.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/03_connectivity/plot_multi_subject_connectome.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_03_connectivity_plot_multi_subject_connectome.py: Group Sparse inverse covariance for multi-subject connectome ============================================================ This example shows how to estimate a connectome on a group of subjects using the group sparse inverse covariance estimate. .. GENERATED FROM PYTHON SOURCE LINES 11-45 .. code-block:: Python import numpy as np from nilearn import plotting n_subjects = 4 # subjects to consider for group-sparse covariance (max: 40) def plot_matrices(cov, prec, title, labels): """Plot covariance and precision matrices, for a given processing.""" prec = prec.copy() # avoid side effects # Put zeros on the diagonal, for graph clarity. size = prec.shape[0] prec[list(range(size)), list(range(size))] = 0 span = max(abs(prec.min()), abs(prec.max())) # Display covariance matrix plotting.plot_matrix( cov, vmin=-1, vmax=1, title=f"{title} / covariance", labels=labels, ) # Display precision matrix plotting.plot_matrix( prec, vmin=-span, vmax=span, title=f"{title} / precision", labels=labels, ) .. GENERATED FROM PYTHON SOURCE LINES 46-48 Fetching datasets ------------------ .. GENERATED FROM PYTHON SOURCE LINES 48-59 .. code-block:: Python from nilearn.datasets import fetch_atlas_msdl, fetch_development_fmri msdl_atlas_dataset = fetch_atlas_msdl() rest_dataset = fetch_development_fmri(n_subjects=n_subjects) # print basic information on the dataset print( f"First subject functional nifti image (4D) is at: {rest_dataset.func[0]}" ) .. rst-class:: sphx-glr-script-out .. code-block:: none [fetch_atlas_msdl] Dataset directory found: /home/runner/work/nilearn/nilearn/nilearn_data/msdl_atlas [fetch_development_fmri] Dataset directory found: /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri First subject functional nifti image (4D) is at: /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz .. GENERATED FROM PYTHON SOURCE LINES 60-62 Extracting region signals ------------------------- .. GENERATED FROM PYTHON SOURCE LINES 62-93 .. code-block:: Python from nilearn.maskers import NiftiMapsMasker masker = NiftiMapsMasker( msdl_atlas_dataset.maps, resampling_target="maps", detrend=True, high_variance_confounds=True, low_pass=None, high_pass=0.01, t_r=rest_dataset.t_r, standardize="zscore_sample", standardize_confounds=True, memory="nilearn_cache", memory_level=1, verbose=1, ) subject_time_series = [] func_filenames = rest_dataset.func confound_filenames = rest_dataset.confounds for func_filename, confound_filename in zip( func_filenames, confound_filenames, strict=False ): print(f"Processing file {func_filename}") region_ts = masker.fit_transform( func_filename, confounds=confound_filename ) subject_time_series.append(region_ts) .. rst-class:: sphx-glr-script-out .. code-block:: none Processing file /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz [NiftiMapsMasker.wrapped] Loading data from sub-pixar123_task-... [NiftiMapsMasker.wrapped] Loading regions from .../msdl_rois.nii [NiftiMapsMasker.wrapped] Finished fit ________________________________________________________________________________ [Memory] Calling nilearn.image.image.high_variance_confounds... high_variance_confounds('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz') __________________________________________high_variance_confounds - 0.5s, 0.0min /home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_multi_subject_connectome.py:87: UserWarning: Resampling images at transform time... To avoid this warning, make sure to resample the images you want to transform to the shape of the maps or set resampling_target to 'data'. ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker.filter_and_extract... filter_and_extract('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', , { 'allow_overlap': True, 'clean_args': None, 'clean_kwargs': {}, 'cmap': 'CMRmap_r', 'detrend': True, 'dtype': None, 'high_pass': 0.01, 'high_variance_confounds': True, 'keep_masked_maps': False, 'low_pass': None, 'maps_img': '/home/runner/work/nilearn/nilearn/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii', 'mask_img': None, 'reports': True, 'smoothing_fwhm': None, 'standardize': 'zscore_sample', 'standardize_confounds': True, 't_r': 2, 'target_affine': array([[ 4., 0., 0., -78.], [ 0., 4., 0., -111.], [ 0., 0., 4., -51.], [ 0., 0., 0., 1.]]), 'target_shape': (40, 48, 35)}, confounds=[ array([[-0.174325, ..., -0.048779], ..., [-0.044073, ..., 0.155444]]), '/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar123_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None) [NiftiMapsMasker.wrapped] Loading data from sub-pixar123_task-... [NiftiMapsMasker.wrapped] Resampling images [NiftiMapsMasker.wrapped] Extracting region signals [NiftiMapsMasker.wrapped] Cleaning extracted signals _______________________________________________filter_and_extract - 4.2s, 0.1min Processing file /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar001_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz [NiftiMapsMasker.wrapped] Loading data from sub-pixar001_task-... [NiftiMapsMasker.wrapped] Loading regions from .../msdl_rois.nii [NiftiMapsMasker.wrapped] Finished fit ________________________________________________________________________________ [Memory] Calling nilearn.image.image.high_variance_confounds... high_variance_confounds('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar001_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz') __________________________________________high_variance_confounds - 0.4s, 0.0min /home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_multi_subject_connectome.py:87: UserWarning: Resampling images at transform time... To avoid this warning, make sure to resample the images you want to transform to the shape of the maps or set resampling_target to 'data'. ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker.filter_and_extract... filter_and_extract('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar001_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', , { 'allow_overlap': True, 'clean_args': None, 'clean_kwargs': {}, 'cmap': 'CMRmap_r', 'detrend': True, 'dtype': None, 'high_pass': 0.01, 'high_variance_confounds': True, 'keep_masked_maps': False, 'low_pass': None, 'maps_img': '/home/runner/work/nilearn/nilearn/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii', 'mask_img': None, 'reports': True, 'smoothing_fwhm': None, 'standardize': 'zscore_sample', 'standardize_confounds': True, 't_r': 2, 'target_affine': array([[ 4., 0., 0., -78.], [ 0., 4., 0., -111.], [ 0., 0., 4., -51.], [ 0., 0., 0., 1.]]), 'target_shape': (40, 48, 35)}, confounds=[ array([[-0.151677, ..., -0.057023], ..., [-0.206928, ..., 0.102714]]), '/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar001_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None) [NiftiMapsMasker.wrapped] Loading data from sub-pixar001_task-... [NiftiMapsMasker.wrapped] Resampling images [NiftiMapsMasker.wrapped] Extracting region signals [NiftiMapsMasker.wrapped] Cleaning extracted signals _______________________________________________filter_and_extract - 4.2s, 0.1min Processing file /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar002_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz [NiftiMapsMasker.wrapped] Loading data from sub-pixar002_task-... [NiftiMapsMasker.wrapped] Loading regions from .../msdl_rois.nii [NiftiMapsMasker.wrapped] Finished fit ________________________________________________________________________________ [Memory] Calling nilearn.image.image.high_variance_confounds... high_variance_confounds('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar002_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz') __________________________________________high_variance_confounds - 0.4s, 0.0min /home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_multi_subject_connectome.py:87: UserWarning: Resampling images at transform time... To avoid this warning, make sure to resample the images you want to transform to the shape of the maps or set resampling_target to 'data'. ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker.filter_and_extract... filter_and_extract('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar002_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', , { 'allow_overlap': True, 'clean_args': None, 'clean_kwargs': {}, 'cmap': 'CMRmap_r', 'detrend': True, 'dtype': None, 'high_pass': 0.01, 'high_variance_confounds': True, 'keep_masked_maps': False, 'low_pass': None, 'maps_img': '/home/runner/work/nilearn/nilearn/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii', 'mask_img': None, 'reports': True, 'smoothing_fwhm': None, 'standardize': 'zscore_sample', 'standardize_confounds': True, 't_r': 2, 'target_affine': array([[ 4., 0., 0., -78.], [ 0., 4., 0., -111.], [ 0., 0., 4., -51.], [ 0., 0., 0., 1.]]), 'target_shape': (40, 48, 35)}, confounds=[ array([[ 0.127944, ..., -0.087084], ..., [-0.015679, ..., -0.02587 ]]), '/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar002_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None) [NiftiMapsMasker.wrapped] Loading data from sub-pixar002_task-... [NiftiMapsMasker.wrapped] Resampling images [NiftiMapsMasker.wrapped] Extracting region signals [NiftiMapsMasker.wrapped] Cleaning extracted signals _______________________________________________filter_and_extract - 4.2s, 0.1min Processing file /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar003_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz [NiftiMapsMasker.wrapped] Loading data from sub-pixar003_task-... [NiftiMapsMasker.wrapped] Loading regions from .../msdl_rois.nii [NiftiMapsMasker.wrapped] Finished fit ________________________________________________________________________________ [Memory] Calling nilearn.image.image.high_variance_confounds... high_variance_confounds('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar003_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz') __________________________________________high_variance_confounds - 0.4s, 0.0min /home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_multi_subject_connectome.py:87: UserWarning: Resampling images at transform time... To avoid this warning, make sure to resample the images you want to transform to the shape of the maps or set resampling_target to 'data'. ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker.filter_and_extract... filter_and_extract('/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar003_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', , { 'allow_overlap': True, 'clean_args': None, 'clean_kwargs': {}, 'cmap': 'CMRmap_r', 'detrend': True, 'dtype': None, 'high_pass': 0.01, 'high_variance_confounds': True, 'keep_masked_maps': False, 'low_pass': None, 'maps_img': '/home/runner/work/nilearn/nilearn/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii', 'mask_img': None, 'reports': True, 'smoothing_fwhm': None, 'standardize': 'zscore_sample', 'standardize_confounds': True, 't_r': 2, 'target_affine': array([[ 4., 0., 0., -78.], [ 0., 4., 0., -111.], [ 0., 0., 4., -51.], [ 0., 0., 0., 1.]]), 'target_shape': (40, 48, 35)}, confounds=[ array([[-0.089762, ..., -0.062316], ..., [-0.065223, ..., -0.022868]]), '/home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/sub-pixar003_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None) [NiftiMapsMasker.wrapped] Loading data from sub-pixar003_task-... [NiftiMapsMasker.wrapped] Resampling images [NiftiMapsMasker.wrapped] Extracting region signals [NiftiMapsMasker.wrapped] Cleaning extracted signals _______________________________________________filter_and_extract - 4.2s, 0.1min .. GENERATED FROM PYTHON SOURCE LINES 94-96 Computing group-sparse precision matrices ----------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 96-108 .. code-block:: Python from nilearn.connectome import GroupSparseCovarianceCV gsc = GroupSparseCovarianceCV(verbose=1) gsc.fit(subject_time_series) from sklearn.covariance import GraphicalLassoCV gl = GraphicalLassoCV(verbose=True) gl.fit(np.concatenate(subject_time_series)) .. rst-class:: sphx-glr-script-out .. code-block:: none [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 7.6s finished [GroupSparseCovarianceCV.fit] [GroupSparseCovarianceCV] Done refinement 0 out of 4 [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 13.0s finished [GroupSparseCovarianceCV.fit] [GroupSparseCovarianceCV] Done refinement 1 out of 4 [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 15.3s finished [GroupSparseCovarianceCV.fit] [GroupSparseCovarianceCV] Done refinement 2 out of 4 [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 12.2s finished [GroupSparseCovarianceCV.fit] [GroupSparseCovarianceCV] Done refinement 3 out of 4 [GroupSparseCovarianceCV.fit] Final optimization [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.4s finished [GraphicalLassoCV] Done refinement 1 out of 4: 0s [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.5s finished [GraphicalLassoCV] Done refinement 2 out of 4: 0s [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.6s finished [GraphicalLassoCV] Done refinement 3 out of 4: 1s [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.6s finished [GraphicalLassoCV] Done refinement 4 out of 4: 2s .. raw:: html 
GraphicalLassoCV(verbose=True)
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 109-111 Displaying results ------------------ .. GENERATED FROM PYTHON SOURCE LINES 111-146 .. code-block:: Python atlas_img = msdl_atlas_dataset.maps atlas_region_coords = plotting.find_probabilistic_atlas_cut_coords(atlas_img) labels = msdl_atlas_dataset.labels plotting.plot_connectome( gl.covariance_, atlas_region_coords, edge_threshold="90%", title="Covariance", display_mode="lzr", ) plotting.plot_connectome( -gl.precision_, atlas_region_coords, edge_threshold="90%", title="Sparse inverse covariance (GraphicalLasso)", display_mode="lzr", edge_vmax=0.5, edge_vmin=-0.5, ) plot_matrices(gl.covariance_, gl.precision_, "GraphicalLasso", labels) title = "GroupSparseCovariance" plotting.plot_connectome( -gsc.precisions_[..., 0], atlas_region_coords, edge_threshold="90%", title=title, display_mode="lzr", edge_vmax=0.5, edge_vmin=-0.5, ) plot_matrices(gsc.covariances_[..., 0], gsc.precisions_[..., 0], title, labels) plotting.show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/03_connectivity/images/sphx_glr_plot_multi_subject_connectome_001.png :alt: plot multi subject connectome :srcset: /auto_examples/03_connectivity/images/sphx_glr_plot_multi_subject_connectome_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/03_connectivity/images/sphx_glr_plot_multi_subject_connectome_002.png :alt: plot multi subject connectome :srcset: /auto_examples/03_connectivity/images/sphx_glr_plot_multi_subject_connectome_002.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/03_connectivity/images/sphx_glr_plot_multi_subject_connectome_003.png :alt: GraphicalLasso / covariance :srcset: /auto_examples/03_connectivity/images/sphx_glr_plot_multi_subject_connectome_003.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/03_connectivity/images/sphx_glr_plot_multi_subject_connectome_004.png :alt: GraphicalLasso / precision :srcset: /auto_examples/03_connectivity/images/sphx_glr_plot_multi_subject_connectome_004.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/03_connectivity/images/sphx_glr_plot_multi_subject_connectome_005.png :alt: plot multi subject connectome :srcset: /auto_examples/03_connectivity/images/sphx_glr_plot_multi_subject_connectome_005.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/03_connectivity/images/sphx_glr_plot_multi_subject_connectome_006.png :alt: GroupSparseCovariance / covariance :srcset: /auto_examples/03_connectivity/images/sphx_glr_plot_multi_subject_connectome_006.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/03_connectivity/images/sphx_glr_plot_multi_subject_connectome_007.png :alt: GroupSparseCovariance / precision :srcset: /auto_examples/03_connectivity/images/sphx_glr_plot_multi_subject_connectome_007.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (1 minutes 17.521 seconds) **Estimated memory usage:** 656 MB .. _sphx_glr_download_auto_examples_03_connectivity_plot_multi_subject_connectome.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/0.14.1?urlpath=lab/tree/notebooks/auto_examples/03_connectivity/plot_multi_subject_connectome.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_multi_subject_connectome.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_multi_subject_connectome.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_multi_subject_connectome.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_