  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
