TY - GEN
T1 - Identification of brain image biomarkers by optimized selection of multimodal independent components
AU - Silva, Rogers F.
AU - Calhoun, Vince D.
PY - 2008
Y1 - 2008
N2 - 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 α = 0.5) due to their metric property and optimality, respectively.
AB - 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 α = 0.5) due to their metric property and optimality, respectively.
UR - http://www.scopus.com/inward/record.url?scp=77950803529&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77950803529&partnerID=8YFLogxK
U2 - 10.1109/SSIAI.2008.4512285
DO - 10.1109/SSIAI.2008.4512285
M3 - Conference contribution
AN - SCOPUS:77950803529
SN - 9781424422975
T3 - Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation
SP - 61
EP - 64
BT - 2008 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2008 - Proceedings
T2 - 2008 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2008
Y2 - 24 March 2008 through 26 March 2008
ER -