Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief

► We apply pattern classifiers towards decoding belief cognitive states from fMRI. ► Independent Component (IC) Analysis was used for dimension reduction. ► Six ICs formed a diagnostic, parsimonious feature subset. ► Informative IC spatial masks were mapped forward. ► Classification accuracy of belief vs. disbelief was robust and varied by algorithm.