Grand mean scaling

Dear AFNI experts,

I have seen in several papers that grand mean scaling is a frequent step included before the first level analysis. I don’t know if this step is mandatory and when it should be included. In brief, my preprocessing pipeline includes the following steps: (i) motion corregistration, (ii) retroicor, (iii) slice timing correction, (iv) despiking, (v) normalization to the MNI template, (vI) nuisance regression, and (vII) smoothing.

Should I perform the grand mean scaling just after smoothing?

Thanks a lot in advance,

Hi, Karel-

Different softwares recommend/promote scaling in different ways. Each carries different implicit assumptions. Grand mean scaling is one particular manner for scaling data; it is not one we recommend. The FMRI scaling method implemented in AFNI’s–what happens when you use the “scale” block–translates the arbitrary units of FMRI datasets into time series that are interpretable as local BOLD percent signal change; each time series is scaled by the “baseline” (~mean) value of a given voxel. This scaling seems to be appropriate for the underlying mechanisms of BOLD-modulated signals, as well as for allowing comparisons of effect estimates (AKA “beta” values or coefficients) across a brain and across a group.

For a bit more description on this, it might useful to check out the section “Units and scaling” here:
(bioRxiv version here:
… though that full commentary by Gang might be useful (and fun?) to read anyways.