My goal is to work with data and model fit after grand mean scaling. My definition of grand mean scaling is when all voxels and time points are scaled by a single value so we can compare results across runs/subjects. My interpretation of the help for signal_scaling is 0 will simply mean scale (does that actually mean dividing by the mean or subtracting the mean? I suppose division so parameters are closer to percent signal change?), 1 will scale each time point (aka intensity normalization…similar to modeling a global mean, right?), and (0,1) says scales with respect to voxels and time, calling this Grand Mean Scaling, but this isn’t clear to me. So it isn’t combining the mean scaling over time with that over space, right? (Help is pasted in below).
Q1: Is grand mean scaling in the help the same as my definition (scale all time points and voxels by one number)?
Q2. For further analyses I need to compare the model fit to the data. Do I have access to the scaled data that served as input to the model? In this tutorial it appear the data I feed into the model are used to compare to the model fit, but I’m assuming the data I’m feeding in have not been scaled (they are output from fmriprep) and are scaled within the model fit steps somewhere.
signal_scaling False, int or (int, int), optional
If not False, fMRI signals are scaled to the mean value of scaling_axis given, which can be 0, 1 or (0, 1). 0 refers to mean scaling each voxel with respect to time, 1 refers to mean scaling each time point with respect to all voxels & (0, 1) refers to scaling with respect to voxels and time, which is known as grand mean scaling. Incompatible with standardize (standardize=False is enforced when signal_scaling is not False). Default=0.