Nature Communications (Dec 2021)
A deep learning framework identifies dimensional representations of Alzheimer’s Disease from brain structure
- Zhijian Yang,
- Ilya M. Nasrallah,
- Haochang Shou,
- Junhao Wen,
- Jimit Doshi,
- Mohamad Habes,
- Guray Erus,
- Ahmed Abdulkadir,
- Susan M. Resnick,
- Marilyn S. Albert,
- Paul Maruff,
- Jurgen Fripp,
- John C. Morris,
- David A. Wolk,
- Christos Davatzikos,
- iSTAGING Consortium,
- Baltimore Longitudinal Study of Aging (BLSA),
- Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Affiliations
- Zhijian Yang
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania
- Ilya M. Nasrallah
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania
- Haochang Shou
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania
- Junhao Wen
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania
- Jimit Doshi
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania
- Mohamad Habes
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania
- Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania
- Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania
- Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging
- Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine
- Paul Maruff
- Florey Institute of Neuroscience and Mental Health, University of Melbourne
- Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO
- John C. Morris
- Knight Alzheimer Disease Research Center, Washington University in St. Louis
- David A. Wolk
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania
- Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania
- iSTAGING Consortium
- Baltimore Longitudinal Study of Aging (BLSA)
- Alzheimer’s Disease Neuroimaging Initiative (ADNI)
- DOI
- https://doi.org/10.1038/s41467-021-26703-z
- Journal volume & issue
-
Vol. 12,
no. 1
pp. 1 – 15
Abstract
Alzheimer’s disease is heterogeneous in its neuroimaging and clinical phenotypes. Here the authors present a semi-supervised deep learning method, Smile-GAN, to show four neurodegenerative patterns and two progression pathways providing prognostic and clinical information.
