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Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study

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 A...

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Autores principales: Millar, Peter R, Gordon, Brian A, Luckett, Patrick H, Benzinger, Tammie LS, Cruchaga, Carlos, Fagan, Anne M, Hassenstab, Jason J, Perrin, Richard J, Schindler, Suzanne E, Allegri, Ricardo F, Day, Gregory S, Farlow, Martin R, Mori, Hiroshi, Nübling, Georg, Bateman, Randall J, Morris, John C, Ances, Beau M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9988262/
https://www.ncbi.nlm.nih.gov/pubmed/36607335
http://dx.doi.org/10.7554/eLife.81869
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author Millar, Peter R
Gordon, Brian A
Luckett, Patrick H
Benzinger, Tammie LS
Cruchaga, Carlos
Fagan, Anne M
Hassenstab, Jason J
Perrin, Richard J
Schindler, Suzanne E
Allegri, Ricardo F
Day, Gregory S
Farlow, Martin R
Mori, Hiroshi
Nübling, Georg
Bateman, Randall J
Morris, John C
Ances, Beau M
author_facet Millar, Peter R
Gordon, Brian A
Luckett, Patrick H
Benzinger, Tammie LS
Cruchaga, Carlos
Fagan, Anne M
Hassenstab, Jason J
Perrin, Richard J
Schindler, Suzanne E
Allegri, Ricardo F
Day, Gregory S
Farlow, Martin R
Mori, Hiroshi
Nübling, Georg
Bateman, Randall J
Morris, John C
Ances, Beau M
author_sort Millar, Peter R
collection PubMed
description 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).
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spelling pubmed-99882622023-03-07 Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study Millar, Peter R Gordon, Brian A Luckett, Patrick H Benzinger, Tammie LS Cruchaga, Carlos Fagan, Anne M Hassenstab, Jason J Perrin, Richard J Schindler, Suzanne E Allegri, Ricardo F Day, Gregory S Farlow, Martin R Mori, Hiroshi Nübling, Georg Bateman, Randall J Morris, John C Ances, Beau M eLife Medicine 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). eLife Sciences Publications, Ltd 2023-01-06 /pmc/articles/PMC9988262/ /pubmed/36607335 http://dx.doi.org/10.7554/eLife.81869 Text en © 2023, Millar et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Medicine
Millar, Peter R
Gordon, Brian A
Luckett, Patrick H
Benzinger, Tammie LS
Cruchaga, Carlos
Fagan, Anne M
Hassenstab, Jason J
Perrin, Richard J
Schindler, Suzanne E
Allegri, Ricardo F
Day, Gregory S
Farlow, Martin R
Mori, Hiroshi
Nübling, Georg
Bateman, Randall J
Morris, John C
Ances, Beau M
Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study
title Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study
title_full Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study
title_fullStr Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study
title_full_unstemmed Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study
title_short Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study
title_sort multimodal brain age estimates relate to alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9988262/
https://www.ncbi.nlm.nih.gov/pubmed/36607335
http://dx.doi.org/10.7554/eLife.81869
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