Identification of discriminative imaging proteomics associations in Alzheimer’s disease via a novel sparse correlation model

Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalConference article

3 Scopus citations


Brain imaging and protein expression, from both cerebrospinal fluid and blood plasma, have been found to provide complementary information in predicting the clinical outcomes of Alzheimer’s disease (AD). But the underlying associations that contribute to such a complementary relationship have not been previously studied yet. In this work, we will perform an imaging proteomics association analysis to explore how they are related with each other. While traditional association models, such as Sparse Canonical Correlation Analysis (SCCA), can not guarantee the selection of only disease-relevant biomarkers and associations, we propose a novel discriminative SCCA (denoted as DSCCA) model with new penalty terms to account for the disease status information. Given brain imaging, proteomic and diagnostic data, the proposed model can perform a joint association and multi-class discrimination analysis, such that we can not only identify disease-relevant multimodal biomarkers, but also reveal strong associations between them. Based on a real imaging proteomic data set, the empirical results show that DSCCA and traditional SCCA have comparable association performances. But in a further classification analysis, canonical variables of imaging and proteomic data obtained in DSCCA demonstrate much more discrimination power toward multiple pairs of diagnosis groups than those obtained in SCCA.

Original languageEnglish (US)
Pages (from-to)94-104
Number of pages11
JournalPacific Symposium on Biocomputing
Issue number212679
StatePublished - Jan 1 2017
Event22nd Pacific Symposium on Biocomputing, PSB 2017 - Kohala Coast, United States
Duration: Jan 4 2017Jan 8 2017


  • Alzheimer’s disease
  • Canonical correlation analysis
  • Imaging genomics
  • Multi-class discrimination
  • Proteomics

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics

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