9.7.7. Simple example of NiftiMasker use

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

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('First functional nifti image (4D) is at: %s' % func_filename)

Out:

First functional nifti image (4D) is at: /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz

Compute the mask

from nilearn.input_data 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=True, 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.plotting import plot_roi
from nilearn.image.image import mean_img

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

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

Out:

<nilearn.plotting.displays.YSlicer object at 0x7fbedd39a280>

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 voxels is necessary. Use case: working with time series of 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
sample_mask	None
smoothing_fwhm	8
standardize	True
standardize_confounds	True
t_r	None
target_affine	None
target_shape	None
verbose	0

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

Out:

/home/nicolas/anaconda3/envs/nilearn/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.
  warnings.warn('FastICA did not converge. Consider increasing '

Reverse masking, and display the corresponding map

components = nifti_masker.inverse_transform(components_masked)

# Visualize results
from nilearn.plotting import plot_stat_map, show
from nilearn.image import index_img
from nilearn.image.image import mean_img

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

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

show()