PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data. Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python’s ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.
Keywords for this software
References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Kampa, K.; Mehta, S.; Chou, C.A.; Chaovalitwongse, W.A.; Grabowski, T.J.: Sparse optimization in feature selection: application in neuroimaging (2014)
- Kasabov, Nikola K.: Neucube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data (2014) ioport
- Mahmoudi, Abdelhak; Takerkart, Sylvain; Regragui, Fakhita; Boussaoud, Driss; Brovelli, Andrea: Multivoxel pattern analysis for fMRI data: a review (2012)