Multi Voxel Pattern Classification Toolbox

The Multi Voxel Pattern Classification toolbox (MVPC toolbox) is a MATLAB toolbox to facilitate multi-voxel pattenr classification analysis on fMRI data.
MVPC toolbox was originally developed by Satoshi Hirose and Isao Nambu.
What can we do with this toolbox ?
You can perform below analyses with this toolbox.
1) Preprocessings including
              •Conversion from DICOM files to NIFTI files,
              •Usual preprocessings for fMRI data (slice timing correction, co-registration, normalization etc.)
              •GLM univariate analysis
2) Voxel and volume selection for decoding.
3) fMRI decoding by using various algorithms.
4) Leave-one-session-out and leave-two-session-out cross validation for performance evaluation and adjustment of (a) hyper-parameter(s).
5) save results in graphs.
Algorithms
· Sparse Logistic Regression (SLR; Yamashita et al., 2008)
· iSLR (Hirose et al., submitted)
· L1-norm reguralized sparse logistic regresssion
· Elastic Net (Zou and Hastie, 2004)
· Support Vector Machine
· SVM with Recursive Feature Elimination (Rakotomamonjy, 2003)
External Toolboxes
Machine learning implementation: We rely on the following toolboxes for algorithm implementaion. Please download and install them at their webpage linked below.
· Sparse Logistic Regression Toolbox for SLR
· GLMnet in MATLAB for L1-norm reguralized sparse logistic regresssion and Elastic Net
· LibSVM for SVM
Preprocessing, univariate analysis, ROI definition and extraction: We rely on SPM5, and its extensions, Volumes Toolbox and Anatomy Toolbox for the processes.
· SPM5
· Volumes Toolbox
· Anatomy Toolbox


Download
· MVPC toolbox (version 1.0; 36KB)    Download !!
· Tutorial (version 1; 662MB)             Download !!
Copyright
MVPC toolbox is free but copyrighted software, distributed under the terms of the GNU General Public Licence. Further details on copyleft can be found at http://www.gnu.org/copyleft/.
Feedback and bug report
Any feedback and bug reports are welcome. Please mail to satoshi.hirose [at] nict.go.jp (please replace the [at] with the '@' symbol).
Future Update
· Weight-based functional mapping
· Decoding from Beta-map (or other statistical values)
· GUI for setting parameters
· Preprocessing with recent version of SPM
· Readme for parallel processing
· Readme for clsfy
· Smaller version of Tutorial