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. 2016 Sep 23;38(2):727–739. doi: 10.1002/hbm.23412

Figure 5.

Figure 5

Multivariate Classification results for ALS versus controls. Random Forest classifier, trained on the most important (mean decrease gini ≥ 0.1) voxel DTI metrics of the CST, showed a good performance (mean accuracy: 80%) in discriminating ALS from controls. On the left side, we reported the variable importance plot, which presented the first 50 DTI‐related features (voxels) on the y‐axis, and their relevance for class detection on the x‐axis, ordered top‐to‐bottom as most to least important. On the right side, we plotted the same importance values, but for all voxels of each diffusion metrics, on a representative 3D map. In blue those voxels that have a mean decrease gini ≥ 0.1 on which the classifier was trained. Abbreviations: L, left; R, right; CST, corticospinal; FA, fractional anisotropy; MD, mean diffusivity; AD, axial diffusivity; RD, radial diffusivity. The number of feature corresponds to the location of the voxel along the tract.