Multivariate Pattern Analysis in Python

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# 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.
- 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.

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