TY - GEN
T1 - Performance of complex-valued ICA algorithms for fMRI analysis
T2 - Importance of taking full diversity into account
AU - Du, Wei
AU - Fu, Geng Shen
AU - Calhoun, Vince D.
AU - Adah, Tulay
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/1/28
Y1 - 2014/1/28
N2 - Independent component analysis (ICA) has been effectively used for the analysis of functional magnetic resonance imaging (fMRI) data, and recently for the analysis of fMRI data in its native complex-valued form. When performing complex ICA of fMRI analysis, it is desirable to take all three types of diversity - statistical property - that are present in the complex fMRI data into account: non-Gaussianity, sample dependence and noncircularity. In this paper, we study the performance of complex ICA by entropy rate bound minimization (CERBM) algorithm for fMRI analysis that takes all these three types of diversity into account. We perform a thorough comparison of its performance with that of complex Infomax (CInfomax) and complex ICA by entropy bound minimization (CEBM), two other important choices. While evaluating the performance of ICA algorithms for fMRI analysis, there are a number of challenges, including the lack of ground truth of fMRI data and inconsistent estimates due to the iterative nature of ICA algorithms. In this work, we also propose a statistical framework that utilizes an objective criterion to evaluate consistency of ICA algorithms. Using this framework, we show that CERBM leads to significant improvement in the estimations of components of interest in terms of providing lower mutual information rate, higher active scores and more numbers of activated voxels than those of CInfomax and CEBM, and the corresponding time courses present best task-relatedness with the paradigm.
AB - Independent component analysis (ICA) has been effectively used for the analysis of functional magnetic resonance imaging (fMRI) data, and recently for the analysis of fMRI data in its native complex-valued form. When performing complex ICA of fMRI analysis, it is desirable to take all three types of diversity - statistical property - that are present in the complex fMRI data into account: non-Gaussianity, sample dependence and noncircularity. In this paper, we study the performance of complex ICA by entropy rate bound minimization (CERBM) algorithm for fMRI analysis that takes all these three types of diversity into account. We perform a thorough comparison of its performance with that of complex Infomax (CInfomax) and complex ICA by entropy bound minimization (CEBM), two other important choices. While evaluating the performance of ICA algorithms for fMRI analysis, there are a number of challenges, including the lack of ground truth of fMRI data and inconsistent estimates due to the iterative nature of ICA algorithms. In this work, we also propose a statistical framework that utilizes an objective criterion to evaluate consistency of ICA algorithms. Using this framework, we show that CERBM leads to significant improvement in the estimations of components of interest in terms of providing lower mutual information rate, higher active scores and more numbers of activated voxels than those of CInfomax and CEBM, and the corresponding time courses present best task-relatedness with the paradigm.
KW - AOD task
KW - ICA
KW - complex-valued fMRI
KW - statistical property
UR - http://www.scopus.com/inward/record.url?scp=84949927452&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949927452&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2014.7025733
DO - 10.1109/ICIP.2014.7025733
M3 - Conference contribution
AN - SCOPUS:84949927452
T3 - 2014 IEEE International Conference on Image Processing, ICIP 2014
SP - 3612
EP - 3616
BT - 2014 IEEE International Conference on Image Processing, ICIP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -