Independent component analysis (ICA), a data-driven approach utilizing high-order statistical moments to find maximally independent sources, has found fruitful application in functional magnetic resonance imaging (fMRI). ICA, being a blind source separation technique, does not require any explicit constraints upon the fMRI time courses. In some cases, such as for the analysis of a rapid eventrelated paradigm, it would be useful to incorporate paradigm information into the ICA analysis in a flexible way. In this paper, we present an approach for constrained or semi-blind ICA (sbICA) analysis of fMRI data. We demonstrate the performance of our approach using simulations and fMRI data of an auditory oddball paradigm. Simulation results suggest that 1) a regression approach slightly outperforms ICA when prior information is accurate and ICA outperforms the general linear modeling (GLM) approach when prior information is not completely accurate, 2) prior information improves the robustness of ICA in the presence of noise, and 3) and ICA analysis using prior information with weak constraints can outperform a regression approach when the prior information is not completely accurate.