Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns.

Journal: Cell reports. Medicine
Published Date: Dec 21, 2021

Abstract

Trajectories of cognitive decline vary considerably among individuals with mild cognitive impairment (MCI). To address this heterogeneity, subtyping approaches have been developed, with the objective of identifying more homogeneous subgroups. To date, subtyping of MCI has been based primarily on cognitive measures, often resulting in indistinct boundaries between subgroups and limited validity. Here, we introduce a subtyping method for MCI based solely upon brain atrophy. We train a deep learning model to differentiate between Alzheimer's disease (AD) and cognitively normal (CN) subjects based on whole-brain MRI features. We then deploy the trained model to classify MCI subjects based on whole-brain gray matter resemblance to AD-like or CN-like patterns. We subsequently validate the subtyping approach using cognitive, clinical, fluid biomarker, and molecular imaging data. Overall, the results suggest that atrophy patterns in MCI are sufficiently heterogeneous and can thus be used to subtype individuals into biologically and clinically meaningful subgroups.

Authors

  • Kichang Kwak
    Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
  • Kelly S Giovanello
    Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
  • Andrea Bozoki
    Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
  • Martin Styner
    Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA.
  • Eran Dayan
    Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

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