1. A deep learning framework identifies dimensional representations of Alzheimer's Disease from brain structure.
- Author
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Yang, Zhijian, Nasrallah, Ilya M, Shou, Haochang, Wen, Junhao, Doshi, Jimit, Habes, Mohamad, Erus, Guray, Abdulkadir, Ahmed, Resnick, Susan M, Albert, Marilyn S, Maruff, Paul, Fripp, Jurgen, Morris, John C, Wolk, David A, Davatzikos, Christos, iSTAGING Consortium, Baltimore Longitudinal Study of Aging (BLSA), and Alzheimer’s Disease Neuroimaging Initiative (ADNI)
- Subjects
iSTAGING Consortium ,Baltimore Longitudinal Study of Aging ,Alzheimer’s Disease Neuroimaging Initiative ,Brain ,Humans ,Alzheimer Disease ,Magnetic Resonance Imaging ,Cluster Analysis ,Case-Control Studies ,Longitudinal Studies ,Image Processing ,Computer-Assisted ,Aged ,Aged ,80 and over ,Middle Aged ,Female ,Male ,Neuroimaging ,Healthy Volunteers ,Cognitive Dysfunction ,Deep Learning ,Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) ,Acquired Cognitive Impairment ,Biomedical Imaging ,Brain Disorders ,Alzheimer's Disease ,Dementia ,Clinical Research ,Aging ,Neurosciences ,Neurodegenerative ,Detection ,screening and diagnosis ,4.1 Discovery and preclinical testing of markers and technologies ,Neurological ,Good Health and Well Being - 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.
- Published
- 2021