Clusterwise inference is a popular approach in neuroimaging to increase sensitivity, but most existing methods are currently restricted to the General Linear Model (GLM) for testing mean parameters. Statistical methods for testing variance components, which are critical in neuroimaging studies that involve estimation of narrow-sense heritability or test-retest reliability, are underdeveloped due to methodological and computational challenges, which would potentially lead to low power. We propose a fast and powerful test for variance components called CLEAN-V (CLEAN for testing Variance components). CLEAN-V models the global spatial dependence structure of imaging data and computes a locally powerful variance component test statistic by data-adaptively pooling neighborhood information. Correction for multiple comparisons is achieved by permutations to control family-wise error rate (FWER). Through analysis of task-fMRI data from the Human Connectome Project across five tasks and comprehensive data-driven simulations, we show that CLEAN-V outperforms existing methods in detecting test-retest reliability and narrow-sense heritability with significantly improved power, with the detected areas aligning with activation maps. The computational efficiency of CLEAN-V also speaks of its practical utility, and it is available as an R package.
Keywords: clusterwise inference; heritability; spatial autocorrelation; task-fMRI; test-retest reliability; variance component.