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.
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
experiments.
Bandettini, P. A. (2009). Seven topics in functional magnetic resonance imaging. Journal of Integrative Neuroscience, 8, 371–403.
Carlin, J. D., Calder, A. J., Kriegeskorte, N., Nili, H. & Rowe, J. B. (2011). A head view-invariant representation of gaze direction in anterior superior temporal sulcus. Curr Biol, 21, 1817–21.
Carlin, J. D., Rowe, J. B., Kriegeskorte, N., Thompson, R. & Calder, A. J. (2011). Direction-Sensitive Codes for Observed Head Turns in Human Superior Temporal Sulcus. Cerebral Cortex, **, .
Chen, X., Pereira, F., Lee, W., Strother, S. & Mitchell, T. (2006). Exploring predictive and reproducible modeling with the single-subject FIAC dataset. Human Brain Mapping, 27, 452–461.
This paper illustrates the necessity to consider the stability or
reproducibility of a classifier’s feature selection as at least equally
important to it’s generalization performance.
Clithero, J. A., Smith, D. V., Carter, R. M. & Huettel, S. A. (2010). Within- and cross-participant classifiers reveal different neural coding of information. NeuroImage.
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.
Gilliam, T., Wilson, R. C. & Clark, J. A. (2010). Scribe Identification in Medieval English Manuscripts. Proceedings of the International Conference on Pattern Recognition.
Gorlin, S., Meng, M., Sharma, J., Sugihara, H., Sur, M. & Sinha, P. (2012). Imaging prior information in the brain. Proceedings of the National Academy of Sciences, 109, 7935-7940.
Hanke, M., Halchenko, Y. O., Haxby, J. V. & Pollmann, S. (2010). Statistical learning analysis in neuroscience: aiming for transparency. Frontiers in Neuroscience, 4, 38–43.
Focused review article emphasizing the role of transparency to facilitate
adoption and evaluation of statistical learning techniques in neuroimaging
research.
Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2009). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7, 37–53.
Introduction into the analysis of fMRI data using PyMVPA.
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
Hanson, S. J. & Halchenko, Y. O. (2008). Brain reading using full brain support vector machines for object recognition: there is no “face” identification area. Neural Computation, 20, 486–503.
Hanson, S. J., Matsuka, T. & Haxby, J. V. (2004). Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area?. NeuroImage, 23, 156–166.
Hastie, T., Tibshirani, R. & Friedman, J. H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer: New York.
Excellent summary of virtually all techniques relevant to the field. A free PDF
version of this book is available from the authors’ website at
http://www-stat.stanford.edu/%7Etibs/ElemStatLearn/
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.
Hiroyuki, A., Brian, M., Li, N., Yumiko, S. & Massimo, P. (2012). Decoding Semantics across fMRI sessions with Different Stimulus Modalities: A practical MVPA Study. Frontiers in Neuroinformatics, 6.
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.
Jimura, K. & Poldrack, R. (2011). Analyses of regional-average activation and multivoxel pattern information tell complementary stories. Neuropsychologia.
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.
Kienzle, W., Franz, M. O., Schölkopf, B. & Wichmann, F. A. (In press). Center-surround patterns emerge as optimal predictors for human saccade targets. Journal of Vision.
This paper offers an approach to make sense out of feature sensitivities of
non-linear classifiers.
Kriegeskorte, N., Goebel, R. & Bandettini, P. A. (2006). Information-based functional brain mapping. Proceedings of the National Academy of Sciences of the USA, 103, 3863–3868.
Kriegeskorte, N., Mur, M. & Bandettini, P. A. (2008). Representational similarity analysis - connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2, 4.
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.
Kubilius, J., Wagemans, J. & Beeck, H. O. d. (2011). Emergence of perceptual gestalts in the human visual cortex: The case of the configural superiority effect. Psychological Science, in press.
Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324.
Paper introducing Modified NIST (MNIST) dataset for performance comparisons of
character recognition performance across a variety of classifiers.
Legge, D. & Badii, A. (2010). An Application of Pattern Matching for the Adjustment of Quality of Service Metrics. The International Conference on Emerging Network Intelligence.
Manelis, A., Hanson, C. & Hanson, S. J. (2010). Implicit memory for object locations depends on reactivation of encoding-related brain regions. Human Brain Mapping.
Manelis, A., Reder, L. M. & Hanson, S. J. (2011). Dynamic Changes In The Medial Temporal Lobe During Incidental Learning Of Object–Location Associations. Cerebral Cortex.
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.
Meyer, K., Kaplan, J. T., Essex, R., Damasio, H. & Damasio, A. (2011). Seeing Touch Is Correlated with Content-Specific Activity in Primary Somatosensory Cortex. Cerebral Cortex.
Meyer, K., Kaplan, J. T., Essex, R., Webber, C., Damasio, H. & Damasio, A. (2010). Predicting visual stimuli based on activity in auditory cortices. Nature Neuroscience.
Mitchell, T., Hutchinson, R., Niculescu, R. S., Pereira, F., Wang, X., Just, M. & Newman, S. (2004). Learning to Decode Cognitive States from Brain Images. Machine Learning, 57, 145–175.
Mur, M., Bandettini, P. A. & Kriegeskorte, N. (2009). Revealing representational content with pattern-information fMRI–an introductory guide. Social Cognitive and Affective Neuroscience.
Nichols, T. E. & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human Brain Mapping, 15, 1–25.
Overview of standard nonparametric randomization and permutation testing
applied to neuroimaging data (e.g. fMRI)
Norman, K. A., Polyn, S. M., Detre, G. J. & Haxby, J. V. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Science, 10, 424–430.
O’Toole, A. J., Jiang, F., Abdi, H. & Haxby, J. V. (2005). Partially Distributed Representations of Objects and Faces in Ventral Temporal Cortex . Journal of Cognitive Neuroscience, 17, 580–590.
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.
Pessoa, L. & Padmala, S. (2007). Decoding near-threshold perception of fear from distributed single-trial brain activation. Cerebral Cortex, 17, 691–701.
Analysis of slow event-related fMRI data using patter classification techniques.
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
some features.
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.
Woolgar, A., Thompson, R., Bor, D. & Duncan, J. (2010). Multi-voxel coding of stimuli, rules, and responses in human frontoparietal cortex. NeuroImage.
Xu, H., Lorbert, A., Ramadge, P. J., Guntupalli, J. S. & Haxby, J. V. (2012). Regularized hyperalignment of multi-set fMRI data. Proceedings of the 2012 IEEE Signal Processing Workshop.
Zou, H. & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B, 67, 301–320.
