Note
Go to the end to download the full example code. or to run this example in your browser via Binder
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.
Note
If you are using Nilearn with a version older than 0.9.0, then you should either upgrade your version or import maskers from the input_data module instead of the maskers module.
That is, you should manually replace in the following example all occurrences of:
from nilearn.maskers import NiftiMasker
with:
from nilearn.input_data import NiftiMasker
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=f"{title} / covariance",
labels=labels,
)
# Display precision matrix
plotting.plot_matrix(
prec,
cmap=plotting.cm.bwr,
vmin=-span,
vmax=span,
title=f"{title} / precision",
labels=labels,
)
Fetching datasets¶
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(
f"First subject functional nifti image (4D) is at: {rest_dataset.func[0]}"
)
[get_dataset_dir] Dataset found in /home/remi/nilearn_data/msdl_atlas
[get_dataset_dir] Dataset found in /home/remi/nilearn_data/development_fmri
[get_dataset_dir] Dataset found in /home/remi/nilearn_data/development_fmri/development_fmri
[get_dataset_dir] Dataset found in /home/remi/nilearn_data/development_fmri/development_fmri
First subject functional nifti image (4D) is at: /home/remi/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
Extracting region signals¶
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=2,
standardize="zscore_sample",
standardize_confounds="zscore_sample",
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(f"Processing file {func_filename}")
region_ts = masker.transform(func_filename, confounds=confound_filename)
subject_time_series.append(region_ts)
[NiftiMapsMasker.fit] loading regions from None
Processing file /home/remi/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/remi/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz')
__________________________________________high_variance_confounds - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker._filter_and_extract...
_filter_and_extract('/home/remi/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x757c26decfd0>, { 'allow_overlap': True,
'clean_kwargs': {},
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': True,
'keep_masked_maps': True,
'low_pass': None,
'maps_img': '/home/remi/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': 'zscore_sample',
'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/remi/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.wrapped] Loading data from
/home/remi/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
[NiftiMapsMasker.wrapped] Resampling images
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
_______________________________________________filter_and_extract - 7.8s, 0.1min
Processing file /home/remi/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/remi/nilearn_data/development_fmri/development_fmri/sub-pixar001_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz')
__________________________________________high_variance_confounds - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker._filter_and_extract...
_filter_and_extract('/home/remi/nilearn_data/development_fmri/development_fmri/sub-pixar001_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x757c5c8910a0>, { 'allow_overlap': True,
'clean_kwargs': {},
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': True,
'keep_masked_maps': True,
'low_pass': None,
'maps_img': '/home/remi/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': 'zscore_sample',
'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/remi/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.wrapped] Loading data from
/home/remi/nilearn_data/development_fmri/development_fmri/sub-pixar001_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
[NiftiMapsMasker.wrapped] Resampling images
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
_______________________________________________filter_and_extract - 8.4s, 0.1min
Processing file /home/remi/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/remi/nilearn_data/development_fmri/development_fmri/sub-pixar002_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz')
__________________________________________high_variance_confounds - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker._filter_and_extract...
_filter_and_extract('/home/remi/nilearn_data/development_fmri/development_fmri/sub-pixar002_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x757c26decfd0>, { 'allow_overlap': True,
'clean_kwargs': {},
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': True,
'keep_masked_maps': True,
'low_pass': None,
'maps_img': '/home/remi/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': 'zscore_sample',
'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/remi/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.wrapped] Loading data from
/home/remi/nilearn_data/development_fmri/development_fmri/sub-pixar002_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
[NiftiMapsMasker.wrapped] Resampling images
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
_______________________________________________filter_and_extract - 7.8s, 0.1min
Processing file /home/remi/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/remi/nilearn_data/development_fmri/development_fmri/sub-pixar003_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz')
__________________________________________high_variance_confounds - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker._filter_and_extract...
_filter_and_extract('/home/remi/nilearn_data/development_fmri/development_fmri/sub-pixar003_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x757c5c8910a0>, { 'allow_overlap': True,
'clean_kwargs': {},
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': True,
'keep_masked_maps': True,
'low_pass': None,
'maps_img': '/home/remi/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': 'zscore_sample',
'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/remi/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.wrapped] Loading data from
/home/remi/nilearn_data/development_fmri/development_fmri/sub-pixar003_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
[NiftiMapsMasker.wrapped] Resampling images
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
_______________________________________________filter_and_extract - 7.7s, 0.1min
Computing group-sparse precision matrices¶
from nilearn.connectome import GroupSparseCovarianceCV
gsc = GroupSparseCovarianceCV(verbose=2)
gsc.fit(subject_time_series)
from sklearn.covariance import GraphicalLassoCV
gl = GraphicalLassoCV(verbose=2)
gl.fit(np.concatenate(subject_time_series))
[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: 2.0s 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 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: 10.4s finished
[GroupSparseCovarianceCV.fit] [GroupSparseCovarianceCV] Done refinement 0 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: 2.1s 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: 17.5s 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 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
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 2.7s 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: 19.8s 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 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: 2.4s remaining: 0.0s
[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 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: 15.1s finished
[GroupSparseCovarianceCV.fit] [GroupSparseCovarianceCV] Done refinement 3 out of 4
[GroupSparseCovarianceCV.fit] Final optimization
[GroupSparseCovarianceCV.fit] * iteration 0 (0 %) ...
[GroupSparseCovarianceCV.fit] * iteration 1 (1 %) variation (max norm): 2.881e+00 ...
[GroupSparseCovarianceCV.fit] * iteration 2 (2 %) variation (max norm): 1.308e+00 ...
[GroupSparseCovarianceCV.fit] * iteration 3 (3 %) variation (max norm): 4.648e-01 ...
[GroupSparseCovarianceCV.fit] * iteration 4 (4 %) variation (max norm): 2.228e-01 ...
[GroupSparseCovarianceCV.fit] * iteration 5 (5 %) variation (max norm): 1.772e-01 ...
[GroupSparseCovarianceCV.fit] * iteration 6 (6 %) variation (max norm): 9.645e-02 ...
[GroupSparseCovarianceCV.fit] * iteration 7 (7 %) variation (max norm): 3.293e-02 ...
[GroupSparseCovarianceCV.fit] * iteration 8 (8 %) variation (max norm): 2.297e-02 ...
[GroupSparseCovarianceCV.fit] * iteration 9 (9 %) variation (max norm): 2.263e-02 ...
[GroupSparseCovarianceCV.fit] * iteration 10 (10 %) variation (max norm): 2.251e-02 ...
[GroupSparseCovarianceCV.fit] * iteration 11 (11 %) variation (max norm): 1.646e-02 ...
[GroupSparseCovarianceCV.fit] * iteration 12 (12 %) variation (max norm): 9.507e-03 ...
[GroupSparseCovarianceCV.fit] * iteration 13 (13 %) variation (max norm): 3.895e-03 ...
[GroupSparseCovarianceCV.fit] * iteration 14 (14 %) variation (max norm): 2.817e-03 ...
[GroupSparseCovarianceCV.fit] * iteration 15 (15 %) variation (max norm): 2.163e-03 ...
[GroupSparseCovarianceCV.fit] * iteration 16 (16 %) variation (max norm): 2.347e-03 ...
[GroupSparseCovarianceCV.fit] * iteration 17 (17 %) variation (max norm): 2.141e-03 ...
[GroupSparseCovarianceCV.fit] * iteration 18 (18 %) variation (max norm): 1.548e-03 ...
[GroupSparseCovarianceCV.fit] tolerance reached at iteration number 19: 8.841e-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: 0.6s 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 1 out of 1 | elapsed: 0.1s remaining: 0.0s
................[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.6s 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: 0.2s remaining: 0.0s
................[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.7s 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 1 out of 1 | elapsed: 0.1s remaining: 0.0s
................[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.6s finished
[GraphicalLassoCV] Done refinement 4 out of 4: 2s
[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
Displaying results¶
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()
Total running time of the script: (1 minutes 50.630 seconds)
Estimated memory usage: 639 MB
