I was just wondering whether the scaling is done across run-specific samples per voxel (dimension), or is done across all samples in training set. Let’s say I have fMRI bold estimates, one beta per condition per run, and I have 3 runs. Thus, in a leave-1-run-out-crossval SVM analysis, my training data consist of 4 samples (because training data = 2 runs). Should I scale the data independently for each run in the first place (this results in -0.7071 and +0.7071, only 2 possible values in all dimensions), or I should scale the data across the 4 training samples? (assuming that I don’t scale my test data). Any general comments or suggestions on scaling for SVM are highly welcomed as well!