Brain parcellation using nilearn's NiftiLabelsMasker

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1

I am using nilearn’s NiftiLabelsMasker for brain parcellation. First I define a masker object as nilearn.input_data.NiftiLabelsMasker and yeo_17 brain atlas as labels_img. After that I perform masker.fit_transform(func_img,confounds) to get the time series.

The shape of the functional nii file and the the yeo_17 nii file do not match. Do I need to first scale the atlas file to the shape of functional file or does nilearn does it internally?

2

Nilearn will do it. you can choose whether the image is resampled to the atlas resolution or vice-versa with the resampling_target parameter. From the NiftiLabelsMasker docstring:

    resampling_target: {"data", "labels", None}, optional.
        Gives which image gives the final shape/size. For example, if
        `resampling_target` is "data", the atlas is resampled to the
        shape of the data if needed. If it is "labels" then mask_img
        and images provided to fit() are resampled to the shape and
        affine of maps_img. "None" means no resampling: if shapes and
        affines do not match, a ValueError is raised. Defaults to "data".
3

Okay. That answers my question. Thanks a lot.

4

Hello,

I have similar problem. I have an atlas with 466 labels. When I am using NifliLabelmasker, I end up with 462 ROI instead of 466.
I am trying to understand how I am “losing” those 4 ROIs and which ROIs are lost.
I tried the resampling_target=“labels” but It doesn’t work and I still end up with 462 ROI.

Any guess of how to solve this problem ?

Thanks a lot !

5

Hi, can you share the atlas image?

6

Hi Jerome,

Actually it is a fusion of masks. I send it to you by email (can’t upload images on Neurostar I think ? - I realize that we are from the same lab !). I used this mask already with another dataset and it worked fine. I think that it might be related to my image. There is a drop in the signal in the orbitofrontal cortex.

image1
image1742×574 56.2 KB

I suspect that it might be related to this. I wanted to know if there is a way to “force” nilearn to inlcude all the labels from the atlas.

Thanks a lot,

Best,

Charles

7

it seems the atlas contains some empty regions (regions that have 0 voxels assigned to them):

from nilearn import image

atlas = image.load_img("Atlas_ting_in_HNU_mul_brainmask_98coverage.nii.gz")
atlas_values = set(image.get_data(atlas).astype(int).ravel())
print(
    f"atlas contains {len(atlas_values)} "
    "non-empty regions (including background = 0)"
)
print(
    "empty regions:",
    set(range(max(atlas_values) + 1)).difference(atlas_values),
)

prints

atlas contains 463 non-empty regions (including background = 0)
empty regions: {456, 441, 457, 461}

the label masker won’t assign a dimension to empty regions in the atlas image, so the output dimension is 462 (number of non-empty regions excluding the background)

after transforming an image, the regions corresponding to dimensions in the masked image can be looked up in masker.labels_: it looks like [1, ..., 440, 442, ... ,455, 458, ..., 466]

1 Like
8

there is no way to force nilearn to keep these empty regions. Note they couldn’t be assigned a meaningful value anyway – the masker computes the mean within each region, but the mean across 0 voxels is not defined

1 Like
9

Hi there, I’m facing a similar issue, but the atlas does not seem to contain empty regions. For context, same data with a different mask did not give an issue, and same data with this specific mask only gave issues (missing rois) with some subjects.

You can find the yeo_atlas, the nifti file for which the atlas ‘works’, and the one for which it ‘doesn’t work’ on this link

Any ideas are appreciated!