Multimodal Image Analysis of Apparent Brain Age Identifies Physical Fitness as Predictor of Brain Maintenance

Abstract

Maintaining a youthful brain structure and function throughout life may be the single most important determinant of successful cognitive aging. In this study, we addressed heterogeneity in brain aging by making image-based brain age predictions and relating the brain age prediction gap (BAPG) to cognitive change in aging. Structural, functional, and diffusion MRI scans from 351 participants were used to train and evaluate 5 single-modal and 4 multimodal prediction models, based on 7 regression methods. The models were compared on mean absolute error and whether they were related to physical fitness and cognitive ability, measured both currently and longitudinally, as well as study attrition and years of education. Multimodal prediction models performed at a similar level as single-modal models, and the choice of regression method did not significantly affect the results. Correlation with the BAPG was found for current physical fitness, current cognitive ability, and study attrition. Correlations were also found for retrospective physical fitness, measured 10 years prior to imaging, and slope for cognitive ability during a period of 15 years. The results suggest that maintaining a high physical fitness throughout life contributes to brain maintenance and preserved cognitive ability.

Mind the gap: Performance metric evaluation in brain‐age prediction

“…The UKB results based on SVR instead of XGB are shown in Section S5, Supporting Information. In line with recent studies (Dunås et al, 2021;Liang et al, 2019), we found no evidence that choice of algorithm influenced the observed patterns: the effects of age range were highly comparable (Figures S11-S13). The trends for subsets with different sample size and age range were also highly comparable, but XGB showed more stable performance across the smallest sample fractions (Figure S14).…”

“…The difference between an individual's brain‐predicted and chronological age ( brain age delta ) provides a proxy for deviations from expected age trajectories, and has been associated with clinical risk factors (Beck et al, 2022 ; Cole, 2020 ; de Lange, Anatürk, et al, 2020 ) as well as neurological and neuropsychiatric conditions (Cole et al, 2020 ; Cole, Marioni, Harris, & Deary, 2019 ; Franke & Gaser, 2019 ; Hajek et al, 2019 ; Han et al, 2020 ; Kaufmann et al, 2019 ; Kolenic et al, 2018 ; Rokicki et al, 2021 ; Tønnesen et al, 2020 ; Van Gestel et al, 2019 ). Brain age delta estimates have also been linked to biomedical variables and lifestyle factors in healthy population cohorts (Anatürk et al, 2021 ; Cole, 2020 ; Cole, Franke, & Cherbuin, 2019 ; de Lange et al, 2019 ; Dunås, Wåhlin, Nyberg, & Boraxbekk, 2021 ; Franke et al, 2020 ; Smith et al, 2020 ), and the overall evidence supports the use of brain‐predicted age as a surrogate marker for brain integrity and health (Cole et al, 2017 ).…”

Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults

“…Moreover, including detailed assessments of dietary routines, alcohol intake, and physical exercise is vital in order to better understand the complex processes at play. For example, physical activity has been associated with lower brain age ( Dunås et al, 2021 , Sanders et al, 2021 ) and higher grey and white matter measures ( Sexton et al, 2016 ), while excess alcohol intake is well documented in influencing liver and brain health ( Agartz et al, 1999 , Rehm et al, 2010 ). Lastly, the current sample is predominantly ethnic Scandinavian and Northern European, restricting our ability to generalise to wider populations, and future studies should aim to increase the diversity in the study population.…”

Machine learning for brain age prediction: Introduction to methods and clinical applications

“…There are several sources of bias that may affect the performance of a brain age model. These include, among others, sex [ 4 , 12 , 16 , [35] , [36] , [37] , [38] , [39] ], body-mass index [ 26 , 34 , 40 ], physical exercise [ 4 , 35 , 41 , 42 ], substance use [ 20 , 26 , 35 , 43 ], and cognitive ability [ 4 , 34 , 37 , 41 , 44 ]. For clinical samples, studies commonly examine how medication [ 6 , 10 , 20 , 26 , 45 ], illness duration [ 6 , 14 , 15 , 20 , 43 , 45 ], and symptom severity [ 6 , 10 , 26 , 37 , 45 ] affect the brain-age gap.…”