Lesion-symptom mapping studies provide insight into what areas of the brain
are involved in different aspects of cognition. This is commonly done via
behavioral testing in patients with a naturally occurring brain injury or
lesions (e.g., strokes or brain tumors). This results in high-dimensional
observational data where lesion status (present/absent) is non-uniformly
distributed with some voxels having lesions in very few (or no) subjects. In
this situation, mass univariate hypothesis tests have severe power
heterogeneity where many tests are known a priori to have little to no power.
Recent advancements in multiple testing methodologies allow researchers to
weigh hypotheses according to side-information (e.g., information on power
heterogeneity). In this paper, we propose the use of p-value weighting for
voxel-based lesion-symptom mapping (VLSM) studies. The weights are created
using the distribution of lesion status and spatial information to estimate
different non-null prior probabilities for each hypothesis test through some
common approaches. We provide a monotone minimum weight criterion which
requires minimum a priori power information. Our methods are demonstrated on
dependent simulated data and an aphasia study investigating which regions of
the brain are associated with the severity of language impairment among stroke
survivors. The results demonstrate that the proposed methods have robust error
control and can increase power. Further, we showcase how weights can be used to
identify regions that are inconclusive due to lack of power