Do not use this code: it is outdated and no longer maintained.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