Women's reproductive factors predict local brain aging profiles mapped using deep neural networks.
Authors
Affiliations (9)
Affiliations (9)
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, Corwin D. Denney Research Center, University of Southern California, Los Angeles, CA, 90089, USA.
- Brain and Mental Health Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- Center for Economic and Social Research, Dana and David Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA.
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA. [email protected].
- Departments of Biomedical Engineering and Quantitative/Computational Biology, University of Southern California, Los Angeles, CA, USA. [email protected].
- Center for Healthy Brain Aging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, England, UK. [email protected].
Abstract
Deep neural networks (DNNs) trained on magnetic resonance images can estimate global brain age (GBA), which reflects women's neurological disease risk. GBA gap (GBAG), the difference between GBA and chronological age (CA), quantifies excessive global aging; local BAG (LBAG) has not been examined despite allowing voxelwise resolution. Using a novel DNN architecture, we estimate LBAG for 12,284 UK Biobank females with chronological ages (CAs) ranging between 46 and 82Β years (y) and quantify how it relates to cognition and women's health variables (CA at menopause, reproductive span, menopausal hormone therapy (HT), contraceptive use (CU), number of births). Women with longer reproductive spans (-0.042/yββ€βΞ²ββ€β-0.037/y, pβ<β0.001) had older CAs at menopause onset (-0.052/yββ€βΞ²ββ€β-0.046/y, pβ<β0.001), more births (-0.230ββ€βΞ²ββ€β-0.190 per birth, pβ<β0.001) and younger brains (more negative LBAGs, younger GBAs); a 1-unit increase in each of these variables reflects an LBAG change of Ξ² y. Left temporal lobe effects of CA at menopause onset are strongest (-0.0517ββ€βΞ²ββ€β-0.0510, pβ<β0.001). Cognitive scores are related to LBAGs negatively and most strongly in subcortical and right-hemisphere cortex (-0.021ββ€βΞ²ββ€β-0.017 per score unit, pβ<β0.01). In postmenopausal women, delayed regional brain aging is predicted by longer endogenous hormone exposure indexed by later menopause onset, longer reproductive span, and more births. This research highlights the complex role of women's health factors upon brain aging and related cognitive trajectories.