Inter-subject correlation (ISC) based analysis is a conceptually simple approach to analyze functional magnetic resonance imaging (fMRI) data acquired under naturalistic stimuli such as a movie. We describe and validate the statistical approaches for comparing ISCs between two groups of subjects implemented in the ISC toolbox, which is an open source software package for ISC-based analysis of fMRI data. The approaches are based on permutation tests. We validated the approaches using five

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... nt data sets from the ICBM functional reference battery tasks. In these experiments, we created two matched groups of subjects and assumed that no group difference exists. Based on the experiments, we recommend the usage of subject-wise permutations, instead of element-wise permutations following Chen et al. (2016). However, we observed that the null-distributions should be voxel-specific and not based on pooling all voxels across the brain as is typical in fMRI. This was the case even if studentized permutation tests were used. Additionally, we experimented with an fMRI dataset acquired using a dance movie stimulus for comparison of the group of adult males in autism spectrum to the matched control group. The experiment confirmed the differences between voxel-based permutation tests and global model based permutation tests.