Relationship between MRI brain-age heterogeneity, cognition, genetics and Alzheimer's disease neuropathology.

Ageing Alzheimer’s disease Brain age Cognition Multimodal

Journal

EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039

Informations de publication

Date de publication:
21 Oct 2024
Historique:
received: 13 11 2023
revised: 24 09 2024
accepted: 30 09 2024
medline: 23 10 2024
pubmed: 23 10 2024
entrez: 22 10 2024
Statut: aheadofprint

Résumé

Brain ageing is highly heterogeneous, as it is driven by a variety of normal and neuropathological processes. These processes may differentially affect structural and functional brain ageing across individuals, with more pronounced ageing (older brain age) during midlife being indicative of later development of dementia. Here, we examined whether brain-ageing heterogeneity in unimpaired older adults related to neurodegeneration, different cognitive trajectories, genetic and amyloid-beta (Aβ) profiles, and to predicted progression to Alzheimer's disease (AD). Functional and structural brain age measures were obtained for resting-state functional MRI and structural MRI, respectively, in 3460 cognitively normal individuals across an age range spanning 42-85 years. Participants were categorised into four groups based on the difference between their chronological and predicted age in each modality: advanced age in both (n = 291), resilient in both (n = 260) or advanced in one/resilient in the other (n = 163/153). With the resilient group as the reference, brain-age groups were compared across neuroimaging features of neuropathology (white matter hyperintensity volume, neuronal loss measured with Neurite Orientation Dispersion and Density Imaging, AD-specific atrophy patterns measured with the Spatial Patterns of Abnormality for Recognition of Early Alzheimer's Disease index, amyloid burden using amyloid positron emission tomography (PET), progression to mild cognitive impairment and baseline and longitudinal cognitive measures (trail making task, mini mental state examination, digit symbol substitution task). Individuals with advanced structural and functional brain-ages had more features indicative of neurodegeneration and they had poor cognition. Individuals with a resilient brain-age in both modalities had a genetic variant that has been shown to be associated with age of onset of AD. Mixed brain-age was associated with selective cognitive deficits. The advanced group displayed evidence of increased atrophy across all neuroimaging features that was not found in either of the mixed groups. This is in line with biomarkers of preclinical AD and cerebrovascular disease. These findings suggest that the variation in structural and functional brain ageing across individuals reflects the degree of underlying neuropathological processes and may indicate the propensity to develop dementia in later life. The National Institute on Aging, the National Institutes of Health, the Swiss National Science Foundation, the Kaiser Foundation Research Institute and the National Heart, Lung, and Blood Institute.

Sections du résumé

BACKGROUND BACKGROUND
Brain ageing is highly heterogeneous, as it is driven by a variety of normal and neuropathological processes. These processes may differentially affect structural and functional brain ageing across individuals, with more pronounced ageing (older brain age) during midlife being indicative of later development of dementia. Here, we examined whether brain-ageing heterogeneity in unimpaired older adults related to neurodegeneration, different cognitive trajectories, genetic and amyloid-beta (Aβ) profiles, and to predicted progression to Alzheimer's disease (AD).
METHODS METHODS
Functional and structural brain age measures were obtained for resting-state functional MRI and structural MRI, respectively, in 3460 cognitively normal individuals across an age range spanning 42-85 years. Participants were categorised into four groups based on the difference between their chronological and predicted age in each modality: advanced age in both (n = 291), resilient in both (n = 260) or advanced in one/resilient in the other (n = 163/153). With the resilient group as the reference, brain-age groups were compared across neuroimaging features of neuropathology (white matter hyperintensity volume, neuronal loss measured with Neurite Orientation Dispersion and Density Imaging, AD-specific atrophy patterns measured with the Spatial Patterns of Abnormality for Recognition of Early Alzheimer's Disease index, amyloid burden using amyloid positron emission tomography (PET), progression to mild cognitive impairment and baseline and longitudinal cognitive measures (trail making task, mini mental state examination, digit symbol substitution task).
FINDINGS RESULTS
Individuals with advanced structural and functional brain-ages had more features indicative of neurodegeneration and they had poor cognition. Individuals with a resilient brain-age in both modalities had a genetic variant that has been shown to be associated with age of onset of AD. Mixed brain-age was associated with selective cognitive deficits.
INTERPRETATION CONCLUSIONS
The advanced group displayed evidence of increased atrophy across all neuroimaging features that was not found in either of the mixed groups. This is in line with biomarkers of preclinical AD and cerebrovascular disease. These findings suggest that the variation in structural and functional brain ageing across individuals reflects the degree of underlying neuropathological processes and may indicate the propensity to develop dementia in later life.
FUNDING BACKGROUND
The National Institute on Aging, the National Institutes of Health, the Swiss National Science Foundation, the Kaiser Foundation Research Institute and the National Heart, Lung, and Blood Institute.

Identifiants

pubmed: 39437659
pii: S2352-3964(24)00435-3
doi: 10.1016/j.ebiom.2024.105399
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105399

Informations de copyright

Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests TB has received investigator-initiated research awards from the NIH, the Alzheimer’s Association, the Foundation at Barnes-Jewish Hospital, Siemens Healthineers, Hyperfine and Avid Radiopharmaceuticals (a wholly-owned subsidiary of Eli Lilly and Company). She participates as a site investigator in clinical trials sponsored by Eli Lilly and Company, Biogen, Eisai, Jaansen, and Roche. She has served as a paid and unpaid consultant to Eisai, Siemens, Biogen, Janssen, Hyperfine, Merck Lilly, and Bristol-Myers Squibb. JCM has served as a paid consultant to the Barcelona Brain Research Center and the Native Alzheimer Disease-related Resource Center in Minority Aging Research. He also received payments for presentations at the AAIM meeting, Longer Life Foundation and the International Brain Health Symposium. JCM has received travel support to attend meetings including: AAIM, DIAN, AD/PD, ATRI/ADNI, ADRC, ADC, the International conference on Health Aging & Biomarkers and the International Brain Health Symposium. He has served on the advisory board for the Cure Alzheimer’s Fund and LEADS at Indiana University. IMN has received payments from Premier, Inc for participating in an advisory board, from Peerview for an educational talk, and from Subtle Medical, Inc for consulting. DW has served as a paid consultant to Qynapse, Beckman Coulter and Eli Lilly. He also received grants from the NIH and Biogen paid to his institution and received travel support from the Alzheimer's Association. SR is an NIA IRP employee and has served on the advisory board of Dementia Platforms, UK, the Canadian Consortium on Neurodegeneration in Aging and the Adult Aging Brain Connectome. She has received travel support from the McKnight Foundation to attend an annual meeting.

Auteurs

Mathilde Antoniades (M)

AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: Mathilde.Antoniades@pennmedicine.upenn.edu.

Dhivya Srinivasan (D)

AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.

Junhao Wen (J)

AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA; Laboratory of AI and Biomedical Science (LABS), University of Southern California, Los Angeles, CA, USA.

Guray Erus (G)

AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.

Ahmed Abdulkadir (A)

AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA; Department of Clinical Neuroscience, Center for Research in Neuroscience, Lausanne University Hospital, Lausanne, Switzerland.

Elizabeth Mamourian (E)

AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.

Randa Melhem (R)

AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.

Gyujoon Hwang (G)

AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee, WI, USA.

Yuhan Cui (Y)

AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.

Sindhuja Tirumalai Govindarajan (ST)

AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.

Andrew A Chen (AA)

Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States.

Zhen Zhou (Z)

AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.

Zhijian Yang (Z)

AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.

Jiong Chen (J)

AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.

Raymond Pomponio (R)

Department of Biostatistics, Colorado School of Public Health, Aurora, CO 80045, USA.

Susan Sotardi (S)

Department of Radiology, Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, USA.

Yang An (Y)

Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.

Murat Bilgel (M)

Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.

Pamela LaMontagne (P)

Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.

Ashish Singh (A)

Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, USA.

Tammie Benzinger (T)

Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.

Lori Beason-Held (L)

Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.

Daniel S Marcus (DS)

Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.

Kristine Yaffe (K)

University of California, San Francisco, CA, USA.

Lenore Launer (L)

Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, MD, USA.

John C Morris (JC)

Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA.

Duygu Tosun (D)

Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.

Luigi Ferrucci (L)

National Institute on Aging, National Institute of Health, Baltimore, MD 21224, USA.

R Nick Bryan (RN)

Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.

Susan M Resnick (SM)

Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.

Mohamad Habes (M)

AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio, TX, USA.

David Wolk (D)

Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.

Yong Fan (Y)

Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.

Ilya M Nasrallah (IM)

Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.

Haochang Shou (H)

Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, USA.

Christos Davatzikos (C)

AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: Christos.Davatzikos@pennmedicine.upenn.edu.

Classifications MeSH