# applied command using Camino example data: --> pointset2scheme -inputfile grad_dirs.txt -bvalue 1E9 -outputfile 4Ddwi_b1000_bvector.scheme --> image2voxel -4dimage 4Ddwi_b1000.nii.gz -outputfile dwi.Bfloat --> wdtfit 4Ddwi_b1000.nii.gz 4Ddwi_b1000_bvector.scheme -brainmask brain_mask.nii.gz -outputfile camino_wdt.nii.gz --> track -inputfile camino_wdt.nii.gz -inputmodel dt -seedfile seeds03.nii.gz -curvethresh 75 -tracker euler -stepsize 0.5 -brainmask brain_mask.nii.gz -outputfile allTracts.Bfloat --> fa -inputfile camino_wdt.nii.gz -outputfile fa.nii.gz --> conmat -inputfile allTracts.Bfloat -targetfile kirby_mindboggle_25_warped.nii.gz -targetnamefile dkt25.csv -scalarfile fa.nii.gz -tractstat min -outputroot mindboggle_25_ Then I open an terminal and run the R: --> library(gplots) --> conmat = read.csv("mindboggle_25_sc.csv") --> row.names(conmat) = colnames(conmat) --> heatCols = colorRampPalette(c("dark red", "red", "orange", "yellow", "white")) --> x = heatmap.2(as.matrix(conmat), Rowv=TRUE, Colv=TRUE, dendrogram = "none" , trace = "none", --> margins = c(16, 16), col = heatCols, breaks = seq(5, 155, 10)) ------------------------------------------ --> conmat = read.csv("mindboggle_25_ts.csv") --> row.names(conmat) = colnames(conmat) --> heatCols = colorRampPalette(c("dark red", "red", "orange", "yellow", "white")) --> x = heatmap.2(as.matrix(conmat), Rowv=TRUE, Colv=TRUE, dendrogram = "none" , trace = "none", --> margins = c(16, 16), col = heatCols, breaks = seq(5, 155, 10)) now I can see the heatmap of calculated connectivity matrix and minimum FA.