.. -*- mode: rst -*-
.. vi: set ft=rst sts=4 ts=4 sw=4 et tw=79:

Below is a list of 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, and email use the reference so
we could add it to our :ref:`chap_whoisusingit` page.

Peer-reviewed publications
--------------------------

**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.

.. _PyMVPA\: A Python toolbox for multivariate pattern analysis of fMRI data: http://dx.doi.org/10.1007/s12021-008-9041-y

**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.

.. _PyMVPA\: a unifying approach to the analysis of neuroscientific data: http://dx.doi.org/10.3389/neuro.11.003.2009

**Hanke, M., Halchenko, Y. O., Haxby, J. V., and Pollmann, S.** (2010) `Statistical learning analysis in neuroscience: aiming for transparency <http://dx.doi.org/10.3389/neuro.01.007.2010>`_. Frontiers in Neuroscience. 4,1: 38-43
  Focused review article emphasizing the role of transparency to facilitate
  adoption and evaluation of statistical learning techniques in neuroimaging
  research.

**Haxby, J. V., Guntupalli, J. S., Connolly, A. C., Halchenko, Y. O., Conroy, B. R., Gobbini, M. I., Hanke, M. & Ramadge, P. J.** (2011). `A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex <http://dx.doi.org/10.1016/j.neuron.2011.08.026>`_. *Neuron*, *72*, 404–416
  The :class:`~mvpa2.algorithms.hyperalignment.Hyperalignment` paper
  demonstrating its application to fMRI data in rich perceptual (movie) and
  categorization (monkey-dog) experiments.

..  `Data <http://data.pymvpa.org/datasets/haxby2011-hyper>` used for the analyses in
..  the paper and `PyMVPA analysis script <https://github.com/HaxbyLab/paper-haxby2011-hyper>`__
..  are available


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*.

.. _PyMVPA\: A Python toolbox for classifier-based data analysis.: http://www.pymvpa.org/files/PyMVPA_PuG2008.pdf
.. _PyMVPA\: A Python toolbox for machine-learning based data analysis.: http://www.pymvpa.org/files/PyMVPA_SfN2008.pdf
.. _Magdeburg: http://www.magdeburg.de/

