Application of Independent Component Analysis with Adaptive Density Model to Complex-valued fMRI Data

摘要

Independent component analysis (ICA) has proven quite useful for the analysis of functional resonance magnetic imaging (fMRI) data, especially when the underlying nature of the data is hard to model. It is especially attractive for the analysis of fMRI data in its native complex form since very little is known about the nature of phase, which is typically discarded in most analyses. In this paper, we show that a complex ICA approach using a flexible nonlinearity that adapts to the source density is the more desirable one for performing ICA of complex fMRI data compared to the those that use fixed nonlinearity. We show that by adaptively matching the underlying fMRI density model, the analysis performance can be improved in terms of both the estimation of the spatial maps and the task-related time courses. We also define a procedure for analysis and visualization of complex-valued fMRI results, which includes the construction of bivariate t-maps for multiple subjects and a complex-valued ICASSO [] scheme for evaluating the consistency of ICA algorithms.