Comparison analysis of ICA versus MCA-KSVD blind source separation on task-related fMRI data

Decomposition of working brain into meaningful clusters of regions is an important step to understand brain functionality. Blind source separation algorithms can achieve this, which results in diverse outcomes dependent upon underlying assumptions of the decomposition algorithm in use. The conventional data-driven method to detect brain functional networks is the Independent Component Analysis (ICA). The ICA method assumes decomposed components are statistically independent of each other. However, such a mathematical assumption is physiologically uncertain in regard to its applications to functional MRI (fMRI) studies. A recently proposed MCA-KSVD method, which stands for Morphological Component Analysis implemented using a K-SVD algorithm, relaxes the independence assumption imposed by the ICA method. In this study, a comprehensive comparison between the conventional ICA and MCA-KSVD methods was conducted in the presence of various simulated noise conditions. Experimental results showed that in a task-related fMRI experiment, the MCA-KSVD method successfully identified same networks as those detected by the ICA method and had advantages of better signal localization and spatial resolution. However, improper choices of the sparsity parameter and the number of trained atoms introduced phenomena, namely signal leakage, signal splitting and signal ambiguity. The MCA-KSVD method could be used as an alternative or in parallel with the ICA method, but with careful consideration of model parameter selection.