Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 15, Issue -, Pages 413-416Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2008.922513
Keywords
entropy; fMRI; genetic association; independent component analysis (ICA); multimodal process; parallel ICA
Categories
Funding
- NIBIB NIH HHS [R01 EB005846-04, R01 EB000840, R01 EB005846-03, R01 EB006841, R01 EB005846, R01 EB005846-01, R01 EB000840-02, R01 EB000840-01, R01 EB000840-06, R01 EB000840-03, R01 EB005846-02, R01 EB020407, R01 EB000840-04, R01 EB000840-05, R01 EB000840-07] Funding Source: Medline
- NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING [R01EB005846, R01EB000840] Funding Source: NIH RePORTER
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Relationships between genomic data and functional brain images are of great interest but require new analysis approaches to integrate the high-dimensional data types. This letter presents an extension of a technique called parallel independent component analysis (paraICA), which enables the joint analysis of multiple modalities including interconnections between them. We extend our earlier work by allowing for multiple interconnections and by providing important overfitting controls. Performance was assessed by simulations under different conditions, and indicated reliable results can be extracted by properly balancing overfitting and underfitting. An application to functional magnetic resonance images and single nucleotide polymorphism array produced interesting findings.
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