Language Fluency and Premature Brain Aging

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.