Abstract
Resting state functional connectivity (rs-fMRI) is impaired early in persons who subsequently develop Alzheimer’s disease (AD) dementia. This impairment may be leveraged to aid investigation of the pre-clinical phase of AD. We developed a model that predicts brain age from resting state (rs)-fMRI data, and assessed whether genetic determinants of AD, as well as beta-amyloid (Aβ) pathology, can accelerate brain aging. Using data from 1340 cognitively unimpaired participants between 18–94 years of age from multiple sites, we showed that topological properties of graphs constructed from rs-fMRI can predict chronological age across the lifespan. Application of our predictive model to the context of pre-clinical AD revealed that the pre-symptomatic phase of autosomal dominant AD includes acceleration of functional brain aging. This association was stronger in individuals having significant Aβ pathology.
| Original language | English |
|---|---|
| Article number | 5346 |
| Journal | Nature communications |
| Volume | 12 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2021 |
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In: Nature communications, Vol. 12, No. 1, 5346, 12.2021.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Accelerated functional brain aging in pre-clinical familial Alzheimer’s disease
AU - Alzheimer’s Disease Neuroimaging Initiative (ADNI)
AU - Dominantly Inherited Alzheimer Network (DIAN) Study Group
AU - Pre-symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer’s Disease (PREVENT-AD) Research Group
AU - Gonneaud, Julie
AU - Baria, Alex T.
AU - Pichet Binette, Alexa
AU - Gordon, Brian
AU - Chhatwal, Jasmeer P.
AU - Cruchaga, Carlos
AU - Jucker, Mathias
AU - Levin, Johannes
AU - Salloway, Stephen
AU - Farlow, Martin
AU - Gauthier, Serge
AU - Benzinger, Tammie L.S.
AU - Morris, John
AU - Bateman, Randall J.
AU - Breitner, John C.S.
AU - Poirier, Judes
AU - Vachon-Presseau, Etienne
AU - Villeneuve, Sylvia
N1 - Funding Information: The authors would like to thank the members of the Villeneuve Lab, J. Tremblay-Mercier, A. Labonté, D. Dea, C. Madjar, and all the PREVENT-AD center for participants’ recruitment, data acquisition, and data management (a complete listing of PREVENT-AD Research Group can be found in the PREVENT-AD database: https:// preventad.loris.ca/acknowledgements/acknowledgements.php?date=[2019-07-30]); the members of the Brain Imaging Center of the Douglas Mental Health Institute for MRI acquisitions; the member of the Cyclotron and PET Units of the Montreal Neurological Institute for PET tracer production and acquisitions; K. Paumier, R. Hornbeck, P. Wang, S. Flores, B. Esposito, and A. Renton for their help in DIAN data access and for providing DIAN procedure information, as well as all the centers involved in DIAN data acquisitions. A complete listing of the DIAN Study Group can be found in the Supplementary Notes. This manuscript has been reviewed by DIAN Study investigators for scientific content and consistency of data interpretation with previous DIAN Study publications. We acknowledge the altruism of the participants and their families and contributions of the DIAN research and support staff at each of the participating sites for their contributions to this study. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. Finally, we would like to thank all the participants and their family for their invaluable help. This work was supported by two Canada Research Chairs (S.V. and J.B.), a Canadian Institutes of Health Research project grant PJT-148963 (S.V.), a Canada Fund for Innovation (S.V.), an Alzheimer’s Association Research Grant NIRG-397028 (S.V.), the Lemaire foundation (J.P. and S.V.), the J.L. Levesque Foundation (J.P.), a joint Alzheimer Society of Canada and a Brain Canada Research grant NIG-17-08 (S.V.), a StoP-AD fellowship (J.G.), a Quebec Bio-Imaging Network scholarship (J.G.), a joint FRQ-S and Alzheimer Society of Canada scholarship (A.P.B.). The PREVENT-AD was funded by a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the Fonds de Recherche du Québec—Santé (FRQ-S), an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, the Canada Fund for Innovation and Genome Quebec Innovation Center (J.B. and J.P.). Data collection and sharing for this project was also supported by: (1) Data collection and sharing for this project was supported by The Dominantly Inherited Alzheimer’s Network (DIAN, U19AG032438) funded by the National Institute on Aging (NIA), the German Center for Neurodegenerative Diseases (DZNE), Raul Carrea Institute for Neurological Research (FLENI), Partial support by the Research and Development Grants for Dementia from Japan Agency for Medical Research and Development, AMED, and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI). (2) The Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neu-rotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Publisher Copyright: © 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - Resting state functional connectivity (rs-fMRI) is impaired early in persons who subsequently develop Alzheimer’s disease (AD) dementia. This impairment may be leveraged to aid investigation of the pre-clinical phase of AD. We developed a model that predicts brain age from resting state (rs)-fMRI data, and assessed whether genetic determinants of AD, as well as beta-amyloid (Aβ) pathology, can accelerate brain aging. Using data from 1340 cognitively unimpaired participants between 18–94 years of age from multiple sites, we showed that topological properties of graphs constructed from rs-fMRI can predict chronological age across the lifespan. Application of our predictive model to the context of pre-clinical AD revealed that the pre-symptomatic phase of autosomal dominant AD includes acceleration of functional brain aging. This association was stronger in individuals having significant Aβ pathology.
AB - Resting state functional connectivity (rs-fMRI) is impaired early in persons who subsequently develop Alzheimer’s disease (AD) dementia. This impairment may be leveraged to aid investigation of the pre-clinical phase of AD. We developed a model that predicts brain age from resting state (rs)-fMRI data, and assessed whether genetic determinants of AD, as well as beta-amyloid (Aβ) pathology, can accelerate brain aging. Using data from 1340 cognitively unimpaired participants between 18–94 years of age from multiple sites, we showed that topological properties of graphs constructed from rs-fMRI can predict chronological age across the lifespan. Application of our predictive model to the context of pre-clinical AD revealed that the pre-symptomatic phase of autosomal dominant AD includes acceleration of functional brain aging. This association was stronger in individuals having significant Aβ pathology.
UR - http://www.scopus.com/inward/record.url?scp=85115969502&partnerID=8YFLogxK
U2 - 10.1038/s41467-021-25492-9
DO - 10.1038/s41467-021-25492-9
M3 - Article
C2 - 34504080
AN - SCOPUS:85115969502
SN - 2041-1723
VL - 12
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 5346
ER -