A multivariate lesion symptom mapping toolbox and examination of lesion‐volume biases and correction methods in lesion‐symptom mapping. Issue 11 (4th July 2018) - DeMarco, Andrew T.; Turkeltaub, Peter E.
Abstract: 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, 2014 ). 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.