Mapping human brain charts cross-sectionally and longitudinally

Author:

Di Biase Maria A.123ORCID,Tian Ye Ella1ORCID,Bethlehem Richard A. I.4ORCID,Seidlitz Jakob567,Alexander-Bloch Aaron. F.567ORCID,Yeo B.T. Thomas89101112ORCID,Zalesky Andrew113

Affiliation:

1. Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, VIC 3000, Australia

2. Department of Anatomy and Physiology, School of Biomedical Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia

3. Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02145

4. Department of Psychology, University of Cambridge, Cambridge CB2 3EB, United Kingdom

5. Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of, Philadelphia, PA 19104

6. Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104

7. Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104

8. Department of Electrical and Computer Engineering, National University of Singapore, Singapore City 119077, Singapore

9. Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore 119077, Singapore

10. N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore 119077, Singapore

11. Integrative Sciences and Engineering Programme, National University of Singapore, Singapore 119077, Singapore

12. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129

13. Department of Biomedical Engineering, Faculty of Engineering & Information Technology, The University of Melbourne, Melbourne, VIC 3000, Australia

Abstract

Brain scans acquired across large, age-diverse cohorts have facilitated recent progress in establishing normative brain aging charts. Here, we ask the critical question of whether cross-sectional estimates of age-related brain trajectories resemble those directly measured from longitudinal data. We show that age-related brain changes inferred from cross-sectionally mapped brain charts can substantially underestimate actual changes measured longitudinally. We further find that brain aging trajectories vary markedly between individuals and are difficult to predict with population-level age trends estimated cross-sectionally. Prediction errors relate modestly to neuroimaging confounds and lifestyle factors. Our findings provide explicit evidence for the importance of longitudinal measurements in ascertaining brain development and aging trajectories.

Funder

DHAC | National Health and Medical Research Council

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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