9.4.3. Computing a connectome with sparse inverse covariance

This example constructs a functional connectome using the sparse inverse covariance.

We use the MSDL atlas of functional regions in movie watching, and the nilearn.input_data.NiftiMapsMasker to extract time series.

Note that the inverse covariance (or precision) contains values that can be linked to negated partial correlations, so we negated it for display.

As the MSDL atlas comes with (x, y, z) MNI coordinates for the different regions, we can visualize the matrix as a graph of interaction in a brain. To avoid having too dense a graph, we represent only the 20% edges with the highest values. Retrieve the atlas and the data

from nilearn import datasets
atlas = datasets.fetch_atlas_msdl()
# Loading atlas image stored in 'maps'
atlas_filename = atlas['maps']
# Loading atlas data stored in 'labels'
labels = atlas['labels']

# Loading the functional datasets
data = datasets.fetch_development_fmri(n_subjects=1)

# print basic information on the dataset
print('First subject functional nifti images (4D) are at: %s' %
      data.func[0])  # 4D data


/home/nicolas/anaconda3/envs/nilearn/lib/python3.8/site-packages/numpy/lib/npyio.py:2405: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.
  output = genfromtxt(fname, **kwargs)
First subject functional nifti images (4D) are at: /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz Extract time series

from nilearn.input_data import NiftiMapsMasker
masker = NiftiMapsMasker(maps_img=atlas_filename, standardize=True,
                         memory='nilearn_cache', verbose=5)

time_series = masker.fit_transform(data.func[0],


[NiftiMapsMasker.fit_transform] loading regions from /home/nicolas/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii
/home/nicolas/GitRepos/nilearn-fork/nilearn/image/image.py:1106: FutureWarning: The parameter "sessions" will be removed in 0.9.0 release of Nilearn. Please use the parameter "runs" instead.
  data = signal.clean(
Resampling maps
/home/nicolas/GitRepos/nilearn-fork/nilearn/_utils/cache_mixin.py:303: UserWarning: memory_level is currently set to 0 but a Memory object has been provided. Setting memory_level to 1.
  warnings.warn("memory_level is currently set to 0 but "
[Memory]0.0s, 0.0min    : Loading resample_img...
________________________________________resample_img cache loaded - 0.0s, 0.0min
[Memory]0.1s, 0.0min    : Loading filter_and_extract...
__________________________________filter_and_extract cache loaded - 0.0s, 0.0min Compute the sparse inverse covariance

    from sklearn.covariance import GraphicalLassoCV
except ImportError:
    # for Scitkit-Learn < v0.20.0
    from sklearn.covariance import GraphLassoCV as GraphicalLassoCV

estimator = GraphicalLassoCV()


GraphicalLassoCV() Display the connectome matrix

from nilearn import plotting
# Display the covariance

# The covariance can be found at estimator.covariance_
plotting.plot_matrix(estimator.covariance_, labels=labels,
                     figure=(9, 7), vmax=1, vmin=-1,
plot inverse covariance connectome


<matplotlib.image.AxesImage object at 0x7fc024932ac0> And now display the corresponding graph

plot inverse covariance connectome


<nilearn.plotting.displays.OrthoProjector object at 0x7fc03686ff40> Display the sparse inverse covariance

we negate it to get partial correlations

plotting.plot_matrix(-estimator.precision_, labels=labels,
                     figure=(9, 7), vmax=1, vmin=-1,
                     title='Sparse inverse covariance')
plot inverse covariance connectome


<matplotlib.image.AxesImage object at 0x7fc022346ca0> And now display the corresponding graph

plotting.plot_connectome(-estimator.precision_, coords,
                         title='Sparse inverse covariance')

plot inverse covariance connectome 3D visualization in a web browser

An alternative to nilearn.plotting.plot_connectome is to use nilearn.plotting.view_connectome that gives more interactive visualizations in a web browser. See 3D Plots of connectomes for more details.

view = plotting.view_connectome(-estimator.precision_, coords)

# In a Jupyter notebook, if ``view`` is the output of a cell, it will
# be displayed below the cell