Sparse models for correlative and integrative analysis of imaging and genetic data

Dongdong Lin, Hongbao Cao, Vince D. Calhoun, Yu Ping Wang

Research output: Contribution to journalReview articlepeer-review

Abstract

The development of advanced medical imaging technologies and high-throughput genomic measurements has enhanced our ability to understand their interplay as well as their relationship with human behavior by integrating these two types of datasets. However, the high dimensionality and heterogeneity of these datasets presents a challenge to conventional statistical methods; there is a high demand for the development of both correlative and integrative analysis approaches. Here, we review our recent work on developing sparse representation based approaches to address this challenge. We show how sparse models are applied to the correlation and integration of imaging and genetic data for biomarker identification. We present examples on how these approaches are used for the detection of risk genes and classification of complex diseases such as schizophrenia. Finally, we discuss future directions on the integration of multiple imaging and genomic datasets including their interactions such as epistasis.

Original languageEnglish (US)
Pages (from-to)69-78
Number of pages10
JournalJournal of Neuroscience Methods
Volume237
DOIs
StatePublished - Nov 30 2014

Keywords

  • Classification
  • Correspondence analysis
  • Imaging genetics
  • Integration
  • Sparse modeling

ASJC Scopus subject areas

  • Neuroscience(all)

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