Simple example of NiftiMasker use

Here is a simple example of automatic mask computation using the nifti masker. The mask is computed and visualized.

Note

If you are using Nilearn with a version older than 0.9.0, then you should either upgrade your version or import maskers from the input_data module instead of the maskers module.

That is, you should manually replace in the following example all occurrences of:

from nilearn.maskers import NiftiMasker

with:

from nilearn.input_data import NiftiMasker

Retrieve the brain development functional dataset

from nilearn import datasets

dataset = datasets.fetch_development_fmri(n_subjects=1)
func_filename = dataset.func[0]

# print basic information on the dataset
print(f"First functional nifti image (4D) is at: {func_filename}")

Compute the mask

from nilearn.maskers import NiftiMasker

# As this is raw movie watching based EPI, the background is noisy and we
# cannot rely on the 'background' masking strategy. We need to use the 'epi'
# one
nifti_masker = NiftiMasker(
    standardize="zscore_sample",
    mask_strategy="epi",
    memory="nilearn_cache",
    memory_level=2,
    smoothing_fwhm=8,
)
nifti_masker.fit(func_filename)
mask_img = nifti_masker.mask_img_

Visualize the mask using the plot_roi method

from nilearn.image.image import mean_img
from nilearn.plotting import plot_roi

# calculate mean image for the background
mean_func_img = mean_img(func_filename, copy_header=True)

plot_roi(mask_img, mean_func_img, display_mode="y", cut_coords=4, title="Mask")

Visualize the mask using the ‘generate_report’ method This report can be displayed in a Jupyter Notebook, opened in-browser using the .open_in_browser() method, or saved to a file using the .save_as_html(output_filepath) method.

report = nifti_masker.generate_report()
report

Preprocess data with the NiftiMasker

nifti_masker.fit(func_filename)
fmri_masked = nifti_masker.transform(func_filename)
# fmri_masked is now a 2D matrix, (n_voxels x n_time_points)

Run an algorithm

from sklearn.decomposition import FastICA

n_components = 10
ica = FastICA(n_components=n_components, random_state=42)
components_masked = ica.fit_transform(fmri_masked.T).T

Reverse masking, and display the corresponding map

components = nifti_masker.inverse_transform(components_masked)

from nilearn.image import index_img

# Visualize results
from nilearn.plotting import plot_stat_map, show

# calculate mean image for the background
mean_func_img = mean_img(func_filename, copy_header=True)

plot_stat_map(
    index_img(components, 0),
    mean_func_img,
    display_mode="y",
    cut_coords=4,
    title="Component 0",
)

show()

Estimated memory usage: 0 MB