A new constrained spatiotemporal ICA method based on multi-objective optimization for fMRI data analysis.

IEEE Trans Neural Syst Rehabil Eng. 2018 Jul 19;:

Authors: Shi Y, Zeng W, Wang N, Zhao L

Abstract
Compared with independent component analysis (ICA), constrained ICA (CICA) has unique advantages in functional magnetic resonance image (fMRI) data analysis by introducing some priori information into the estimation process. However, there are still some controversies in the current CICA methods, such as how to choose the threshold parameter to restrain the similarity, and how to reduce the accuracy requirements for the priori information. In this paper, we propose a new constrained spatiotemporal ICA (CSTICA) method based on the framework of multi-objective optimization, where the inequality constraint of traditional CICA method is transformed into the objective optimization function of CSTICA, and both temporal and spatial priori information are included simultaneously. The simulated, hybrid and real fMRI data experiments are designed to evaluate the performance of the proposed CSTICA method in comparison with classical ICA and CICA methods. Compared with the traditional CICA methods, CSTICA has circumvented the problem of threshold parameter selection. Furthermore, the experimental results demonstrate that the source recovery ability of CSTICA has been improved to a certain extent especially in the cases of priori information with low accuracies. Meanwhile, the results also indicate that CSTICA reduces dependency on the accuracy of priori information.

PMID: 30028710 [PubMed - as supplied by publisher]