Relationship between MRI brain-age heterogeneity, cognition, genetics and Alzheimer’s disease neuropathologyResearch in context
Mathilde Antoniades,
Dhivya Srinivasan,
Junhao Wen,
Guray Erus,
Ahmed Abdulkadir,
Elizabeth Mamourian,
Randa Melhem,
Gyujoon Hwang,
Yuhan Cui,
Sindhuja Tirumalai Govindarajan,
Andrew A. Chen,
Zhen Zhou,
Zhijian Yang,
Jiong Chen,
Raymond Pomponio,
Susan Sotardi,
Yang An,
Murat Bilgel,
Pamela LaMontagne,
Ashish Singh,
Tammie Benzinger,
Lori Beason-Held,
Daniel S. Marcus,
Kristine Yaffe,
Lenore Launer,
John C. Morris,
Duygu Tosun,
Luigi Ferrucci,
R. Nick Bryan,
Susan M. Resnick,
Mohamad Habes,
David Wolk,
Yong Fan,
Ilya M. Nasrallah,
Haochang Shou,
Christos Davatzikos
Affiliations
Mathilde Antoniades
AI2D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA; Corresponding author. 3700 Hamilton Walk, Philadelphia, PA 19104, USA.
Dhivya Srinivasan
AI2D, 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
AI2D, 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
AI2D, 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
AI2D, 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
AI2D, 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
AI2D, 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
AI2D, 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
AI2D, 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
AI2D, 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
Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States
Zhen Zhou
AI2D, 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
AI2D, 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
AI2D, 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
Department of Biostatistics, Colorado School of Public Health, Aurora, CO 80045, USA
Susan Sotardi
Department of Radiology, Children’s Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, USA
Yang An
Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
Murat Bilgel
Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
Pamela LaMontagne
Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
Ashish Singh
Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, USA
Tammie Benzinger
Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
Lori Beason-Held
Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
Daniel S. Marcus
Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
Kristine Yaffe
University of California, San Francisco, CA, USA
Lenore Launer
Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, MD, USA
John C. Morris
Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
Duygu Tosun
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
Luigi Ferrucci
National Institute on Aging, National Institute of Health, Baltimore, MD 21224, USA
R. Nick Bryan
Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
Susan M. Resnick
Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
Mohamad Habes
AI2D, 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
Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
Yong Fan
Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
Ilya M. Nasrallah
Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
Haochang Shou
Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, USA
Christos Davatzikos
AI2D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA; Corresponding author. 3700 Hamilton Walk, Philadelphia, PA 19104, USA.
Summary: 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: 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: 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: 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: 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.
