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Voxel-based lesion-symptom mapping (VLSM) is an important method for basicand translational human neuroscience research. VLSM leverages modernneuroimaging analysis techniques to build on the classic approach of examiningthe relationship between location of brain damage and cognitive deficits.Testing an association between deficit severity and lesion status in each voxelinvolves very many individual tests and requires statistical correction formultiple comparisons. Several strategies have been adapted from analysis offunctional neuroimaging data, though VLSM faces a more difficult trade-offbetween avoiding false positives and statistical power (missing true effects).Non-parametric, permutation-based methods are generally preferable because theydo not make assumptions that are likely to be violated by skewed distributionsof behavioral deficit (symptom) scores and by the necessary spatial contiguityof stroke lesions. We used simulated and real deficit scores from a sample ofapproximately 100 individuals with left hemisphere stroke to evaluate two suchpermutation-based approaches. Using permutation to set a minimum cluster sizeidentified a region that systematically extended well beyond the true region,even under the most conservative settings tested here, making it ill-suited toidentifying brain-behavior relationships. In contrast, generalizing thestandard permutation-based family-wise error correction approach provided aprincipled way to balance false positives and false negatives. Animplementation of this continuous permutation-based FWER correction method isavailable at https://gist.github.com/dmirman/05a92e0e9e0027f6fe6e528c648143d7

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