Abstract
Spatial Independent component analysis (sICA) has been successfully used to analyze functional magnetic resonance (fMRI) data. However, the application of ICA was limited in multi-task fMRI data due to the potential spatial dependence between task-related components. Long et al. (2009) proposed ICA with linear projection (ICAp) method and demonstrated its capacity to solve the interaction among task-related components in multi-task fMRI data of single subject. However, it's unclear that how to perform ICAp over a group of subjects. In this study, we proposed a group analysis framework on multi-task fMRI data by combining ICAp with the temporal concatenation method reported by Calhoun (2001). The results of real fMRI experiment containing multiple visual processing tasks demonstrated the feasibility and effectiveness of the group ICAp method. Moreover, compared to the GLM method, the group ICAp method is more sensitive to detect the regions specific to each task.
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