Many key findings in neuroimaging studies involve similarities between brain maps. While several statistical procedures have been proposed to test correspondence between maps, there remains no consensus on the correct framing of a null hypothesis or a suitable testing approach. We propose a simple yet powerful permutation-based testing procedure for assessing similarities between two modalities using subject-level data. Our proposed method is similar to traditional permutation procedures in that it involves randomly permuting subjects to generate a null distribution. However, it differs from other recently proposed methods that have involved spherical rotations of the cortical surface or spatial autocorrelation-preserving ”surrogate” maps of the brain, which depend on strong and potentially unrealistic statistical assumptions. To address these issues, we first demonstrate in simulated data that our method is conservative in terms of type I error and has high power. Next, we illustrate that our method performs well for assessing intermodal relationships from multimodal magnetic resonance imaging data from the Philadelphia Neurodevelopmental Cohort. The proposed test rejects the null hypothesis for modalities for which there is known interdependence in structure (cortical thickness and sulcal depth) but not in cases where an association would not be predicted biologically (cortical thickness and activation on the n-back working memory task). In contrast to previous methods, our approach does not depend on strong statistical assumptions other than the independence of subjects. Notably, our method is the most flexible for analyzing intermodal correspondence within subregions of the brain and has the greatest potential to be used for generalizable statistical inference.
bioRxiv Subject Collection: Neuroscience