.. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` 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. Retrieve the brain development functional dataset .. code-block:: default 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) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none 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 .. code-block:: default 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 .. code-block:: default 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") .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_nifti_simple_001.png :alt: plot nifti simple :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none 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. .. code-block:: default 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 voxels is necessary. Use case: working with time series of resting-state or task maps.

image

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 .. code-block:: default 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 .. code-block:: default 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 .. rst-class:: sphx-glr-script-out Out: .. code-block:: none /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 .. code-block:: default 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() .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_nifti_simple_002.png :alt: plot nifti simple :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 15.046 seconds) .. _sphx_glr_download_auto_examples_06_manipulating_images_plot_nifti_simple.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/main?filepath=examples/auto_examples/06_manipulating_images/plot_nifti_simple.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_nifti_simple.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_nifti_simple.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_