Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment.

ABSTRACT:

Background

In Alzheimer's Diseases (AD) research, multimodal imaging analysis can unveil complementary information from multiple imaging modalities and further our understanding of the disease. One application is to discover disease subtypes using unsupervised clustering. However, existing clustering methods are often applied to input features directly, and could suffer from the curse of dimensionality with high-dimensional multimodal data. The purpose of our study is to identify multimodal imaging-driven subtypes in Mild Cognitive Impairment (MCI) participants using a multiview learning framework based on Deep Generalized Canonical Correlation Analysis (DGCCA), to learn shared latent representation with low dimensions from 3 neuroimaging modalities.

Results

DGCCA applies no

SUBMITTER: Feng Y 

PROVIDER: S-EPMC9523890 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature