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J Neurosci Methods. 2014 Nov 30;237:69-78. doi: 10.1016/j.jneumeth.2014.09.001. Epub 2014 Sep 9.

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

Author information

1
Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA; Center of Genomics and Bioinformatics, Tulane University, New Orleans, LA, 70112, USA. Electronic address: dlin5@tulane.edu.
2
Unit on Statistical Genomics, Intramural Program of Research, National Institute of Mental Health, NIH, Bethesda 20852, USA. Electronic address: hongbao.cao@nih.gov.
3
The Mind Research Network & LBERI, Albuquerque, NM 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA. Electronic address: vcalhoun@unm.edu.
4
Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA; Center of Genomics and Bioinformatics, Tulane University, New Orleans, LA, 70112, USA. Electronic address: wyp@tulane.edu.

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.

KEYWORDS:

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

PMID:
25218561
PMCID:
PMC4194220
DOI:
10.1016/j.jneumeth.2014.09.001
[Indexed for MEDLINE]
Free PMC Article

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