Rate of brain aging associates with future executive function in Asian children and older adults

  1. Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore, Singapore, Singapore
  2. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
  3. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
  4. Memory Aging and Cognition Centre, National University Health System, Singapore
  5. Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
  6. Duke-NUS Medical School, Singapore
  7. Singapore Institute for Clinical Sciences (SICS), A,STAR Research Entities (ARES), Singapore
  8. Brain–Body Initiative Program, Agency for Science, Technology and Research (A,STAR), Singapore, Singapore
  9. National University Health System, Singapore
  10. Liggins Institute, University of Auckland, Auckland, New Zealand
  11. Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada, and the Strategic Research Program, A,STAR Research Entities (ARES), Singapore
  12. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
  13. N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore

Peer review process

Not revised: This Reviewed Preprint includes the authors’ original preprint (without revision), an eLife assessment, and public reviews.

Read more about eLife’s peer review process.

Editors

  • Reviewing Editor
    Saad Jbabdi
    University of Oxford, Oxford, United Kingdom
  • Senior Editor
    Jonathan Roiser
    University College London, London, United Kingdom

Joint Public Review:

Summary:

The authors of the study investigated the generalization capabilities of a deep learning brain age model across different age groups within the Singaporean population, encompassing both elderly individuals aged 55 to 88 years and children aged 4 to 11 years. The model, originally trained on a dataset primarily consisting of Caucasian adults, demonstrated a varying degree of adaptability across these age groups. For the elderly, the authors observed that the model could be applied with minimal modifications, whereas for children, significant fine-tuning was necessary to achieve accurate predictions. Through their analysis, the authors established a correlation between changes in the brain age gap and future executive function performance across both demographics. Additionally, they identified distinct neuroanatomical predictors for brain age in each group: lateral ventricles and frontal areas were key in elderly participants, while white matter and posterior brain regions played a crucial role in children. These findings underscore the authors' conclusion that brain age models hold the potential for generalization across diverse populations, further emphasizing the significance of brain age progression as an indicator of cognitive development and aging processes.

Strengths:

(1) The study tackles a crucial research gap by exploring the adaptability of a brain age model across Asian demographics (Chinese, Malay, and Indian Singaporeans), enriching our knowledge of brain aging beyond Western populations.
(2) It uncovers distinct anatomical predictors of brain aging between elderly and younger individuals, highlighting a significant finding in the understanding of age-related changes and ethnic differences.

Weaknesses:

(1) Clarity in describing the fine-tuning process is essential for improved comprehension.
(2) The analysis often limits its findings to p-values, omitting the effect sizes crucial for understanding the relationship with cognition.
(3) Employing a predictive framework for cognition using brain age could offer more insight than mere statistical correlations.
(4) Expanding the study's scope to evaluate the model's generalisability to unseen Caucasian samples is vital for establishing a comparative baseline.

In summary, this paper underscores the critical need to include diverse ethnicities in model testing and estimation.

  1. Howard Hughes Medical Institute
  2. Wellcome Trust
  3. Max-Planck-Gesellschaft
  4. Knut and Alice Wallenberg Foundation