NiftiLabelsMasker fit_transform gives only a single array per image

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.


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Thanks for the tips kamalakar!

hi Kamalaker,

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.)

sample code:

    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.