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CanICA: Canonical ICA for fMRI data

Do not use this code: it is outdated and no longer maintained. Use the code in nilearn http://nilearn.github.io/

What is CanICA?

CanICA is an ICA package for group-level analysis of fMRI data. Compared to other packages, 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)

How CanICA compare to Melodic or Gift?

As of Spring 2010, CanICA is more reproducible and offers better statistical control (see references).

On the other hand, it lacks a nice user interface. In addition, it does not offer a way to estimate back-projected subject-level components, similar to dual-regression. For someone with a mathematical understanding of dual-regression, it should be easy to apply dual-regression on the output maps of CanICA.

Using CanICA

Currently, CanICA is used through it's Python programming interface. Get started with the example Python script available in the examples directory.

.. warning:: Memory usage

If you are planing to work on multisubject data, you will most likely
use a lot of memory. This means that you will probably need a
**64bit** OS and version of Python. If you get a *MemoryError* when
running CanICA on your data, you should switch to 64bit.

Installing CanICA

CanICA is meant to be merged in the NiPy collaborative project for NeuroImaging in Python soon.

Currently, to install CanICA, you need to install:

* **Standard scientific Python packages**: recent versions of 
  numpy, scipy, matplotlib, and sympy. The easiest way to install
  these is to rely on a distribution. Either your Linux distribution,
  if you are running Linux, or EPD  or Python(x,y) -
  http://www.enthought.com/products/epd.php
  http://pythonxy.com
  
* **NiPy**: NeuroImaging in Python 
  http://nipy.sourceforge.net/nipy/stable/index.html
  We suggest that you install the latest development version of NiPy:
  https://github.com/nipy/nipy/zipball/master

  You can check out the NiPy install instructions for more details:
  http://nipy.sourceforge.net/nipy/stable/users/installation.html

* **joblib**: 
  http://pypi.python.org/pypi/joblib

You can download the latest CanICA code from http://github.com/GaelVaroquaux/canica/zipball/master

.. note::

Python packages can be installed from source using the following
command::

python setup.py install

Licensing

CanICA is BSD-licenced (3 clause):

This software is OSI Certified Open Source Software.
OSI Certified is a certification mark of the Open Source Initiative.

Copyright (c) 2009-2010, Gael Varoquaux
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, 
  this list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of Gael Varoquaux. nor the names of other CanICA 
  contributors may be used to endorse or promote products derived from 
  this software without specific prior written permission.

**This software is provided by the copyright holders and contributors
"as is" and any express or implied warranties, including, but not
limited to, the implied warranties of merchantability and fitness for
a particular purpose are disclaimed. In no event shall the copyright
owner or contributors be liable for any direct, indirect, incidental,
special, exemplary, or consequential damages (including, but not
limited to, procurement of substitute goods or services; loss of use,
data, or profits; or business interruption) however caused and on any
theory of liability, whether in contract, strict liability, or tort
(including negligence or otherwise) arising in any way out of the use
of this software, even if advised of the possibility of such
damage.**