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
This list aims to be a collection of literature, that is of particular interest
in the context of multivariate pattern analysis. It includes all references
cited throughout this manual, but also a number of additional manuscripts
containing descriptions of interesting analysis methods or fruitful
Bandettini, P. A. (2009). Seven topics in functional magnetic resonance imaging. Journal of Integrative Neuroscience, 8, 371–403.
Cohen, J. R., Asarnow, R. F., Sabb, F. W., Bilder, R. M., Bookheimer, S. Y., Knowlton, B. J. & Poldrack, R. A. (2010). Decoding developmental differences and individual variability in response inhibition through predictive analyses across individuals. Frontiers in Human Neuroscience, 4:47.
Connolly, A. C., Guntupalli, J. S., Gors, J., Hanke, M., Halchenko, Y. O., Wu, Y., Abdi, H. & Haxby, J. V. (2012). The Representation of Biological Classes in the Human Brain. Journal of Neuroscience, 32, 2608-2618.
Duff, E. P., Trachtenberg, A. J., CE, C. E. M., Howard, M. A., Wilson, F., Smith, S. M. & Woolrich, M. W. (2011). Task-driven ICA feature generation for accurate and interpretable prediction using fMRI. NeuroImage, 60, 189-203.
Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Fründ, I., Rieger, J. W., Herrmann, C. S., Haxby, J. V., Hanson, S. J. & Pollmann, S. (2009). PyMVPA: A Unifying Approach to the Analysis of Neuroscientific Data. Frontiers in Neuroinformatics, 3, 3.
Demonstration of PyMVPA capabilities concerning multi-modal or
modality-agnostic data analysis.
Keywords: PyMVPA, fMRI, EEG, MEG, extracellularrecordings
Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L. & Pietrini, P. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293, 2425–2430.
Haxby, J. V., Guntupalli, J. S., Connolly, A. C., Halchenko, Y. O., Conroy, B. R., Gobbini, M. I., Hanke, M. & Ramadge, P. J. (2011). A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex. Neuron, 72, 404–416.
Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Med, 2, e124.
Simulation study speculating that it is more likely for a research claim to be
false than true. Along the way the paper highlights aspects to keep in mind
while assessing the ‘scientific significance’ of any given study, such as,
viability, reproducibility, and results.
Kaunitz, L. N., Kamienkowski, J. E., Olivetti, E., Murphy, B., Avesani, P. & Melcher, D. P. (2011). Intercepting the first pass: rapid categorization is suppressed for unseen stimuli. Frontiers in Perception Science, 2, 198.
Krishnapuram, B., Carin, L., Figueiredo, M. A. & Hartemink, A. J. (2005). Sparse multinomial logistic regression: fast algorithms and generalization bounds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 957–968.
Margulies, D. S., Böttger, J., Long, X., Lv, Y., Kelly, C., Schäfer, A., Goldhahn, D., Abbushi, A., Milham, M. P., Lohmann, G. & Villringer, A. (2010). Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity. Magnetic Resonance Materials in Physics, Biology and Medicine, 23, 289–307.
O’Toole, A. J., Jiang, F., Abdi, H., Penard, N., Dunlop, J. P. & Parent, M. A. (2007). Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data. Journal of Cognitive Neuroscience, 19, 1735–1752.
Olivetti, E., Veeramachaneni, S., Greiner, S. & Avesani, P. (2010). Brain Connectivity Analysis by Reduction to Pair Classification. The 2nd IAPR International Workshop on Cognitive Information Processing.
Pereira, F., Mitchell, T. & Botvinick, M. (2009). Machine learning classifiers and fMRI: A tutorial overview. NeuroImage, 45, 199–209.
Raizada, R. D. & Connolly, A. C. (In press). What makes different people’s representations alike: neural similarity-space solves the problem of across-subject fMRI decoding. Journal of Cognitive Neuroscience.
Sato, J. R., Mourão-Miranda, J., Martin, M. d. G. M., Amaro, E., Morettin, P. A. & Brammer, M. J. (2008). The impact of functional connectivity changes on support vector machines mapping of fMRI data. Journal of Neuroscience Methods, 172, 94–104.
Discussion of possible scenarios where univariate and multivariate (SVM)
sensitivity maps derived from the same dataset could differ. Including the
case were univariate methods would assign a substantially larger score to
Shackman, A. J., Salomons, T. V., Slagter, H. A., Fox, A. S., Winter, J. J. & Davidson, R. J. (2011). The integration of negative affect, pain and cognitive control in the cingulate cortex. Nature Reviews Neuroscience, 12, 154–167.
Sun, D., van Erp, T. G., Thompson, P. M., Bearden, C. E., Daley, M., Kushan, L., Hardt, M. E., Nuechterlein, K. H., Toga, A. W. & Cannon, T. D. (2009). Elucidating an MRI-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms. Biological Psychiatry, 66, 1055–1060.
First published study employing PyMVPA for MRI-based analysis of Psychosis.
Trautmann, E., Ray, L. & Lever, J. (2009). Development of an autonomous robot for ground penetrating radar surveys of polar ice. The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1685–1690.
Study using PyMVPA to perform immobilization detection to improve navigation
reliability of an autonomous robot.