Improving Lesion-Symptom Mapping

Abstract

Abstract Measures of brain activation (e.g., changes in scalp electrical potentials) have become the most popular method for inferring brain function. However, examining brain disruption (e.g., examining behavior after brain injury) can complement activation studies. Activation techniques identify regions involved with a task, whereas disruption techniques are able to discover which regions are crucial for a task. Voxel-based lesion mapping can be used to determine relationships between behavioral measures and the location of brain injury, revealing the function of brain regions. Lesion mapping can also correlate the effectiveness of neurosurgery with the location of brain resection, identifying optimal surgical targets. Traditionally, voxel-based lesion mapping has employed the chi-square test when the clinical measure is binomial and the Student's t test when measures are continuous. Here we suggest that the Liebermeister approach for binomial data is more sensitive than the chi-square test. We also suggest that a test described by Brunner and Munzel is more appropriate than the t test for nonbinomial data because clinical and neuropsychological data often violate the assumptions of the t test. We test our hypotheses comparing statistical tests using both simulated data and data obtained from a sample of stroke patients with disturbed spatial perception. We also developed software to implement these tests (MRIcron), made freely available to the scientific community.