Comparison between spatial and temporal independent component analysis for blind source separation in fMRI data

Independent component analysis (ICA) is an exploratory method for analyzing spatial and temporal properties of fMRI data and requires no explicit temporal model, necessary for conventional fMRI analysis. Two varieties of ICA are employed to achieve maximal independence component in space or time yields for functional MRI (fMRI) analysis: spatial ICA (sICA) and temporal ICA (tICA). sICA is widely studied and used in signal separation of fMRI data. In this study, we compared the performance of sica and tICA to extract and separate signals with spatial and temporal independence based on simulated data. Our results reveal that sICA is able to extract and separate relatively highly independent signals. tICA can fulfill the separation of mutually independent component signal in time course and classify the temporally corresponding signal as one group in spite of having a spatially independent component. The results suggest that tICA can be applied to detect a special signal overlapping with the physiological signals by evoking other activations using the special signal.