Comparing the means of 2 images#

The goal of this example is to illustrate the use of the function nilearn.image.math_img with a list of images as input. We compare the means of 2 resting state 4D images. The mean of the images could have been computed with nilearn nilearn.image.mean_img function.

Fetching 2 subject movie watching brain development fMRI datasets.

from nilearn import datasets

dataset = datasets.fetch_development_fmri(n_subjects=2)

Print basic information on the adhd subjects resting state datasets.

print(f"Subject 1 resting state dataset at: {dataset.func[0]}")
print(f"Subject 2 resting state dataset at: {dataset.func[1]}")
Subject 1 resting state dataset at: /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
Subject 2 resting state dataset at: /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar001_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz

Comparing the means of the 2 movie watching datasets.

from nilearn import image, plotting

result_img = image.math_img(
    "np.mean(img1, axis=-1) - np.mean(img2, axis=-1)",
    img1=dataset.func[0],
    img2=dataset.func[1],
)

plotting.plot_stat_map(
    result_img, title="Comparing means of 2 resting state 4D images."
)
plotting.show()
plot compare mean image

Total running time of the script: (0 minutes 2.922 seconds)

Estimated memory usage: 766 MB

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