  Voxel-based lesion-symptom mapping (VLSM) is a major method for studying
brain-behavior relationships that 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). One such strategy is
using permutation to non-parametrically determine a null distribution of
cluster sizes, which is then used to establish a minimum cluster size
threshold. This strategy is intuitively appealing because it respects the
necessary spatial contiguity of stroke lesions and connects with the typical
cluster-based interpretation of VLSM results. We evaluated this strategy for
detecting true lesion-symptom relations using simulated deficit scores based on
percent damage to defined brain regions (BA 45 and BA 39) in a sample of 124
individuals with left hemisphere stroke. Even under the most conservative
settings tested here, the region identified by VLSM with cluster size
correction systematically extended well beyond the true region. As a result,
this strategy appears to be effective for ruling out situations with no true
lesion-symptom relations, but the spatial contiguity of stroke lesions may
cause identified lesion-symptom relations to extend beyond their true regions.
