# 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.