PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data
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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.
KeywordsPython Neuroimaging software Image analysis MVPA Scripting Machine learning Functional magnetic resonance imaging
Michael Hanke and Stefan Pollmann were supported by the German Academic Exchange Service (Grant: PPP-USA D/05/504/7). Per Sederberg was supported by National Institutes of Health NRSA grant MH080526. Yaroslav O. Halchenko and Dr. Stephen J. Hanson were supported by National Science Foundation (grant: SBE 0751008) and James McDonnell Foundation (grant: 220020127).
- Chang, C.-C., & Lin, C.-J. (2001). LIBSVM: A library for support vector machines. Software available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm/
- Detre, G., Polyn, S. M., Moore, C., Natu, V., Singer, B., Cohen, J., et al. (2006). The multi-voxel pattern analysis (MVPA) toolbox. Poster presented at the Annual Meeting of the Organization for Human Brain Mapping (Florence, Italy).Google Scholar
- Efron, B., & Tibshirani, R. (1993). An introduction to the Bootstrap. Boca Raton: Chapman & Hall/CRC.Google Scholar
- O’Toole, A. J., Jiang, F., Abdi, H., Penard, N., Dunlop, J. P., & Parent, M. A. (2007). Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data. Journal of Cognitive Neuroscience, 19, 1735–1752.PubMedCrossRefGoogle Scholar
- Schapire, R. E. (2003). The boosting approach to machine learning: An overview. In Denison, D. D., Hansen, M. H., Holmes, C., Mallick, B., & Yu, B. (Eds.), Nonlinear estimation and classification. New York: Springer.Google Scholar
- Sonnenburg, S., Braun, M., Ong, C. S., Bengio, S., Bottou, L., Holmes, G., et al. (2007). The need for open source software in machine learning. Journal of Machine Learning Research, 8, 2443–2466.Google Scholar
- Sonnenburg, S., Raetsch, G., Schaefer, C., & Schoelkopf, B. (2006). Large scale multiple kernel learning. Journal of Machine Learning Research, 7, 1531–1565.Google Scholar
- Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer.Google Scholar