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.
· 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

· MVPC toolbox (version 1.0; 36KB)    Download !!
· Tutorial (version 1; 662MB)             Download !!
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
Feedback and bug report
Any feedback and bug reports are welcome. Please mail to satoshi.hirose [at] (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