Short term evaluation of brain activities in fMRI data by spatiotemporal independent component analysis

Abstract

At present, Independent Components Analysis (ICA) represents the most important and efficient approach for extraction of independent non-Gaussian linearly mixed signals. This statistic-informative technique has been successfully applied to fMRI temporal data, which can be considered as an overlapped mixture of hemodynamic signals, physiological perturbations and noise. In this paper an extension to spatial application of ICA (sICA) was performed. The results confirmed that the spatial approach permits to obtain improved identification of brain activities, even when the temporal length of data is reduced.