A deep learning framework identifies dimensional representations of Alzheimer's Disease from brain structure.

Journal: Nature communications
Published Date: Dec 3, 2021

Abstract

Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical heterogeneity contrasted against normal brain structure, to identify disease subtypes through neuroimaging signatures. When applied to regional volumes derived from T1-weighted MRI (two studies; 2,832 participants; 8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified four patterns or axes of neurodegeneration. Applying this framework to longitudinal data revealed two distinct progression pathways. Measures of expression of these patterns predicted the pathway and rate of future neurodegeneration. Pattern expression offered complementary performance to amyloid/tau in predicting clinical progression. These deep-learning derived biomarkers offer potential for precision diagnostics and targeted clinical trial recruitment.

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.
  • 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.
  • 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.
  • 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.
  • Jimit Doshi
    Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
  • Mohamad Habes
    Biggs Alzheimer's Institute, University of Texas San Antonio Health Science Center, 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.
  • Ahmed Abdulkadir
    Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.
  • Susan M Resnick
    Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
  • Marilyn S Albert
    Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • 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.
  • John C Morris
    Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA.
  • David A Wolk
    Department of Neurology, 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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