The inter-subject correlation of fMRI data of different subjects performing the same fMRI task (ISC) is in principle a powerful way to localize and differentiate neural processes caused by a presented stimulus from those that spontaneously or idiosyncratically take place in each subject. The wider adoption of this method has however been impeded by the lack of widely available tools to assess the significance of the observed correlations. Several non-parametric approaches have been proposed,

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... these approaches are computationally intensive, challenging to implement, and sensitive methods to correct for multiple comparison across voxels in these approaches are not yet well established. More widely available, and computationally simple, parametric methods have been criticized on the basis that dependencies in the data could inflate false positives. Here, using three independent resting state fMRI datasets, we demonstrate that conventional parametric tests actually do provide appropriate control for false positives for inter-subject correlation analyses. This finding paves the way to a wider adoption of ISC, and empowers a wider range of neuroimagers to use ISC to tackle the challenges of naturalistic neuroscience.