.. _example_connectivity_plot_canica_resting_state.py:


Group analysis of resting-state fMRI with ICA: CanICA
=====================================================

An example applying CanICA to resting-state data. This example applies it
to 40 subjects of the ADHD200 datasets.

CanICA is an ICA method for group-level analysis of fMRI data. Compared
to other strategies, it brings a well-controlled group model, as well as a
thresholding algorithm controlling for specificity and sensitivity with
an explicit model of the signal. The reference papers are:

    * G. Varoquaux et al. "A group model for stable multi-subject ICA on
      fMRI datasets", NeuroImage Vol 51 (2010), p. 288-299

    * G. Varoquaux et al. "ICA-based sparse features recovery from fMRI
      datasets", IEEE ISBI 2010, p. 1177

Pre-prints for both papers are available on hal
(http://hal.archives-ouvertes.fr)



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





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

.. literalinclude:: plot_canica_resting_state.py
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**Total running time of the example:**  0.00 seconds
( 0 minutes  0.00 seconds)