This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

Below is a list of all publications about PyMVPA that have been published so far (in chronological order). If you use PyMVPA in your research please cite the one that matches best. In addition there is also a list of studies done by other groups employing PyMVPA somewhere in the analysis.

Peer-reviewed publications

Hanke, M., Halchenko, Y. O., Haxby, J. V., and Pollmann, S. (accepted) Statistical learning analysis in neuroscience: aiming for transparency. Frontiers in Neuroscience.

Focused review article emphasizing the role of transparency to facilitate adoption and evaluation of statistical learning techniques in neuroimaging research.

Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Fründ, I., Rieger, J. W., Herrmann, C. S., Haxby, J. V., Hanson, S. J. and Pollmann, S. (2009) PyMVPA: a unifying approach to the analysis of neuroscientific data. Frontiers in Neuroinformatics, 3:3.

Demonstration of PyMVPA capabilities concerning multi-modal or modality-agnostic data analysis.

Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2009). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7, 37-53.

First paper introducing fMRI data analysis with PyMVPA.

Posters

Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2008). PyMVPA: A Python toolbox for machine-learning based data analysis.

Poster emphasizing PyMVPA’s capabilities concerning multi-modal data analysis at the annual meeting of the Society for Neuroscience, Washington, 2008.

Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2008). PyMVPA: A Python toolbox for classifier-based data analysis.

First presentation of PyMVPA at the conference Psychologie und Gehirn [Psychology and Brain], Magdeburg, 2008. This poster received the poster prize of the German Society for Psychophysiology and its Application.

Studies employing PyMVPA

• Sun et al. (2009): Elucidating an MRI-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms.
• Manelis et al. (2010): Implicit memory for object locations depends on reactivation of encoding-related brain regions