An Improved ICA Method on Resting State fMRI Data Analysis

Abstract

For the resting state functional magnetic resonance imaging (fMRI) data, it has an important significance in clinical medicine to extract the functional connectivity regions (Default Mode Network, DMN). Independent component analysis (ICA) is an excellent method to detect the DMN. It has been popularly applied to the resting state fMRI data. However, ICA decomposition is realized by updating the objective function iteratively till convergence to further estimate the independent sources. The iterative process requires to a set of initial vectors which is generated by randomness. Thus the randomness of initialization brings about the randomness of the results. So the results acquired by ICA are not fixed in different decompositions. To mitigate the problem, we proposed an improved method, named ATGP- ICA, to do fMRI data analysis. The new method can generate a set of fixed initial vectors with automatic target generation process (ATGP) instead of being produced randomly. Our experimental results show that the ATGP-ICA method can not only detect the DMN network of resting state fMRI data, but also can eliminate the randomness of the classical ICA method.