Multivariate Pattern Analysis in Python

# 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

…and many more (stopped extending this list in 2012).

## Studies employing PyMVPA

### 2016

- Guntupalli et al., Cerebral Cortex (2016). A Model of Representational Spaces in Human Cortex
- Watson et al., NeuroImage (2016). Spatial properties of objects predict patterns of neural response in the ventral visual pathway
- Watson et al., NeuroImage (2016). Patterns of neural response in scene-selective regions of the human brain are affected by low-level manipulations of spatial frequency

### 2015

- Floren et al., Frontiers in Human Neuroscience (2015). Accurately decoding visual information from fMRI data obtained in a realistic virtual environment
- Merkel et al, NeuroImage (2015). Neural correlates of multiple object tracking strategies
- Pogoda, et al., Brain and Cognition (2015). Multivariate representation of food preferences in the human brain
- Emmerling et al., NeuroImage (2015). Decoding the direction of imagined visual motion using 7 T ultra-high field fMRI
- Danelli et al., Frontiers in Psychology (2015). Framing effects reveal discrete lexical-semantic and sublexical procedures in reading: an fMRI study
- Schlegel et al., NeuroImage (2015). The artist emerges: Visual art learning alters neural structure and function
- Maass et al., ELife (2015). Functional subregions of the human entorhinal cortex
- Sha et al., Journal of Cognitive Neuroscience (2015). The Animacy Continuum in the Human Ventral Vision Pathway
- Greisel et al., arXiv (2015). Photometric redshifts and model spectral energy distributions of galaxies from the SDSS-III BOSS DR10 data
- McNamee et al., Journal of Neuroscience (2015). Characterizing the associative content of brain structures involved in habitual and goal-directed actions in humans: a multivariate fMRI study
- Cole et al., Cerebral Cortex (2015). The behavioral relevance of task information in human prefrontal cortex
- Guo and Meng, NeuroImage (2015). The encoding of category-specific versus nonspecific information in human inferior temporal cortex

### 2014

- Rueschemeyer et al., Journal of Cognitive Neuroscience (2014). Observing, performing, and understanding actions: Revisiting the role of cortical motor areas in processing of action words.
- Hanke et al., Scientific Data (2014). A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie.
- Helfinstein et al., PNAS (2014). Predicting risky choices from brain activity patterns.
- Sha et al., Journal of Cognitive Neuroscience (2014). The animacy continuum in the human ventral vision pathway
- Schönwiesner et al., Cerebral Cortex (2014). Parcellation of Human and Monkey core auditory cortex with fMRI pattern classification and objective detection of tonotopic gradient reversals
- Klein and Zatorre, Cerebral Cortex (2014). Representations of invariant musical categories are decodable by pattern analysis of locally distributed BOLD responses in superior temporal and intraparietal sulci
- Huffman and Start, Hippocampus (2014). Multivariate pattern analysis of the human medial temporal lobe revealed representationally categorical cortex and representationally agnostic hippocampus
- Schlegel et al., NeuroImage (2014). The artist emerges: Visual art learning alters neural structure and function
- Jimura et al, Frontiers in Human Neuroscience (2014). The neural basis of task switching changes with skill acquisition
- Lee and McCarthy, Cerebral Cortex (2014). Functional Heterogeneity and Convergence in the Right Temporoparietal Junction
- Watson et al., NeuroImage (2014). Patterns of response to visual scenes are linked to the low-level properties of the image
- Schlichting and Preston, PNAS (2014). Memory reactivation during rest supports upcoming learning of related content
- Mittner et al., Journal of Neuroscience (2014). When the brain takes a break: A model-based analysis of mind wandering
- Wang et al., Journal of Neuroscience (2014). Distributed Value Representation in the Medial Prefrontal Cortex during Intertemporal Choices
- Parkinson et al., Journal of Neuroscience (2014). A common cortical metric for spatial, temporal, and social distance
- Kim et al., Frontiers in Human Neuroscience (2014). Discriminable spatial patterns of activation for faces and bodies in the fusiform gyrus
- Pollmann et al., NeuroImage (2014). The right temporo-parietal junction contributes to visual feature binding
- Fogelson et al., Frontiers in Psychology (2014). Unconscious neural processing differs with method used to render stimuli invisible
- Plitt et al., Social Neuroscience (2014). Are corporations people too? The neural correlates of moral judgments about companies and individuals
- Kubilius et al., Journal of Vision (2014). Encoding of configural regularity in the human visual system
- Heitmeyer et al., Automated Software Engineering (2014). Building high assurance human-centric decision systems

### 2013

- Anderson et al., Clinical Neuropsychology (2013). 7T fMRI reveals feasibility of covert visual attention-based brain–computer interfacing with signals obtained solely from cortical grey matter accessible by subdural surface electrodes
- Manelis and Reder, Cerebral Cortex (2013). he who is well prepared has half won the battle: an fMRI study of task preparation
- Kohler et al., NeuroImage (2013). Pattern classification precedes region-average hemodynamic response in early visual cortex.
- Hassabis et al., Cerebral Cortex (2013). Imagine all the people: How the brain creates and uses personality models to predict behavior.
- Smith et al., PNAS (2013). Decoding the anatomical network of spatial attention.
- Lescroart and Biederman, Cerebral Cortex (2013). Cortical representation of medial axis structure.
- Strnad et al., PloS one (2013). Multivoxel Pattern Analysis Reveals Auditory Motion Information in MT+ of Both Congenitally Blind and Sighted Individuals.
- Baumgartner et al., NeuroImage (2013). Evidence for feature binding in the superior parietal lobule.
- McNamee et al., Nature Neuroscience (2013). Category-dependent and category-independent goal-value codes in human ventromedial prefrontal cortex.
- Liang, et al., Nature Communications (2013). Primary sensory cortices contain distinguishable spatial patterns of activity for each sense.

### 2012

- Viswanathan et al., arXiv preprint (2012). On the geometric structure of fMRI searchlight-based information maps.
- Farrell et al., Biochemistry (2012). Toward Fast Determination of Protein Stability Maps: Experimental and Theoretical Analysis of Mutants of a Nocardiopsis prasina Serine Protease.
- Sobhani et al., PloS one (2012). Interpersonal liking modulates motor-related neural regions.
- Kingson et al., Journal of Neuroscience (2012). Sight and Sound Converge to Form Modality-Invariant Representations in Temporoparietal Cortex.
- Kaplan and Meyer, NeuroImage (2012). Multivariate pattern analysis reveals common neural patterns across individuals during touch observation.
- Carter et al., Science (2012). A distinct role of the temporal-parietal junction in predicting socially guided decisions.
- van der Laan, PloS one (2012). Appearance matters: neural correlates of food choice and packaging aesthetics.
- Merrill et al., Frontiers in Psychology (2012). Perception of words and pitch patterns in song and speech.
- Ekman et al., PNAS (2012). Predicting errors from reconfiguration patterns in human brain networks.
- 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 (2012): 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.

### 2011

- Cole et al, Frontiers in Human Neuroscience (2011). Rapid Transfer of Abstract Rules to Novel Contexts in Human Lateral Prefrontal Cortex.
- Vickery et al, Neuron (2011). Ubiquity and Specificity of Reinforcement Signals throughout 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.

### 2010

- 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.

### 2009

- 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

### 2014

- Haxby et al, Annual review of neuroscience (2014). Decoding neural representational spaces using multivariate pattern analysis
- Avants et al, NeuroImage (2014). Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population

### 2013

- Adluru et al., Neuroinformatics (2013). Penalized likelihood phenotyping: unifying voxelwise analyses and multi-voxel pattern analyses in neuroimaging: penalized likelihood phenotyping.
- Schrouff et al., Neuroinformatics (2013). PRoNTo: Pattern Recognition for Neuroimaging Toolbox.

### 2012

- Albanese et al., arXiv preprint (2012). mlpy: machine learning Python.
- Stelzer and Turner, NeuroImage (2012). Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control.

### 2011

- Meyer and Kaplan, Journal of Visualized Experiments (2011). Cross-Modal Multivariate Pattern Analysis.
- Hollmann et al, PloS one (2011). Predicting decisions in human social interactions using real-time fMRI and pattern classification.
- Hanson and Schmidt, NeuroImage (2011). High-resolution imaging of the fusiform face area (FFA) using multivariate non-linear classifiers shows diagnosticity for non-face categories.
- Pereira and Botvinick, NeuroImage (2011). Information mapping with pattern classifiers: a comparative study.
- Pedregosa et al., The Journal of Machine Learning Research (2011). Scikit-learn: Machine Learning in Python.
- 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.

### 2010

- 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.

### 2009

- 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.