Dear Samira,
You should be able too use the spm_summarise function for this:
in = spm_summarise('spmT.nii','roi.nii',@mean)
Then create another mask, nootroi.nii, with ImCalc where you specify two
images, mask.nii and roi.nii, and use the expression 'i1 & ~i2' then:
out = spm_summarise('spmT.nii','notroi.nii',@mean)
Best regards,
Guillaume.
On 13/02/2021 20:18, Samira Mellah wrote:
>
> Hi SPM experts,
>
> For a prediction study with a task-fmri dataset. I would like to perform
> "Goodness-of-fit" (GOF) analysis to obtain a GOF score for each elderly
> participant with the activation pattern for a young group for the same
> contrast.
>
> GOF was defined as the difference of the mean t-score of all voxels
> inside vs. outside the young activation mask.
>
> First, I generated a binarized mask of all ROIs from a 1-test analysis
> in the young group) with statistical thresholding (cluster-level FWE
> corrected p <0.05).
>
> How to obtain the mean t-score of all voxel inside and outside the mask
> for each old participant in SPM12?
>
> best regards
>
> Samira
>
> --
> */Samira Mellah/*
--
Guillaume Flandin, PhD
Wellcome Centre for Human Neuroimaging
UCL Queen Square Institute of Neurology
London WC1N 3BG
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