We recently wrote a paper about correcting for multiple comparisons in voxel-based lesion-symptom mapping (Mirman et al., in press). Two methods did not perform very well: (1) setting a minimum cluster size based on permutations produced too much spillover beyond the true region, (2) false discovery rate (FDR) correction produced anti-conservative results for smaller sample sizes (N = 30–60). We developed an alternative solution by generalizing the standard permutation-based family-wise error correction approach, which provides a principled way to balance false positives and false negatives.
For that paper, we focused on standard "mass univariate" VLSM where the multiple comparisons are a clear problem. The multiple comparisons problem plays out differently in multivariate lesion-symptom mapping methods such as support vector regression LSM (SVR-LSM; Zhang et al., 2014, a slightly updated version is available from our github repo). Multivariate LSM methods consider all voxels simultaneously and there is not a simple relationship between voxel-level test statistics and p-values. In SVR-LSM, the voxel-level statistic is a SVR beta value and the p-values for those betas are calculated by permutation. I've been trying to work out how to deal with multiple comparisons in SVR-LSM.
For that paper, we focused on standard "mass univariate" VLSM where the multiple comparisons are a clear problem. The multiple comparisons problem plays out differently in multivariate lesion-symptom mapping methods such as support vector regression LSM (SVR-LSM; Zhang et al., 2014, a slightly updated version is available from our github repo). Multivariate LSM methods consider all voxels simultaneously and there is not a simple relationship between voxel-level test statistics and p-values. In SVR-LSM, the voxel-level statistic is a SVR beta value and the p-values for those betas are calculated by permutation. I've been trying to work out how to deal with multiple comparisons in SVR-LSM.
