This content refers to an unreleased development version of PyMVPA

Who Is Using It?

If you are using PyMVPA or have published a study employing it, please leave a comment at the bottom of this page, if you want to be listed here as well.

Institutions Where PyMVPA Is Used

• Department of Psychological and Brain Sciences, Dartmouth College, USA
• Thayer School of Engineering, Dartmouth College, USA
• Department of Psychology & Neuroscience, Duke University, USA
• Fondazione Bruno Kessler, Italy
• Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA
• Department of Neurology, Max Planck Insititute for Neurological Research, Cologne, Germany
• MRC Cognition and Brain Sciences Unit, Cambridge, UK
• Department of Experimental Psychology, Otto-von-Guericke-University Magdeburg, Germany
• Department of Psychology, University of California at Los Angeles, USA
• Center for Functional Neuroimaging, University of Pennsylvania, USA
• Brain & Creativity Institute, University of Southern California, USA
• Imaging Research Center, University of Texas at Austin, USA
• Department of Psychology, Yale University, USA

Studies employing PyMVPA

• Meyer et al., Nature Neuroscience (2010): Predicting visual stimuli based on activity in auditory cortices.
• Manelis et al., Human Brain Mapping (2010): Implicit memory for object locations depends on reactivation of encoding-related brain regions.
• Trautmann et al., IEEE/RSJ International Conference on Intelligent Robots and Systems (2009): Development of an autonomous robot for ground penetrating radar surveys of polar ice.
• Sun et al., Biological Psychiatry (2009): Elucidating an MRI-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms.

Articles referring to PyMVPA

• Spacek et al., The Neuromorphic Engineer (2009): Python in Neuroscience.
• Bandettini, Journal of Integrative Neuroscience (2009): Seven topics in functional magnetic reasonance imaging.
• Garcia et al., Frontiers in Neuroinformatics (2009): OpenElectrophy: An Electrophysiological Data- and Analysis-Sharing Framework.
• Mur et al., Social Cognitive and Affective Neuroscience (2009): Revealing representational content with pattern-information fMRI – an introductory guide.
• Pereira et al., NeuroImage (2009): Machine learning classifiers and fMRI: A tutorial overview.