@article {19184561, title = {PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.}, journal = {Neuroinformatics}, volume = {7}, year = {2009}, month = {2009 Spring}, pages = {37-53}, abstract = {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{\textquoteright}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.}, author = {Hanke, Michael and Halchenko, Yaroslav O and Sederberg, Per B and Hanson, Stephen Jos{\'e} and Haxby, James V and Pollmann, Stefan} } @article {19212459, title = {PyMVPA: A Unifying Approach to the Analysis of Neuroscientific Data.}, journal = {Frontiers in neuroinformatics}, volume = {3}, year = {2009}, month = {2009}, pages = {3}, abstract = {The Python programming language is steadily increasing in popularity as the language of choice for scientific computing. The ability of this scripting environment to access a huge code base in various languages, combined with its syntactical simplicity, make it the ideal tool for implementing and sharing ideas among scientists from numerous fields and with heterogeneous methodological backgrounds. The recent rise of reciprocal interest between the machine learning (ML) and neuroscience communities is an example of the desire for an inter-disciplinary transfer of computational methods that can benefit from a Python-based framework. For many years, a large fraction of both research communities have addressed, almost independently, very high-dimensional problems with almost completely non-overlapping methods. However, a number of recently published studies that applied ML methods to neuroscience research questions attracted a lot of attention from researchers from both fields, as well as the general public, and showed that this approach can provide novel and fruitful insights into the functioning of the brain. In this article we show how PyMVPA, a specialized Python framework for machine learning based data analysis, can help to facilitate this inter-disciplinary technology transfer by providing a single interface to a wide array of machine learning libraries and neural data-processing methods. We demonstrate the general applicability and power of PyMVPA via analyses of a number of neural data modalities, including fMRI, EEG, MEG, and extracellular recordings.}, author = {Hanke, Michael and Halchenko, Yaroslav O and Sederberg, Per B and Olivetti, Emanuele and Fr{\"u}nd, Ingo and Rieger, Jochem W and Herrmann, Christoph S and Haxby, James V and Hanson, Stephen Jos{\'e} and Pollmann, Stefan} }