TY - JOUR
T1 - Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages
T2 - a cross-sectional observational study
AU - Dominantly Inherited Alzheimer Network
AU - Millar, Peter R.
AU - Gordon, Brian A.
AU - Luckett, Patrick H.
AU - Benzinger, Tammie L.S.
AU - Cruchaga, Carlos
AU - Fagan, Anne M.
AU - Hassenstab, Jason J.
AU - Perrin, Richard J.
AU - Schindler, Suzanne E.
AU - Allegri, Ricardo F.
AU - Day, Gregory S.
AU - Farlow, Martin R.
AU - Mori, Hiroshi
AU - Nübling, Georg
AU - Bateman, Randall J.
AU - Morris, John C.
AU - Ances, Beau M.
N1 - Funding Information:
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, U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer’s Association (SG-20-690363-DIAN).
Funding Information:
T he authors declare no competing interests. JC Morris is funded by NIH grants # P30 A G066444; P01AG003991; P01AG026276; U19 AG032438; and U19 AG024904. Neither Dr. M orris nor his family owns stock or has equity interest (outside of mutual funds or other e xternally directed accounts) in any pharmaceutical or biotechnology company. Dr. Bateman is o n the scientific advisory board of C2N Diagnostics and reports research support from Abbvie,
Funding Information:
Funding T his research was funded by grants from the National Institutes of Health (P01-AG026276, P01-A G03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1 S10RR022984-01A1) and the BrightFocus Foundation (A2022014F), with generous support f rom the Paula and Rodger O. Riney Fund and the Daniel J. Brennan MD Fund. Data collection a nd sharing for this project was supported by The Dominantly Inherited Alzheimer Network ( DIAN, U19-AG032438) funded by the National Institute on Aging (NIA),the Alzheimer’s A ssociation (SG-20-690363-DIAN), the German Center for Neurodegenerative Diseases ( DZNE), Raul Carrea Institute for Neurological Research (FLENI), Partial support by the R esearch and Development Grants for Dementia from Japan Agency for Medical Research and D evelopment, AMED, and the Korea Health Technology R&D Project through the Korea Health I ndustry Development Institute (KHIDI), Spanish Institute of Health Carlos III (ISCIII), C anadian Institutes of Health Research (CIHR), Canadian Consortium of Neurodegeneration and A ging, Brain Canada Foundation, and Fonds de Recherche du Québec – Santé.
Publisher Copyright:
© 2023, eLife Sciences Publications Ltd. All rights reserved.
PY - 2023/1/6
Y1 - 2023/1/6
N2 - 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).
AB - 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).
KW - Alzheimer disease
KW - brain aging
KW - human
KW - machine learning
KW - medicine
KW - neuroscience
KW - resting-state functional connectivity
KW - structural MRI
UR - http://www.scopus.com/inward/record.url?scp=85147889501&partnerID=8YFLogxK
U2 - 10.7554/eLife.81869
DO - 10.7554/eLife.81869
M3 - Article
C2 - 36607335
AN - SCOPUS:85147889501
SN - 2050-084X
VL - 12
JO - eLife
JF - eLife
ER -