this is quite confusing as I was using the atlas provided in conn toolbox, which has 132 regions
However when extracting data, I was expecting a
roi * voxels map of sort.
At least what I’m trying to get is the signals in each ROI
but the result seems to averaged value of given ROI
is there a way to get the raw value of rois based on atlas in nilearn?
You could use NiftiMasker providing each time a ROI mask as an input. NiftiMasker gives you raw values unlike NiftiLabelsMasker or NiftiMapsMasker.
Thanks for the tips kamalakar!
I ended up using input_data.NiftiMasker as you suggested. The way I handled this is I have decompsed atlas image into independent roi masks.
then in the NiftiMasker, I resample the mask to the data shape and affine…
The resulting signals lengths seems a bit off tho (vs the expected number of voxels I computed.)
img = nb.load(data)
print ('roi shape', img.shape)
masker = input_data.NiftiMasker(
Is this the correct way of handling roi masks? thanks!
I don’t see any issue in the code provided that the data you are fitting on is a functional data.