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

Authors

Zhijian Yang, University of Pennsylvania
Ilya M. Nasrallah, University of Pennsylvania
Haochang Shou, University of Pennsylvania
Junhao Wen, University of Pennsylvania
Jimit Doshi, University of Pennsylvania
Mohamad Habes, University of Pennsylvania
Guray Erus, University of Pennsylvania
Ahmed Abdulkadir, University of Pennsylvania
Susan M. Resnick, National Institute on Aging (NIA)
Marilyn S. Albert, Johns Hopkins University School of Medicine
Paul Maruff, University of Melbourne
Jurgen Fripp, Commonwealth Scientific and Industrial Research Organisation
John C. Morris, Washington University School of Medicine in St. Louis
David A. Wolk, University of Pennsylvania
Christos Davatzikos, University of Pennsylvania
Yong Fan, University of Pennsylvania
Vishnu Bashyam, University of Pennsylvania
Elizabeth Mamouiran, University of Pennsylvania
Randa Melhem, University of Pennsylvania
Raymond Pomponio, University of Pennsylvania
Dushyant Sahoo, University of Pennsylvania
Singh Ashish, University of Pennsylvania
Ioanna Skampardoni, University of Pennsylvania
Lasya Sreepada, University of Pennsylvania
Dhivya Srinivasan, University of Pennsylvania
Fanyang Yu, University of Pennsylvania
Sindhuja Govindarajan Tirumalai, University of Pennsylvania
Yuhan Cui, University of Pennsylvania
Zhen Zhou, University of Pennsylvania
Katharina Wittfeld, Deutsches Zentrum für Neurodegenerative Erkrankungen
Hans J. Grabe, Deutsches Zentrum für Neurodegenerative Erkrankungen
Duygun Tosun, University of California, San Francisco
Murat Bilgel, National Institute on Aging (NIA)
Yang An, National Institute on Aging (NIA)

Document Type

Article

Publication Date

12-1-2021

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.

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