Smoothing an image

Here we smooth a mean EPI image and plot the result

As we vary the smoothing FWHM, note how we decrease the amount of noise, but also lose spatial details. In general, the best amount of smoothing for a given analysis depends on the spatial extent of the effects that are expected.

We then show how to smooth a SurfaceImage.

from nilearn.datasets import fetch_development_fmri
from nilearn.image import mean_img, smooth_img
from nilearn.plotting import plot_epi, show

data = fetch_development_fmri(n_subjects=1)

# Print basic information on the dataset
print(
    f"First subject functional nifti image (4D) are located at: {data.func[0]}"
)

first_epi_file = data.func[0]

# First compute the mean image, from the 4D series of image
mean_func = mean_img(first_epi_file)

[fetch_development_fmri] Dataset found in
/home/runner/nilearn_data/development_fmri
[fetch_development_fmri] Dataset found in
/home/runner/nilearn_data/development_fmri/development_fmri
[fetch_development_fmri] Dataset found in
/home/runner/nilearn_data/development_fmri/development_fmri
First subject functional nifti image (4D) are located at: /home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz

Then we smooth, with a varying amount of smoothing, from none to 20mm by increments of 5mm

for fwhm in [None, 5, 10, 15, 20, 25]:
    smoothed_img = smooth_img(mean_func, fwhm)
    title = "No smoothing" if fwhm is None else f"Smoothing {fwhm} mm"
    plot_epi(
        smoothed_img,
        title=title,
        colorbar=True,
        cmap="gray",
        vmin=0,
    )

show()

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from nilearn.datasets import (
    load_fsaverage,
    load_fsaverage_data,
    load_sample_motor_activation_image,
)
from nilearn.plotting import plot_surf_stat_map
from nilearn.surface import SurfaceImage

fsaverage_meshes = load_fsaverage()

stat_img = load_sample_motor_activation_image()

curvature = load_fsaverage_data(data_type="curvature")

surface_image = SurfaceImage.from_volume(
    mesh=fsaverage_meshes["pial"],
    volume_img=stat_img,
)

for fwhm in [None, 5, 10, 15, 20, 25]:
    smoothed_img = smooth_img(surface_image, fwhm)
    title = "No smoothing" if fwhm is None else f"Smoothing {fwhm} mm"
    plot_surf_stat_map(
        surf_mesh=fsaverage_meshes["inflated"],
        stat_map=smoothed_img,
        title=title,
        threshold=1.0,
        vmax=8,
        bg_map=curvature,
    )

show()

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Estimated memory usage: 107 MB