Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures.

Journal: medRxiv : the preprint server for health sciences
Published Date: Dec 30, 2023

Abstract

Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan. Herein, we leverage a state-of-the art deep representation learning method, Surreal-GAN, and present both methodological advances and extensive experimental results that allow us to elucidate the heterogeneity of brain aging in a large and diverse cohort of 49,482 individuals from 11 studies. Five dominant patterns of neurodegeneration were identified and quantified for each individual by their respective (herein referred to as) R-indices. Significant associations between R-indices and distinct biomedical, lifestyle, and genetic factors provide insights into the etiology of observed variances. Furthermore, baseline R-indices showed predictive value for disease progression and mortality. These five R-indices contribute to MRI-based precision diagnostics, prognostication, and may inform stratification into clinical trials.

Authors

  • Zhijian Yang
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Junhao Wen
    Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA.
  • Guray Erus
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Sindhuja T Govindarajan
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Randa Melhem
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Elizabeth Mamourian
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Yuhan Cui
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Dhivya Srinivasan
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Ahmed Abdulkadir
    Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.
  • Paraskevi Parmpi
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Katharina Wittfeld
    Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany.
  • Hans J Grabe
    Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany.
  • Robin Bülow
    Institute of Diagnostic Radiology and Neuroradiology, University of Greifswald, Germany.
  • Stefan Frenzel
    Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany.
  • Duygu Tosun
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
  • Murat Bilgel
    Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
  • Yang An
    Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
  • Dahyun Yi
    Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, Republic of Korea.
  • Daniel S Marcus
    Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
  • Pamela LaMontagne
    Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
  • Tammie L S Benzinger
    Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
  • Susan R Heckbert
    Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA.
  • Thomas R Austin
    Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA.
  • Shari R Waldstein
    Department of Psychology, University of Maryland, Baltimore County, Catonsville, MD, USA.
  • Michele K Evans
    Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA.
  • Alan B Zonderman
    Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA.
  • Lenore J Launer
    Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA.
  • Aristeidis Sotiras
    Department of Radiology and Institute of Informatics, Washington University in St. Luis, St. Luis, MO63110, USA.
  • Mark A Espeland
    Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
  • Colin L Masters
    Florey Institute, The University of Melbourne, Parkville, VIC, 3052, Australia.
  • Paul Maruff
    Florey Institute, The University of Melbourne, Parkville, VIC, 3052, Australia.
  • Jurgen Fripp
    CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia.
  • Arthur Toga
    Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA.
  • Sid O'Bryant
    Institute for Translational Research University of North Texas Health Science Center Fort Worth Texas USA.
  • Mallar M Chakravarty
    Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada.
  • Sylvia Villeneuve
    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
  • Sterling C Johnson
    Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
  • John C Morris
    Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA.
  • Marilyn S Albert
    Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Kristine Yaffe
    Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
  • Henry Völzke
    Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
  • Luigi Ferrucci
    Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, USA.
  • Nick R Bryan
    Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
  • Russell T Shinohara
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Yong Fan
    CPB/ECMO Children's Hospital, Zhejiang University School of Medicine, 310052 Hangzhou, Zhejiang, China.
  • Mohamad Habes
    Biggs Alzheimer's Institute, University of Texas San Antonio Health Science Center, USA.
  • Paris Alexandros Lalousis
    Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
  • Nikolaos Koutsouleris
    Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
  • David A Wolk
    Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
  • Susan M Resnick
    Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
  • Haochang Shou
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Ilya M Nasrallah
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Christos Davatzikos
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

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