Application of Independent Component Analysis With Adaptive Density Model to Complex-Valued fMRI Data

• Published in: IEEE transactions on biomedical engineering Vol. 58; no. 10; pp. 2794 - 2803
• Main Authors: Li, Hualiang, Correa, Nicolle M., Rodriguez, Pedro A., Calhoun, Vince D., Adali, Tülay
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.10.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation model; Adaptive density model; Algorithm design and analysis; Algorithms; Applied sciences; Biological and medical sciences; Brain Mapping; complex-valued signal processing; Computerized, statistical medical data processing and models in biomedicine; Covariance matrix; Data models; Detection, estimation, filtering, equalization, prediction; Entropy; Estimation; Exact sciences and technology; functional magnetic resonance imaging; Humans; independent component analysis; Information, signal and communications theory; Magnetic Resonance Imaging - methods; Manganese; Medical management aid. Diagnosis aid; Medical sciences; Noise levels; Principal Component Analysis; ROC Curve; Signal and communications theory; Signal Processing, Computer-Assisted; Signal, noise; Studies; Telecommunications and information theory; Independent component analysis; Adaptive density model; Signal processing; Medical imagery; Functional analysis; Complex signal; complex-valued signal processing; Adaptive method; functional magnetic resonance imaging; Biomedical engineering; Functional imaging; Complex variable method
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2011.2159841

Independent component analysis (ICA) has proven quite useful for the analysis of real world datasets such as functional resonance magnetic imaging (fMRI) data, where the underlying nature of the data is hard to model. It is particularly useful for the analysis of fMRI data in its native complex form since very little is known about the nature of phase. Phase information has been discarded in most analyses as it is particularly noisy. 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 those that use fixed nonlinearity, especially when noise level is high. By adaptively matching the underlying fMRI density model, the analysis performance can be improved in terms of both the estimation of spatial maps and the task-related time courses, especially for the estimation of phase of the time course. 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.