive status, we found again in both cohorts that differences were only significant for CN individuals. See Table 1 for details. Conclusion Our analyses show that our approach to estimate chronological age using high‐dimensional ENR, produces BAG values which are strongly associated with cognitive status. The increased severity of cognitive impairment is related to accelerated brain aging. Differences in BAG between men and women were significant for CN individuals only.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".