A multivariate lesion symptom mapping toolbox and examination of lesion-volume biases and correction methods in lesion-symptom mapping.

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, ). 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. Specific

SUBMITTER: DeMarco AT 

PROVIDER: S-EPMC6647024 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature