Multivariate Lesion-Symptom Mapping Using Support Vector Regression
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dmirman added two functions
optimize_parameters: find best cost and gamma paramaters using 5-fold cross-vaidation
PMU_atlas_vol_analysis_thresh: does same thing as PMU_atlas_vol_analysis, but using arbitrary user-specified p-threshold instead of FDR
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Multivariate Lesion-Symptom Mapping Using Support Vector Regression

SVR-LSM toolbox

Theory and background

Lesion analysis is a classic approach to study brain functions. Because brain function is a result of coherent activations of a collection of functionally related voxels, lesion-symptom relations are generally contributed by multiple voxels simultaneously. Although voxel-based lesion-symptom mapping (VLSM) has made substantial contributions to the understanding of brain-behavior relationships, a better understanding of the brain-behavior relationship contributed by multiple brain regions needs a multivariate lesion-symptom mapping (MLSM). The purpose of this artilce was to develop an MLSM using a machine learning-based multivariate regression algorithm: support vector regression (SVR). In the proposed SVR-LSM, the symptom relation to the entire lesion map as opposed to each isolated voxel is modeled using a nonlinear function, so the intervoxel correlations are intrinsically considered, resulting in a potentially more sensitive way to examine lesion-symptom relationships.

For detailed information about the SVR-LSM method, please see the following paper, and please cite that paper if you use the method:

Zhang, Y., Kimberg, D. Y., Coslett, H. B., Schwartz, M. F., & Wang, Z. (2014). Multivariate lesion-symptom mapping using support vector regression. Human Brain Mapping, 35(12), 5861–5876. DOI: 10.1002/hbm.22590.


This toolbox requires

  • MATLAB (The MathWork, Inc., Natick, MA), version 7.12 or higher
  • SPM (Wellcome Institute of Imaging Neuroscience, London, UK), version SPM8 or SPM12b
  • libSVM (, version 3.18

See manual for information about installation and use.