.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/06_manipulating_images/plot_nifti_simple.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via Binder. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_06_manipulating_images_plot_nifti_simple.py: Simple example of NiftiMasker use ================================= Here is a simple example of automatic mask computation using the nifti masker. The mask is computed and visualized. .. include:: ../../../examples/masker_note.rst .. GENERATED FROM PYTHON SOURCE LINES 13-14 Retrieve the brain development functional dataset .. GENERATED FROM PYTHON SOURCE LINES 14-23 .. code-block:: Python 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}") .. rst-class:: sphx-glr-script-out .. code-block:: none [fetch_development_fmri] Dataset found in /home/remi-gau/nilearn_data/development_fmri [fetch_development_fmri] Dataset found in /home/remi-gau/nilearn_data/development_fmri/development_fmri [fetch_development_fmri] Dataset found in /home/remi-gau/nilearn_data/development_fmri/development_fmri First functional nifti image (4D) is at: /home/remi-gau/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz .. GENERATED FROM PYTHON SOURCE LINES 24-25 Compute the mask .. GENERATED FROM PYTHON SOURCE LINES 25-41 .. code-block:: Python 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, verbose=1, ) nifti_masker.fit(func_filename) mask_img = nifti_masker.mask_img_ .. rst-class:: sphx-glr-script-out .. code-block:: none [NiftiMasker.fit] Loading data from '/home/remi-gau/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz' [NiftiMasker.fit] Computing mask ________________________________________________________________________________ [Memory] Calling nilearn.masking.compute_epi_mask... compute_epi_mask('/home/remi-gau/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', verbose=0) _________________________________________________compute_epi_mask - 0.3s, 0.0min [NiftiMasker.fit] Resampling mask ________________________________________________________________________________ [Memory] Calling nilearn.image.resampling.resample_img... resample_img(, target_affine=None, target_shape=None, copy=False, interpolation='nearest') _____________________________________________________resample_img - 0.0s, 0.0min [NiftiMasker.fit] Finished fit .. GENERATED FROM PYTHON SOURCE LINES 42-43 Visualize the mask using the plot_roi method .. GENERATED FROM PYTHON SOURCE LINES 43-51 .. code-block:: Python 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) plot_roi(mask_img, mean_func_img, display_mode="y", cut_coords=4, title="Mask") .. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_nifti_simple_001.png :alt: plot nifti simple :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_nifti_simple_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 52-56 Visualize the mask using the 'generate_report' method .. include:: ../../../examples/report_note.rst .. GENERATED FROM PYTHON SOURCE LINES 56-59 .. code-block:: Python report = nifti_masker.generate_report() report .. raw:: html

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` is necessary. Use case: working with time series of :term:`resting-state` or task maps.

No image provided.

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.

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

Value
Parameter
cmap gray
detrend False
high_variance_confounds False
mask_strategy epi
memory nilearn_cache
memory_level 1
reports True
smoothing_fwhm 8
standardize zscore_sample
standardize_confounds True
verbose 1

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



.. GENERATED FROM PYTHON SOURCE LINES 60-61 Preprocess data with the NiftiMasker .. GENERATED FROM PYTHON SOURCE LINES 61-65 .. code-block:: Python 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) .. rst-class:: sphx-glr-script-out .. code-block:: none [NiftiMasker.fit] Loading data from '/home/remi-gau/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz' [NiftiMasker.fit] Computing mask [NiftiMasker.fit] Resampling mask [NiftiMasker.fit] Finished fit ________________________________________________________________________________ [Memory] Calling nilearn.maskers.nifti_masker.filter_and_mask... filter_and_mask('/home/remi-gau/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', , { 'clean_args': None, 'clean_kwargs': {}, 'cmap': 'gray', 'detrend': False, 'dtype': None, 'high_pass': None, 'high_variance_confounds': False, 'low_pass': None, 'reports': True, 'runs': None, 'smoothing_fwhm': 8, 'standardize': 'zscore_sample', 'standardize_confounds': True, 't_r': None, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=1, confounds=None, sample_mask=None, copy=True, dtype=None, sklearn_output_config=None) [NiftiMasker.wrapped] Loading data from [NiftiMasker.wrapped] Smoothing images [NiftiMasker.wrapped] Extracting region signals [NiftiMasker.wrapped] Cleaning extracted signals __________________________________________________filter_and_mask - 0.8s, 0.0min .. GENERATED FROM PYTHON SOURCE LINES 66-67 Run an algorithm .. GENERATED FROM PYTHON SOURCE LINES 67-75 .. code-block:: Python from sklearn.decomposition import FastICA n_components = 10 ica = FastICA( n_components=n_components, random_state=42, tol=0.001, max_iter=2000 ) components_masked = ica.fit_transform(fmri_masked.T).T .. rst-class:: sphx-glr-script-out .. code-block:: none /home/remi-gau/github/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/sklearn/decomposition/_fastica.py:127: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations. warnings.warn( .. GENERATED FROM PYTHON SOURCE LINES 76-77 Reverse masking, and display the corresponding map .. GENERATED FROM PYTHON SOURCE LINES 77-96 .. code-block:: Python 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) plot_stat_map( index_img(components, 0), mean_func_img, display_mode="y", cut_coords=4, title="Component 0", ) show() .. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_nifti_simple_002.png :alt: plot nifti simple :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_nifti_simple_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none [NiftiMasker.inverse_transform] Computing image from signals ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([[ 1.251303, ..., 0.053215], ..., [ 0.754042, ..., -1.335595]], shape=(10, 24256)), ) ___________________________________________________________unmask - 0.2s, 0.0min .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 10.733 seconds) **Estimated memory usage:** 854 MB .. _sphx_glr_download_auto_examples_06_manipulating_images_plot_nifti_simple.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/nilearn/nilearn/0.13.0?urlpath=lab/tree/notebooks/auto_examples/06_manipulating_images/plot_nifti_simple.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_nifti_simple.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_nifti_simple.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_nifti_simple.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_