.. 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_mask_computation.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_mask_computation.py: Understanding NiftiMasker and mask computation ============================================== In this example, the NiftiMasker is used to automatically compute a mask. * The default strategy is based on the background. * Another option is to use a template. * For raw EPI, as in :term:`resting-state` or movie watching time series, we need to use the 'epi' strategy of the NiftiMasker. In addition, we show here how to tweak the different parameters of the underlying routine that extract masks from EPI :func:`~nilearn.masking.compute_epi_mask`. .. GENERATED FROM PYTHON SOURCE LINES 21-29 Computing a mask from the background ------------------------------------ The default strategy to compute a mask is to try to detect the background. With data that has already been masked this should work well, as it relies on a homogeneous background .. GENERATED FROM PYTHON SOURCE LINES 31-36 Fetch the dataset ^^^^^^^^^^^^^^^^^ We fetch do some basic visualization of the image we will be using. .. GENERATED FROM PYTHON SOURCE LINES 36-53 .. code-block:: Python from nilearn.datasets import fetch_miyawaki2008 from nilearn.image import mean_img from nilearn.plotting import plot_epi, show miyawaki_dataset = fetch_miyawaki2008() print( "First functional nifti image (4D) is located " f"at: {miyawaki_dataset.func[0]}" ) miyawaki_filename = miyawaki_dataset.func[0] miyawaki_mean_img = mean_img(miyawaki_filename) plot_epi(miyawaki_mean_img, title="Mean EPI image") show() .. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_mask_computation_001.png :alt: plot mask computation :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_mask_computation_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none [fetch_miyawaki2008] Dataset directory found: /home/runner/work/nilearn/nilearn/nilearn_data/miyawaki2008 First functional nifti image (4D) is located at: /home/runner/work/nilearn/nilearn/nilearn_data/miyawaki2008/func/data_figure_run01.nii.gz .. GENERATED FROM PYTHON SOURCE LINES 54-58 A NiftiMasker with the default strategy --------------------------------------- Let's use the NiftiMasker with its defaults parameters. .. GENERATED FROM PYTHON SOURCE LINES 58-62 .. code-block:: Python from nilearn.maskers import NiftiMasker masker = NiftiMasker() .. GENERATED FROM PYTHON SOURCE LINES 63-65 .. include:: ../../../examples/html_repr_note.rst .. GENERATED FROM PYTHON SOURCE LINES 66-68 .. code-block:: Python masker .. raw:: html

NiftiMasker()

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 69-71 .. code-block:: Python masker.fit(miyawaki_filename) .. raw:: html

NiftiMasker()

.. GENERATED FROM PYTHON SOURCE LINES 72-78 .. note :: You can also note that after fitting, the HTML representation of the estimator looks different than before fitting. .. GENERATED FROM PYTHON SOURCE LINES 80-93 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 ``_`` of 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 93-102 .. code-block:: Python from nilearn.plotting import plot_roi plot_roi( masker.mask_img_, miyawaki_mean_img, title="Mask from already masked data" ) # display the image show() .. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_mask_computation_002.png :alt: plot mask computation :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_mask_computation_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 103-110 View the generated mask ^^^^^^^^^^^^^^^^^^^^^^^ 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 110-112 .. code-block:: Python report = masker.generate_report(engine="brainsprite") .. rst-class:: sphx-glr-script-out .. code-block:: none /home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/numpy/core/fromnumeric.py:771: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray. /home/runner/work/nilearn/nilearn/examples/06_manipulating_images/plot_mask_computation.py:110: UserWarning: Resampling binary images with continuous or linear interpolation. This might lead to unexpected results. You might consider using nearest interpolation instead. .. GENERATED FROM PYTHON SOURCE LINES 113-115 .. include:: ../../../examples/report_note.rst .. GENERATED FROM PYTHON SOURCE LINES 116-119 .. 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.

Opacity