Application of deep canonically correlated sparse autoencoder for the classification of schizophrenia

Gang Li, Depeng Han, Chao Wang, Wenxing Hu, Vince D. Calhoun, Yu Ping Wang

Research output: Contribution to journalArticle


Background and objective: Imaging genetics has been widely used to help diagnose and treat mental illness, e.g., schizophrenia, by combining magnetic resonance imaging of the brain and genomic information for comprehensive and systematic analysis. As a result, utilizing the correlation between magnetic resonance imaging of the brain and genomic information is becoming an important challenge. Methods: In this paper, the joint analysis of single nucleotide polymorphisms and functional magnetic resonance imaging is conducted for comprehensive study of schizophrenia. We developed a deep canonically correlated sparse autoencoder to classify schizophrenia patients from healthy controls, which can address the limitation of many existing methods such as canonical correlation analysis, deep canonical correlation analysis and sparse autoencoder. Results: The proposed deep canonically correlated sparse autoencoder can not only use complex nonlinear transformation and dimension reduction, but also achieve more accurate classifications. Our experiments showed the proposed method achieved an accuracy of 95.65% for SNP data sets and an accuracy of 80.53% for fMRI data sets. Conclusions: Experiments demonstrated higher accuracy of using the proposed method over other conventional models when classifying schizophrenia patients and healthy controls.

Original languageEnglish (US)
Article number105073
JournalComputer Methods and Programs in Biomedicine
StatePublished - Jan 1 2020
Externally publishedYes



  • Canonical correlation analysis
  • Deep canonically correlated sparse autoencoder
  • Imaging-genetic associations
  • Schizophrenia classification
  • Sparse autoencoder

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics

Cite this