.. 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 13-18 Retrieve the brain development functional dataset ------------------------------------------------- We fetch the dataset and print some basic information about it. .. GENERATED FROM PYTHON SOURCE LINES 18-26 .. code-block:: Python from nilearn.datasets import fetch_development_fmri dataset = fetch_development_fmri(n_subjects=1) func_filename = dataset.func[0] 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/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 .. GENERATED FROM PYTHON SOURCE LINES 27-34 Compute the mask ---------------- As the input image is an EPI image, the background is noisy and we cannot rely on the ``'background'`` masking strategy. We need to use the ``'epi'`` one. .. GENERATED FROM PYTHON SOURCE LINES 34-45 .. code-block:: Python from nilearn.maskers import NiftiMasker masker = NiftiMasker( standardize="zscore_sample", mask_strategy="epi", memory="nilearn_cache", memory_level=1, smoothing_fwhm=8, verbose=1, ) .. GENERATED FROM PYTHON SOURCE LINES 46-48 .. include:: ../../../examples/html_repr_note.rst .. GENERATED FROM PYTHON SOURCE LINES 49-51 .. code-block:: Python masker .. raw:: html 
NiftiMasker(mask_strategy='epi', memory='nilearn_cache', smoothing_fwhm=8,
                standardize='zscore_sample', verbose=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


.. GENERATED FROM PYTHON SOURCE LINES 52-54 .. code-block:: Python masker.fit(func_filename) .. rst-class:: sphx-glr-script-out .. code-block:: none [NiftiMasker.fit] Loading data from '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-p ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz' [NiftiMasker.fit] Computing mask ________________________________________________________________________________ [Memory] Calling nilearn.masking.compute_epi_mask... compute_epi_mask('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', verbose=0) _________________________________________________compute_epi_mask - 0.4s, 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 .. raw:: html 
NiftiMasker(mask_strategy='epi', memory='nilearn_cache', smoothing_fwhm=8,
                standardize='zscore_sample', verbose=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


.. GENERATED FROM PYTHON SOURCE LINES 55-61 .. note :: You can also note that after fitting, the HTML representation of the estimator looks different than before before fitting. .. GENERATED FROM PYTHON SOURCE LINES 61-63 .. code-block:: Python masker .. raw:: html 
NiftiMasker(mask_strategy='epi', memory='nilearn_cache', smoothing_fwhm=8,
                standardize='zscore_sample', verbose=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


.. GENERATED FROM PYTHON SOURCE LINES 64-76 Visualize the mask ------------------ We can quickly get an idea about the estimated mask for this functional image by plotting the mask. We get the estimated mask from the ``mask_img_`` attribute of the masker: the final ``_`` ofd this attribute name means it was generated by the :meth:`~nilearn.maskers.NiftiMasker.fit` method. We can then plot it using the :func:`~nilearn.plotting.plot_roi` function with the mean functional image as background. .. GENERATED FROM PYTHON SOURCE LINES 76-87 .. code-block:: Python from nilearn.image.image import mean_img from nilearn.plotting import plot_roi, show mask_img = masker.mask_img_ mean_func_img = mean_img(func_filename) plot_roi(mask_img, mean_func_img, display_mode="y", cut_coords=4, title="Mask") show() .. 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 /home/runner/work/nilearn/nilearn/examples/06_manipulating_images/plot_nifti_simple.py:85: UserWarning: You are using the 'agg' matplotlib backend that is non-interactive. No figure will be plotted when calling matplotlib.pyplot.show() or nilearn.plotting.show(). You can fix this by installing a different backend: for example via pip install PyQt6 .. GENERATED FROM PYTHON SOURCE LINES 88-95 Visualize the masker report --------------------------- More information can be obtained about the masker and its mask by generating a masker report. This can be done using the :meth:`~nilearn.maskers.NiftiMasker.generate_report` method. .. GENERATED FROM PYTHON SOURCE LINES 95-97 .. code-block:: Python report = masker.generate_report() .. GENERATED FROM PYTHON SOURCE LINES 98-100 .. include:: ../../../examples/report_note.rst .. GENERATED FROM PYTHON SOURCE LINES 101-103 .. code-block:: Python 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. 

NiftiMasker(mask_strategy='epi', memory='nilearn_cache', smoothing_fwhm=8,
                standardize='zscore_sample', verbose=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

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 104-109 Preprocess data with the NiftiMasker ------------------------------------ We extract the data from the nifti image and turn it into a numpy array. .. GENERATED FROM PYTHON SOURCE LINES 109-112 .. code-block:: Python fmri_masked = masker.transform(func_filename) print(fmri_masked.shape) .. rst-class:: sphx-glr-script-out .. code-block:: none ________________________________________________________________________________ [Memory] Calling nilearn.maskers.nifti_masker.filter_and_mask... filter_and_mask('/home/runner/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 - 1.1s, 0.0min (168, 24256) .. GENERATED FROM PYTHON SOURCE LINES 113-114 ``fmri_masked`` is now a 2D numpy array, (n_voxels x n_time_points). .. GENERATED FROM PYTHON SOURCE LINES 116-126 Run an algorithm and visualize the results ------------------------------------------ Given that we now have a numpy array, we can then pass the data the wide range of algorithm. Here we will just do an independent component analysis, turned the extracted component back into images (using :meth:`~nilearn.maskers.NiftiMasker.inverse_transform`), then we will plot the first component. .. GENERATED FROM PYTHON SOURCE LINES 126-145 .. code-block:: Python from sklearn.decomposition import FastICA from nilearn.image import index_img from nilearn.plotting import plot_stat_map, show ica = FastICA(n_components=10, random_state=42, tol=0.001, max_iter=2000) components_masked = ica.fit_transform(fmri_masked.T).T components = masker.inverse_transform(components_masked) 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 /home/runner/work/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. [NiftiMasker.inverse_transform] Computing image from signals ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([[0.244354, ..., 2.145976], ..., [0.500638, ..., 0.718085]], shape=(10, 24256)), ) ___________________________________________________________unmask - 0.3s, 0.0min /home/runner/work/nilearn/nilearn/examples/06_manipulating_images/plot_nifti_simple.py:144: UserWarning: You are using the 'agg' matplotlib backend that is non-interactive. No figure will be plotted when calling matplotlib.pyplot.show() or nilearn.plotting.show(). You can fix this by installing a different backend: for example via pip install PyQt6 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 22.315 seconds) **Estimated memory usage:** 526 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/0.13.1?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 `_