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

ISTAGING Consortium, Baltimore Longitudinal Study of Aging (BLSA), Alzheimer’S Disease Neuroimaging Initiative (Adni)

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)7065
Number of pages1
JournalNature communications
Volume12
Issue number1
DOIs
StatePublished - Dec 3 2021

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

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