.. _example_connectivity_plot_inverse_covariance_connectome.py:


Computing a connectome with sparse inverse covariance
=======================================================

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

We use the `MSDL atlas
<https://team.inria.fr/parietal/research/spatial_patterns/spatial-patterns-in-resting-state/>`_
of functional regions in rest, and the
:class:`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.




.. rst-class:: sphx-glr-horizontal





**Python source code:** :download:`plot_inverse_covariance_connectome.py <plot_inverse_covariance_connectome.py>`

.. literalinclude:: plot_inverse_covariance_connectome.py
    :lines: 23-

**Total running time of the example:**  0.00 seconds
( 0 minutes  0.00 seconds)