NDLI: Identification of Brain Image Biomarkers by Optimized Selection of Multimodal Independent Components

Content Provider	IEEE Xplore Digital Library
Author	Silva, R.F. Calhoun, V.D.
Copyright Year	2008
Description	Author affiliation: Electr. & Comput. Eng., Univ. of New Mexico, Albuquerque, NM (Silva, R.F.; Calhoun, V.D.)
Abstract	The acquisition of multiple imaging modalities on the same individual in brain imaging studies has become a common practice. In functional studies, often several different tasks are performed on the same person. This motivates an analysis method that directly looks for the joint (shared) information lying within multimodal datasets. The present work uses a data fusion framework called joint independent component analysis (jICA) to yield joint (multimodal), maximally independent components (ICs) which capture the joint information from multiple modalities and enable identification of brain imaging biomarkers. We thus propose the use of a divergence metric on the estimated group distributions as an optimization factor for this framework, thus characterizing the differences in the across-group distribution functions for each modality individually and jointly as well. Special attention is being devoted to the behavior aspects of the J- divergence and Alpha divergence (with alpha= 0.5) due to their metric property and optimality, respectively.
Starting Page	61
Ending Page	64
File Size	907125
Page Count	4
File Format	PDF
ISBN	9781424422968
DOI	10.1109/SSIAI.2008.4512285
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-03-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain Biomarkers Independent component analysis Computer networks Biomedical engineering Biomedical imaging Image analysis Laboratories Information analysis Distribution functions
Content Type	Text
Resource Type	Article

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