Translational Psychiatry (Oct 2024)
Genetic and clinical correlates of two neuroanatomical AI dimensions in the Alzheimer’s disease continuum
- Junhao Wen,
- Zhijian Yang,
- Ilya M. Nasrallah,
- Yuhan Cui,
- Guray Erus,
- Dhivya Srinivasan,
- Ahmed Abdulkadir,
- Elizabeth Mamourian,
- Gyujoon Hwang,
- Ashish Singh,
- Mark Bergman,
- Jingxuan Bao,
- Erdem Varol,
- Zhen Zhou,
- Aleix Boquet-Pujadas,
- Jiong Chen,
- Arthur W. Toga,
- Andrew J. Saykin,
- Timothy J. Hohman,
- Paul M. Thompson,
- Sylvia Villeneuve,
- Randy Gollub,
- Aristeidis Sotiras,
- Katharina Wittfeld,
- Hans J. Grabe,
- Duygu Tosun,
- Murat Bilgel,
- Yang An,
- Daniel S. Marcus,
- Pamela LaMontagne,
- Tammie L. Benzinger,
- Susan R. Heckbert,
- Thomas R. Austin,
- Lenore J. Launer,
- Mark Espeland,
- Colin L. Masters,
- Paul Maruff,
- Jurgen Fripp,
- Sterling C. Johnson,
- John C. Morris,
- Marilyn S. Albert,
- R. Nick Bryan,
- Susan M. Resnick,
- Luigi Ferrucci,
- Yong Fan,
- Mohamad Habes,
- David Wolk,
- Li Shen,
- Haochang Shou,
- Christos Davatzikos
Affiliations
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), University of Southern California
- Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
- Ilya M. Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
- Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
- Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
- Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
- Ahmed Abdulkadir
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
- Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
- Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
- Ashish Singh
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
- Mark Bergman
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
- Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine
- Erdem Varol
- Department of Statistics, Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University
- Zhen Zhou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
- Aleix Boquet-Pujadas
- Laboratory of AI and Biomedical Science (LABS), University of Southern California
- Jiong Chen
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
- Arthur W. Toga
- Laboratory of NeuroImaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California
- Andrew J. Saykin
- Radiology and Imaging Sciences, Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana Alzheimer’s Disease Research Center and the Melvin and Bren Simon Cancer Center, Indiana University School of Medicine
- Timothy J. Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt Genetics Institute, Department of Neurology, Vanderbilt University Medical Center
- Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California
- Sylvia Villeneuve
- Douglas Mental Health University Institute, McGill University
- Randy Gollub
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School
- Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Washington University School of Medicine
- Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald
- Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald
- Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California
- Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH
- Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH
- Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine
- Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine
- Tammie L. Benzinger
- Department of Radiology, Washington University School of Medicine
- Susan R. Heckbert
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington
- Thomas R. Austin
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington
- Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging
- Mark Espeland
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine
- Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne
- Paul Maruff
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne
- Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO
- Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health
- John C. Morris
- Knight Alzheimer Disease Research Center, Washington University in St. Louis
- Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine
- R. Nick Bryan
- Department of Radiology, University of Pennsylvania
- Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH
- Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital
- Yong Fan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
- Mohamad Habes
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio
- David Wolk
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
- Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine
- Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
- Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
- DOI
- https://doi.org/10.1038/s41398-024-03121-5
- Journal volume & issue
-
Vol. 14,
no. 1
pp. 1 – 14
Abstract
Abstract Alzheimer’s disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised representation learning technique known as Surreal-GAN, through which we identified two latent dimensional representations of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the “diffuse-AD” (R1) dimension shows widespread brain atrophy, and the “MTL-AD” (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE ε4) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were “druggable genes” for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOE ε4, amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction—driven by genes different from APOE—which may collectively contribute to the early pathogenesis of AD. All results are publicly available at https://labs-laboratory.com/medicine/ .