To provide the most recent news and documentation www.pymvpa.org reflects the development 2.0 series (renamed 0.6 series) of PyMVPA. If you are interested in the documentation of the previous stable 0.4 series of PyMVPA, please visit v04.pymvpa.org.

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 Known To Be Used

• Center for Mind/Brain Sciences, University of Trento, Italy
• 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
• Donders Center for Cognition, Radboud University Nijmegen, Netherlands
• 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 Psychiatry, University of Wisconsin, Madison, USA
• Department of Psychology, Yale University, USA

Studies employing PyMVPA

• Hiroyuki et al., Frontiers in Neuroinformatics (2012): Decoding Semantics across fMRI sessions with Different Stimulus Modalities: A practical MVPA Study.
• Gorlin et al., PNAS (2012): Imaging prior information in the brain.
• Raizada and Connolly, Cognitive Neuroscience (In press): What makes different people’s representations alike: neural similarity-space solves the problem of across-subject fMRI decoding. Preprint PDF and code are available
• Connolly et al., Journal of Neuroscience (2012): Representation of Biological Classes in the Human Brain.
• Duff et al., NeuroImage (2011): Task-driven ICA feature generation for accurate and interpretable prediction using fMRI.
• Haxby et al., Neuron (2011): A common, high-dimensional model of the representational space in human ventral temporal cortex.
• Jimura and Poldrack, Neuropsychologia (2011): Analyses of regional-average activation and multivoxel pattern information tell complementary stories
• Carlin et al., Current Biology (2011): A head view-invariant representation of gaze direction in anterior superior temporal sulcus
• Kaunitz et al., Frontiers in Perception Science (2011): Intercepting the first pass: rapid categorization is suppressed for unseen stimuli.
• Carlin et al., Cerebral Cortex (2011): Direction-Sensitive Codes for Observed Head Turns in Human Superior Temporal Sulcus.
• Kubilius et al., Psychological Science (2011): Emergence of perceptual gestalts in the human visual cortex: The case of the configural superiority effect. Complete suite of sources from stimuli delivery (PsychoPy) to data analysis (PyMVPA) is available
• Manelis et al., Cerebral Cortex (2011): Dynamic Changes In The Medial Temporal Lobe During Incidental Learning Of Object–Location Associations.
• Meyer et al., Cerebral Cortex (2011): Seeing Touch Is Correlated with Content-Specific Activity in Primary Somatosensory Cortex.
• Woolgar et al., NeuroImage (2010): Multi-voxel coding of stimuli, rules, and responses in human frontoparietal cortex.
• Clithero et al., NeuroImage (2010): Within- and cross-participant classifiers reveal different neural coding of information.
• Gilliam et al., Proceedings of the International Conference on Pattern Recognition (2010): Scribe Identification in Medieval English Manuscripts.
• Cohen at al., Frontiers in Human Neuroscience (2010): Decoding Developmental Differences and Individual Variability in Response Inhibition Through Predictive Analyses Across Individuals.
• 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

• Pernet et al., Front. Psychology (2011). Single-trial analyses: why bother?
• Schackman et al., Nature Reviews Neuroscience (2011): The integration of negative affect, pain and cognitive control in the cingulate cortex.
• Margulies et al., Magnetic Resonance Materials in Physics, Biology and Medicine (2010): Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity.
• Shiffrin, Topics in Cognitive Science, (2010): Perspectives on Modeling in Cognitive Science.
• LaConte, NeuroImage (2010): Decoding fMRI brain states in real-time.
• Legge & Badii, Proceedings of the 2nd International Conference on Emerging Network Intelligence (2010): An Application of Pattern Matching for the Adjustment of Quality of ServiceMetrics.
• 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.