NDLI: Three-way parallel independent component analysis for imaging genetics using multi-objective optimization

Content Provider	IEEE Xplore Digital Library
Author	Ulloa, A. Jingyu Liu Vergara, V. Jiayu Chen Calhoun, V. Pattichis, M.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of New Mexico, Albuquerque, NM, USA (Ulloa, A.; Calhoun, V.; Pattichis, M.) \|\| Mind Res. Network, Albuqueque, NM, USA (Jingyu Liu; Vergara, V.; Jiayu Chen)
Abstract	In the biomedical field, current technology allows for the collection of multiple data modalities from the same subject. In consequence, there is an increasing interest for methods to analyze multi-modal data sets. Methods based on independent component analysis have proven to be effective in jointly analyzing multiple modalities, including brain imaging and genetic data. This paper describes a new algorithm, three-way parallel independent component analysis (3pICA), for jointly identifying genomic loci associated with brain function and structure. The proposed algorithm relies on the use of multi-objective optimization methods to identify correlations among the modalities and maximally independent sources within modality. We test the robustness of the proposed approach by varying the effect size, cross-modality correlation, noise level, and dimensionality of the data. Simulation results suggest that 3p-ICA is robust to data with SNR levels from 0 to 10 dB and effect-sizes from 0 to 3, while presenting its best performance with high cross-modality correlations, and more than one subject per 1,000 variables. In an experimental study with 112 human subjects, the method identified links between a genetic component (pointing to brain function and mental disorder associated genes, including PPP3CC, KCNQ5, and CYP7B1), a functional component related to signal decreases in the default mode network during the task, and a brain structure component indicating increases of gray matter in brain regions of the default mode region. Although such findings need further replication, the simulation and in-vivo results validate the three-way parallel ICA algorithm presented here as a useful tool in biomedical data decomposition applications.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	6651
Ending Page	6654
File Size	711582
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6945153
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Brain Entropy Accuracy Genetics Matrix decomposition Independent component analysis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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