.. _example_connectivity_plot_signal_extraction.py:


Extracting signals from a brain parcellation
============================================

Here we show how to extract signals from a brain parcellation and compute
a correlation matrix.

We also show the importance of defining good confounds signals: the
first correlation matrix is computed after regressing out simple
confounds signals: movement regressors, white matter and CSF signals, ...
The second one is without any confounds: all regions are connected to
each other.


One reference that discusses the importance of confounds is `Varoquaux and
Craddock, Learning and comparing functional connectomes across subjects,
NeuroImage 2013
<http://www.sciencedirect.com/science/article/pii/S1053811913003340>`_.

This is just a code example, see the :ref:`corresponding section in the
documentation <parcellation_time_series>` for more.



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





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

.. literalinclude:: plot_signal_extraction.py
    :lines: 23-

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