Rate of brain aging associates with future executive function in Asian children and older adults.
Authors
Affiliations (14)
Affiliations (14)
- Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore, Singapore, Singapore.
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
- Memory Aging and Cognition Centre, National University Health System, Singapore, Singapore.
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
- Duke-NUS Medical School, Singapore, Singapore.
- Singapore Institute for Clinical Sciences (SICS), A*STAR Research Entities (ARES), Singapore, Singapore.
- Brain-Body Initiative Program, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
- National University Health System, Singapore, Singapore.
- Liggins Institute, University of Auckland, Auckland, New Zealand.
- Douglas Mental Health University Institute, McGill University, Montreal, Canada.
- Strategic Research Program, A*STAR Research Entities (ARES), Singapore, Singapore.
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore.
Abstract
Brain age has emerged as a powerful tool to understand neuroanatomical aging and its link to health outcomes like cognition. However, there remains a lack of studies investigating the rate of brain aging and its relationship to cognition. Furthermore, most brain age models are trained and tested on cross-sectional data from primarily Caucasian, adult participants. It is thus unclear how well these models generalize to non-Caucasian participants, especially children. Here, we tested a previously published deep learning model on Singaporean elderly participants (55-88 years old) and children (4-11 years old). We found that the model directly generalized to the elderly participants, but model finetuning was necessary for children. After finetuning, we found that the rate of change in brain age gap was associated with future executive function performance in both elderly participants and children. We further found that lateral ventricles and frontal areas contributed to brain age prediction in elderly participants, while white matter and posterior brain regions were more important in predicting brain age of children. Taken together, our results suggest that there is potential for generalizing brain age models to diverse populations. Moreover, the longitudinal change in brain age gap reflects developing and aging processes in the brain, relating to future cognitive function.