A Multivariate Lesion Symptom Mapping Toolbox and Examination of Lesion-volume Biases and Correction Methods in Lesion-symptom Mapping

Lesion-symptom mapping has become a cornerstone of neuroscience research seeking to localize cognitive function in the brain by examining the sequelae of brain lesions. Recently, multivariate lesion-symptom mapping methods have emerged, such as support vector regression, which simultaneously consider many voxels at once when determining whether damaged regions contribute to behavioral deficits (Zhang, Kimberg, Coslett, Schwartz, & Wang, ). Such multivariate approaches are capable of identifying complex dependences that traditional mass-univariate approach cannot. Here, we provide a new toolbox for support vector regression lesion-symptom mapping (SVR-LSM) that provides a graphical interface and enhances the flexibility and rigor of analyses that can be conducted using this method. Specifically, the toolbox provides cluster-level family-wise error correction via permutation testing, the capacity to incorporate arbitrary nuisance models for behavioral data and lesion data and makes available a range of lesion volume correction methods including a new approach that regresses lesion volume out of each voxel in the lesion maps. We demonstrate these new tools in a cohort of chronic left-hemisphere stroke survivors and examine the difference between results achieved with various lesion volume control methods. A strong bias was found toward brain wide lesion-deficit associations in both SVR-LSM and traditional mass-univariate voxel-based lesion symptom mapping when lesion volume was not adequately controlled. This bias was corrected using three different regression approaches; among these, regressing lesion volume out of both the behavioral score and the lesion maps provided the greatest sensitivity in analyses.