Hi,
I have a question regarding the grand mean normalization in FEAT and how it is performed. I have searched through the archives and I refer for instance to post 026180 where the topic of intensity normalization is discussed:
"I have noticed that there are sometimes systematic differences between different BOLD
runs (same subject) in the average intensity value of the mean_func image (averaged
spatially across the entire brain), and would like to understand why. I thought the average
intensity value was "grand mean scaled" by FSL software to 10,000; the values are near
10,000, but why are they not exactly 10,000? The grand mean scaling is only for non-
background voxels, but I assume mean_func only includes non-background voxels? Does the
standard deviation of the signal in the raw data affect the calculation of the scaled values?"
In post 007632 I find
"Feat uses ip to do the filtering. At a minimum, ip performs a
grand mean scaling to 10000 (i.e. the entire 4D data is scaled such
that the global 4D mean is consistent; this is necessary for later
higher level stats)"
I want to apply the grand mean scaling in FEAT which I guess is happening in this line (taken from the log)
/usr/share/fsl/bin/fslmaths prefiltered_func_data_smooth -mul 9.89261464905 prefiltered_func_data_intnorm
My question is; how is the value 9.89261464905 computed? I tried to follow the post above and scale the non-background voxels to 10000 but then I get another scaling factor. Also, when I scale the overall mean to 10000 I get another factor than the one that appears in the log.
Best,
Erlend Hodneland
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