Note
Go to the end to download the full example code. or to run this example in your browser via Binder
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}")
[get_dataset_dir] Dataset found in /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri
[get_dataset_dir] Dataset found in /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri
[get_dataset_dir] Dataset found in /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri
First functional nifti image (4D) is at: /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
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")
<nilearn.plotting.displays._slicers.YSlicer object at 0x7fac48a11910>
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.
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
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/sklearn/decomposition/_fastica.py:128: ConvergenceWarning:
FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.
Reverse masking, and display the corresponding map
components = nifti_masker.inverse_transform(components_masked)
from nilearn.image import index_img
from nilearn.image.image import mean_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()
Total running time of the script: (0 minutes 8.974 seconds)
Estimated memory usage: 925 MB