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}")

[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 functional nifti image (4D) is at: /home/runner/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=1,
    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 0x7f6d3315caf0>

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.

The mask includes 24256 voxels (16.4 %) of the image.

	Value
Parameter
clean_args	None
cmap	gray
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	1
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

/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/sklearn/decomposition/_fastica.py:128: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.
  warnings.warn(

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: 863 MB