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

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

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.

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Detalles Bibliográficos
Autores principales: 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)
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Science
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Acceso en línea:https://doaj.org/article/bc622eef4986462d950294c8155877a6
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Sumario: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.

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