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

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). 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, 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. Comparison with the widely-used parametric false discovery rate (FDR) correction showed that FDR produces anti-conservative results at smaller sample sizes (N = 30–60). An implementation of the continuous permutation-based FWER correction method described here is included in the lesymap package for lesion-symptom mapping (https://dorianps.github.io/LESYMAP/). •Tested multiple comparison correction strategies for lesion-symptom mapping.•Cluster-based correction is particularly prone to spill-over beyond true regions.•False discovery rate correction is anti-conservative at smaller sample sizes.•Generalized permutation-based family-wise error correction offers flexible control.