Women's reproductive factors predict local brain aging profiles mapped using deep neural networks.

Rachel Fox, Nikhil N Chaudhari, Samayan Bhattacharya, Phoebe E Imms, Teal S Eich, Wendy J Mack, Margaret Gatz, Andrei Irimia

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Article in GeroScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Rachel FoxLeonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.

Nikhil N ChaudhariLeonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.

Samayan BhattacharyaLeonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.

Phoebe E ImmsLeonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.

Teal S EichLeonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.

Wendy J MackDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

Margaret GatzCenter for Economic and Social Research, Dana and David Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA.

Andrei IrimiaLeonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA. irimia@usc.edu.ORCID http://orcid.org/0000-0002-9254-9388

Funding

National Institute of Health (US) ROI AG 079957

6 · The paper itself

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 82 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/y ≤ β ≤ -0.037/y, p < 0.001) had older CAs at menopause onset (-0.052/y ≤ β ≤ -0.046/y, p < 0.001), more births (-0.230 ≤ β ≤ -0.190 per birth, p < 0.001) and younger brains (more negative LBAGs, younger GBAs); a 1-unit increase in each of these variables reflects an LBAG change of β y. Left temporal lobe effects of CA at menopause onset are strongest (-0.0517 ≤ β ≤ -0.0510, p < 0.001). Cognitive scores are related to LBAGs negatively and most strongly in subcortical and right-hemisphere cortex (-0.021 ≤ β ≤ -0.017 per score unit, p < 0.01). 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.

Indexed as

Brain ageDeep neural networkMenopauseSex

Identifiers

PMID41779067

DOI10.1192/bjp.2025.52

What Socratic holds

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Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.