Language Fluency and Premature Brain Aging
Bibliographic record
Abstract
Abstract Background Age‐related changes in the brain are often associated with general cognitive decline. Cognitive aging affects the ability to produce spoken language, with older adults experiencing increased difficulties in word‐finding1 and verbal fluency2,3. Neurodegenerative diseases such as Alzheimer’s disease are often attributed to an acceleration of typical aging processes (i.e., premature aging). Accelerated age‐related atrophy is present in mild cognitive impairment4,5 and premature brain aging may contribute to declining cognitive skills6,7. A recent study demonstrated that the disparity between chronological age and brain age (based on measures of cortical integrity) is a good predictor of early cognitive impairment8. Our aim was to extend these findings by investigating the role of brain age in overall cognition as well as language in particular. Method Participants included 216 healthy controls (age range 20–79) from the Aging Brain Cohort at the University of South Carolina9. Structural T1‐weighted MRI scans were collected, and participants completed the Montreal Cognitive Assessment (MoCA10). Brain age was calculated based on T1‐weighted structural data using the BrainAgeR analysis pipeline (github.com/james‐cole/brainageR). A brain age gap estimate (BrainAGE)11 was calculated as the difference between brain age and chronological age. This was used as the primary measure of advanced/delayed brain health, where positive values represent premature brain aging. Result Estimated brain age differences ranged from 22 years younger to 14 years older than chronological age. Pearson correlations revealed a negative correlation between MoCA total score and chronological age (r(209) = ‐0.41, p<0.001), but not in partial correlations using BrainAGE, accounting for chronological age (r(207) = ‐0.003, p = 0.48.). Regarding our language measure (words generated during the MoCA fluency task), we observed a negative correlation with BrainAGE, corrected for chronological age (r(208) = ‐0.133, p = 0.03), but no correlation with chronological age (r(210) = ‐0.04, p = 0.58). Conclusion Our data support the hypothesis that differences between chronological and brain age are related to cognition and language, and highlight the utility/importance of brain age in understanding cognitive impairment. Interestingly, findings suggest that there may be a particularly strong relationship between the brain age gap estimation and MRI‐based measure of brain aging and language in particular.
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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 | 0.004 |
| 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 | 0.001 |
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".