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.

report = nifti_masker.generate_report()
report

NiftiMasker Applying a mask to extract time-series from Niimg-like objects. NiftiMasker is useful when preprocessing (detrending, standardization, resampling, etc.) of in-mask :term:`voxels<voxel>` is necessary. Use case: working with time series of :term:`resting-state` or task maps.

This report shows the input Nifti image overlaid with the outlines of the mask (in green). We recommend to inspect the report for the overlap between the mask and its input image.

Parameters

Parameter	Value
detrend	False
dtype	None
high_pass	None
high_variance_confounds	False
low_pass	None
mask_args	None
mask_img	None
mask_strategy	epi
memory	Memory(location=nilearn_cache/joblib)
memory_level	2
reports	True
runs	None
smoothing_fwhm	8
standardize	zscore_sample
standardize_confounds	True
t_r	None
target_affine	None
target_shape	None
verbose	0

This report was generated based on information provided at instantiation and fit time. Note that the masker can potentially perform resampling at transform time.

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()

Estimated memory usage: 925 MB