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 |
| low_pass | None |
| mask_args | None |
| mask_img | None |
| mask_strategy | epi |
| memory | Memory(location=nilearn_cache/joblib) |
| memory_level | 2 |
| reports | True |
| sample_mask | None |
| sessions | None |
| smoothing_fwhm | 8 |
| standardize | 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) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none /home/varoquau/dev/nilearn/nilearn/input_data/base_masker.py:97: UserWarning: Persisting input arguments took 0.76s to run. If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function (e.g. large strings). THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an example so that they can fix the problem. region_signals, aux = cache(extraction_function, memory, 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/varoquau/dev/scikit-learn/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_003.png :alt: plot nifti simple :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 16.454 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:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/master?filepath=examples/auto_examples/06_manipulating_images/plot_nifti_simple.ipynb :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_nifti_simple.py