.. 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 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_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. .. include:: ../../../examples/masker_note.rst .. GENERATED FROM PYTHON SOURCE LINES 11-38 .. code-block:: default 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, cmap=plotting.cm.bwr, vmin=-1, vmax=1, title="%s / covariance" % title, labels=labels) # Display precision matrix plotting.plot_matrix(prec, cmap=plotting.cm.bwr, vmin=-span, vmax=span, title="%s / precision" % title, labels=labels) .. GENERATED FROM PYTHON SOURCE LINES 39-41 Fetching datasets ------------------ .. GENERATED FROM PYTHON SOURCE LINES 41-50 .. code-block:: default from nilearn import datasets msdl_atlas_dataset = datasets.fetch_atlas_msdl() rest_dataset = datasets.fetch_development_fmri(n_subjects=n_subjects) # print basic information on the dataset print('First subject functional nifti image (4D) is at: %s' % rest_dataset.func[0]) # 4D data .. rst-class:: sphx-glr-script-out Out: .. code-block:: none First subject 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 .. GENERATED FROM PYTHON SOURCE LINES 51-53 Extracting region signals -------------------------- .. GENERATED FROM PYTHON SOURCE LINES 53-78 .. code-block:: default from nilearn.maskers import NiftiMapsMasker # A "memory" to avoid recomputation from joblib import Memory mem = Memory('nilearn_cache') masker = NiftiMapsMasker( msdl_atlas_dataset.maps, resampling_target="maps", detrend=True, high_variance_confounds=True, low_pass=None, high_pass=0.01, t_r=2, standardize=True, memory='nilearn_cache', memory_level=1, verbose=2) masker.fit() subject_time_series = [] func_filenames = rest_dataset.func confound_filenames = rest_dataset.confounds for func_filename, confound_filename in zip(func_filenames, confound_filenames): print("Processing file %s" % func_filename) region_ts = masker.transform(func_filename, confounds=confound_filename) subject_time_series.append(region_ts) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none [NiftiMapsMasker.fit] loading regions from /home/nicolas/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii Processing file /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz ________________________________________________________________________________ [Memory] Calling nilearn.image.image.high_variance_confounds... high_variance_confounds('/home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz') __________________________________________high_variance_confounds - 0.7s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... _filter_and_extract('/home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', , { 'allow_overlap': True, 'detrend': True, 'dtype': None, 'high_pass': 0.01, 'high_variance_confounds': True, 'low_pass': None, 'maps_img': '/home/nicolas/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii', 'mask_img': None, 'reports': True, 'smoothing_fwhm': None, 'standardize': True, '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/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=2) [NiftiMapsMasker.transform_single_imgs] Loading data from /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz [NiftiMapsMasker.transform_single_imgs] Resampling images [NiftiMapsMasker.transform_single_imgs] Extracting region signals [NiftiMapsMasker.transform_single_imgs] Cleaning extracted signals _______________________________________________filter_and_extract - 5.2s, 0.1min Processing file /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar001_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz ________________________________________________________________________________ [Memory] Calling nilearn.image.image.high_variance_confounds... high_variance_confounds('/home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar001_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz') __________________________________________high_variance_confounds - 0.7s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... _filter_and_extract('/home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar001_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', , { 'allow_overlap': True, 'detrend': True, 'dtype': None, 'high_pass': 0.01, 'high_variance_confounds': True, 'low_pass': None, 'maps_img': '/home/nicolas/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii', 'mask_img': None, 'reports': True, 'smoothing_fwhm': None, 'standardize': True, '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/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar001_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=2) [NiftiMapsMasker.transform_single_imgs] Loading data from /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar001_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz [NiftiMapsMasker.transform_single_imgs] Resampling images [NiftiMapsMasker.transform_single_imgs] Extracting region signals [NiftiMapsMasker.transform_single_imgs] Cleaning extracted signals _______________________________________________filter_and_extract - 5.3s, 0.1min Processing file /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar002_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz ________________________________________________________________________________ [Memory] Calling nilearn.image.image.high_variance_confounds... high_variance_confounds('/home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar002_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz') __________________________________________high_variance_confounds - 0.7s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... _filter_and_extract('/home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar002_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', , { 'allow_overlap': True, 'detrend': True, 'dtype': None, 'high_pass': 0.01, 'high_variance_confounds': True, 'low_pass': None, 'maps_img': '/home/nicolas/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii', 'mask_img': None, 'reports': True, 'smoothing_fwhm': None, 'standardize': True, '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/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar002_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=2) [NiftiMapsMasker.transform_single_imgs] Loading data from /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar002_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz [NiftiMapsMasker.transform_single_imgs] Resampling images [NiftiMapsMasker.transform_single_imgs] Extracting region signals [NiftiMapsMasker.transform_single_imgs] Cleaning extracted signals _______________________________________________filter_and_extract - 5.3s, 0.1min Processing file /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar003_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz ________________________________________________________________________________ [Memory] Calling nilearn.image.image.high_variance_confounds... high_variance_confounds('/home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar003_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz') __________________________________________high_variance_confounds - 0.7s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.base_masker._filter_and_extract... _filter_and_extract('/home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar003_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', , { 'allow_overlap': True, 'detrend': True, 'dtype': None, 'high_pass': 0.01, 'high_variance_confounds': True, 'low_pass': None, 'maps_img': '/home/nicolas/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii', 'mask_img': None, 'reports': True, 'smoothing_fwhm': None, 'standardize': True, '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/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar003_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=2) [NiftiMapsMasker.transform_single_imgs] Loading data from /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar003_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz [NiftiMapsMasker.transform_single_imgs] Resampling images [NiftiMapsMasker.transform_single_imgs] Extracting region signals [NiftiMapsMasker.transform_single_imgs] Cleaning extracted signals _______________________________________________filter_and_extract - 5.3s, 0.1min .. GENERATED FROM PYTHON SOURCE LINES 79-81 Computing group-sparse precision matrices ------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 81-95 .. code-block:: default from nilearn.connectome import GroupSparseCovarianceCV gsc = GroupSparseCovarianceCV(verbose=2) gsc.fit(subject_time_series) try: from sklearn.covariance import GraphicalLassoCV except ImportError: # for Scitkit-Learn < v0.20.0 from sklearn.covariance import GraphLassoCV as GraphicalLassoCV gl = GraphicalLassoCV(verbose=2) gl.fit(np.concatenate(subject_time_series)) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 2 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 7 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 0 [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 2.3s remaining: 0.0s [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 2 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 0 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 2 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 0 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 1 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 0 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 2 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 6 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 0 [Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 11.9s finished [GroupSparseCovarianceCV.fit] [GroupSparseCovarianceCV] Done refinement 1 out of 4 [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 3 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 6 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 1 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 0 [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 2.6s remaining: 0.0s [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 4 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 0 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 3 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 0 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 0 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 3 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 6 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 0 [Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 21.3s finished [GroupSparseCovarianceCV.fit] [GroupSparseCovarianceCV] Done refinement 2 out of 4 [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 5 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 1 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 1 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 1 [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 2.5s remaining: 0.0s [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 9 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 10 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 0 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 5 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 0 [Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 24.2s finished [GroupSparseCovarianceCV.fit] [GroupSparseCovarianceCV] Done refinement 3 out of 4 [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 6 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 1 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 1 [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 3.2s remaining: 0.0s [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 10 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 11 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 5 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 0 [GroupSparseCovarianceCV.fit] Log-likelihood on test set is decreasing. Stopping at iteration 0 [Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 19.8s finished [GroupSparseCovarianceCV.fit] [GroupSparseCovarianceCV] Done refinement 4 out of 4 [GroupSparseCovarianceCV.fit] Final optimization [GroupSparseCovarianceCV.fit] tolerance reached at iteration number 19: 8.789e-04 [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. ....[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s remaining: 0.0s ................[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.0s finished [GraphicalLassoCV] Done refinement 1 out of 4: 1s [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. ....[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s ................[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.8s finished [GraphicalLassoCV] Done refinement 2 out of 4: 1s [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. ....[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.4s remaining: 0.0s ................[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.2s finished [GraphicalLassoCV] Done refinement 3 out of 4: 3s [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. ....[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s ................[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.8s finished [GraphicalLassoCV] Done refinement 4 out of 4: 4s [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.0s finished [graphical_lasso] Iteration 0, cost 1.68e+02, dual gap 1.123e+00 [graphical_lasso] Iteration 1, cost 1.68e+02, dual gap -1.664e-03 [graphical_lasso] Iteration 2, cost 1.68e+02, dual gap 1.158e-04 [graphical_lasso] Iteration 3, cost 1.68e+02, dual gap 1.389e-04 [graphical_lasso] Iteration 4, cost 1.68e+02, dual gap 1.530e-04 [graphical_lasso] Iteration 5, cost 1.68e+02, dual gap 1.318e-04 [graphical_lasso] Iteration 6, cost 1.68e+02, dual gap 6.844e-05 GraphicalLassoCV(verbose=2) .. GENERATED FROM PYTHON SOURCE LINES 96-98 Displaying results ------------------- .. GENERATED FROM PYTHON SOURCE LINES 98-123 .. code-block:: default 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=.5, edge_vmin=-.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=.5, edge_vmin=-.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: plot multi subject connectome :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: plot multi subject connectome :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: plot multi subject connectome :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: plot multi subject connectome :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 59.869 seconds) **Estimated memory usage:** 423 MB .. _sphx_glr_download_auto_examples_03_connectivity_plot_multi_subject_connectome.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/03_connectivity/plot_multi_subject_connectome.ipynb :alt: Launch binder :width: 150 px .. 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-jupyter :download:`Download Jupyter notebook: plot_multi_subject_connectome.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_