Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study
Year: 2023
Type: article
Study design: Observational Study
Abstract: Background: Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods: We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. Results: All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A-. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions: Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences. Funding: This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer's Association (SG-20-690363-DIAN).
Source: eLife
DOI: 10.7554/elife.81869
Authors: Peter R Millar, Brian A Gordon, Patrick H Luckett, Tammie LS Benzinger, Carlos Cruchaga, Anne M Fagan, Jason J Hassenstab, Richard J Perrin, Suzanne E Schindler, Ricardo F Allegri, Gregory S Day, Martin R Farlow, Hiroshi Mori, Georg Nübling, The Dominantly Inherited Alzheimer Network, Randall J Bateman, John C Morris, Beau M Ances
Institutions: Washington University in St. Louis, Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia, Mayo Clinic in Florida, Indiana University Indianapolis, Indiana University School of Medicine, Osaka Metropolitan University, Nagaoka Sutoku University, German Center for Neurodegenerative Diseases, Ludwig-Maximilians-Universität München
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,500
Funders: Alzheimer's Association, BrightFocus Foundation, Korea Health Industry Development Institute, National Institutes of Health, Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University in St. Louis, National Institute on Aging, National Center for Research Resources
Awards: G1148754291, G1560564480, G1602390028, G2669013977, G2880069213, G4427631003, HIGH PERFORMANCE BIOMEDICAL IMAGING COMPUTER RESOURCES, G4610379609, G4775855825, G4951618211, Modeling brain-predicted age in autosomal dominant Alzheimer disease, G9000008339, G9000045301, G9000060740, G9000239624, G9000266300, G9000275136, G9000322622, Alzheimer's Disease Research Center
OpenAlex ID: W4313645584
