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.