Corrections for multiple comparisons in voxel-based lesion-symptom mapping

Abstract

Voxel-based lesion-symptom mapping (VLSM) is an important method for basic and translational human neuroscience research. VLSM leverages modern neuroimaging analysis techniques to build on the classic approach of examining the relationship between location of brain damage and cognitive deficits. Testing an association between deficit severity and lesion status in each voxel involves very many individual tests and requires statistical correction for multiple comparisons. Several strategies have been adapted from analysis of functional neuroimaging data, though VLSM faces a more difficult trade-off between avoiding false positives and statistical power (missing true effects). Non-parametric, permutation-based methods are generally preferable because they do not make assumptions that are likely to be violated by skewed distributions of behavioral deficit (symptom) scores and by the necessary spatial contiguity of stroke lesions. We used simulated and real deficit scores from a sample of approximately 100 individuals with left hemisphere stroke to evaluate two such permutation-based approaches. Using permutation to set a minimum cluster size identified a region that systematically extended well beyond the true region, even under the most conservative settings tested here, making it ill-suited to identifying brain-behavior relationships. In contrast, generalizing the standard permutation-based family-wise error correction approach provided a principled way to balance false positives and false negatives. An implementation of this continuous permutation-based FWER correction method is available at https://gist.github.com/dmirman/05a92e0e9e0027f6fe6e528c648143d7