References

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.

Adluru, N., Hanlon, B. M., Lutz, A., Lainhart, J. E., Alexander, A. L. & Davidson, R. J. (2013). Penalized Likelihood Phenotyping: Unifying Voxelwise Analyses and Multi-Voxel Pattern Analyses in Neuroimaging. Neuroinformatics, 1-21.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.1007/s12021-012-9175-9

Albanese, D., Visintainer, R., Merler, S., Riccadonna, S., Jurman, G. & Furlanello, C. (2012). mlpy: machine learning Python. arXiv preprint arXiv:1202.6548.
Keywords: pymvpa-reference
Andersson, P., Ramsey, N. F., Viergever, M. A. & Pluim, J. P. (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. Clinical neurophysiology, 124, 2191-2197.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1016/j.clinph.2013.05.009

Avants, B. B., Libon, D. J., Rascovsky, K., Boller, A., McMillan, C. T., Massimo, L., Coslett, H. B., Chatterjee, A., Gross, R. G. & Grossman, M. (2014). Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population. NeuroImage, 84, 698-711.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.1016/j.neuroimage.2013.09.048

URL: http://dx.doi.org/10.1016/j.neuroimage.2013.09.048

Bandettini, P. A. (2009). Seven topics in functional magnetic resonance imaging. Journal of Integrative Neuroscience, 8, 371–403.

Keywords: pymvpa-reference

URL: http://www.ncbi.nlm.nih.gov/pubmed/19938211

Baumgartner, F., Hanke, M., Geringswald, F., Zinke, W., Speck, O. & Pollmann, S. (2013). Evidence for feature binding in the superior parietal lobule. NeuroImage, 68, 173-180.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1016/j.neuroimage.2012.12.002

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.
DOI: http://dx.doi.org/10.1016/j.cub.2011.09.025
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, **, .

Keywords: pymvpa, fMRI, searchlight

DOI: http://dx.doi.org/10.1093/cercor/bhr061

URL: http://cercor.oxfordjournals.org/content/early/2011/06/27/cercor.bhr061.short

Carter, R. M., Bowling, D. L., Reeck, C. & Huettel, S. A. (2012). A distinct role of the temporal-parietal junction in predicting socially guided decisions. Science, 337, 109-111.
DOI: http://dx.doi.org/10.1126/science.1219681
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.

Keywords: feature selection, feature selection stability

DOI: http://dx.doi.org/10.1002/hbm.20243

URL: http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=16565951

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.

DOI: http://dx.doi.org/10.1016/j.neuroimage.2010.03.057

URL: http://www.ncbi.nlm.nih.gov/pubmed/20347995

Cohen, J. (1994). The earth is round (p< 0.05). American Psychologist, 49, 997–1003.

Classical critic of null hypothesis significance testing

Keywords: hypothesis testing

URL: http://www.citeulike.org/user/mdreid/article/2643653

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.

DOI: http://dx.doi.org/10.3389/fnhum.2010.00047

URL: http://www.ncbi.nlm.nih.gov/pubmed/20661296

Cole, M. W., Etzel, J. A., Zacks, J. M., Schneider, W. & Braver, T. S. (2011). Rapid transfer of abstract rules to novel contexts in human lateral prefrontal cortex. Frontiers in Human Neuroscience, 5.
DOI: http://dx.doi.org/10.3389/fnhum.2011.00142
Cole, M. W., Ito, T. & Braver, T. S. (2015). The Behavioral Relevance of Task Information in Human Prefrontal Cortex. Cerebral Cortex.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1093/cercor/bhv072

URL: http://dx.doi.org/10.1093/cercor/bhv072

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.

DOI: http://dx.doi.org/10.1523/JNEUROSCI.5547-11.2012

URL: http://www.jneurosci.org/content/32/8/2608#aff-4

Demšar, J. (2006). Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research, 7, 1–30.

This is a review of several classifier benchmark procedures.

URL: http://portal.acm.org/citation.cfm?id=1248548

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.
URL: http://www.ncbi.nlm.nih.gov/pubmed/22227050
Efron, B., Trevor, H., Johnstone, I. & Tibshirani, R. (2004). Least Angle Regression. Annals of Statistics, 32, 407–499.

Keywords: least angle regression, LARS

DOI: http://dx.doi.org/10.1214/009053604000000067

Ekman, M., Derrfuss, J., Tittgemeyer, M. & Fiebach, C. J. (2012). Predicting errors from reconfiguration patterns in human brain networks. Proceedings of the National Academy of Sciences, 109, 16714-16719.
DOI: http://dx.doi.org/10.1073/pnas.1207523109
Farrell, D., Webb, H., Johnston, M. A., Poulsen, T. A., O’Meara, F., Christensen, L. L., Beier, L., Borchert, T. V. & Nielsen, J. E. (2012). Toward Fast Determination of Protein Stability Maps: Experimental and Theoretical Analysis of Mutants of a Nocardiopsis prasina Serine Protease. Biochemistry, 51, 5339-5347.
DOI: http://dx.doi.org/10.1021/bi201926f
Fisher, R. A. (1925). Statistical methods for research workers. Oliver and Boyd: Edinburgh.

One of the 20th century’s most influential books on statistical methods, which coined the term ‘Test of significance’.

Keywords: statistics, hypothesis testing

URL: http://psychclassics.yorku.ca/Fisher/Methods/

Fogelson, S. V., Kohler, P. J., Miller, K. J., Granger, R. & Tse, P. U. (2014). Unconscious neural processing differs with method used to render stimuli invisible. Frontiers in Psychology, 5.

Keywords: pymvpa

DOI: http://dx.doi.org/10.3389/fpsyg.2014.00601

URL: http://dx.doi.org/10.3389/fpsyg.2014.00601

Garcia, S. & Fourcaud-Trocmé, N. (2009). OpenElectrophy: An Electrophysiological Data- and Analysis-Sharing Framework. Front Neuroinformatics, 3, 14.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.3389/neuro.11.014.2009

URL: http://www.ncbi.nlm.nih.gov/pubmed/19521545

Gilliam, T., Wilson, R. C. & Clark, J. A. (2010). Scribe Identification in Medieval English Manuscripts. Proceedings of the International Conference on Pattern Recognition.
URL: ftp://ftp.computer.org/press/outgoing/proceedings/juan/icpr10b/data/4109b880.pdf
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.

DOI: http://dx.doi.org/10.1073/pnas.1111224109

URL: http://www.pnas.org/content/109/20/7935.abstract

Greisel, N., Seitz, S., Drory, N., Bender, R., Saglia, R. & Snigula, J. (2015). Photometric Redshifts and Model Spectral Energy Distributions of Galaxies From the SDSS-III BOSS DR10 Data. arXiv preprint arXiv:1505.01157.

Keywords: pymvpa

URL: http://arxiv.org/abs/1505.01157

Guntupalli, J. S., Hanke, M., Halchenko, Y. O., Connolly, A. C., Ramadge, P. J. & Haxby, J. V. (2016). A Model of Representational Spaces in Human Cortex. Cerebral Cortex.

Keywords: pymvpa, hyperalignment

DOI: http://dx.doi.org/10.1093/cercor/bhw068

Guo, B. & Meng, M. (2015). The encoding of category-specific versus nonspecific information in human inferior temporal cortex. NeuroImage.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1016/j.neuroimage.2015.04.006

URL: http://dx.doi.org/10.1016/j.neuroimage.2015.04.006

Guyon, I. & Elisseeff, A. (2003). An Introduction to Variable and Feature Selection. Journal of Machine Learning, 3, 1157–1182.
URL: http://www.jmlr.org/papers/v3/guyon03a.html
Hanke, M., Baumgartner, F. J., Ibe, P., Kaule, F. R., Pollmann, S., Speck, O., Zinke, W. & Stadler, J. (in press). A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie. Scientific Data.

Keywords: pymvpa

URL: http://www.studyforrest.org

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.

DOI: http://dx.doi.org/10.3389/neuro.01.007.2010

Hanke, M., Halchenko, Y. O., Sederberg, P. B. & Hughes, J. M. The PyMVPA Manual. Available online at http://www.pymvpa.org/PyMVPA-Manual.pdf.

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.

Keywords: PyMVPA, fMRI

DOI: http://dx.doi.org/10.1007/s12021-008-9041-y

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, extracellular recordings

DOI: http://dx.doi.org/10.3389/neuro.11.003.2009

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.

Keywords: support vector machine, SVM, feature selection, recursive feature elimination, RFE

DOI: http://dx.doi.org/10.1162/neco.2007.09-06-340

Hanson, S. J. & Schmidt, A. (2011). High-resolution imaging of the fusiform face area (FFA) using multivariate non-linear classifiers shows diagnosticity for non-face categories. NeuroImage, 54, 1715-1734.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.1016/j.neuroimage.2010.08.02

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.
DOI: http://dx.doi.org/10.1016/j.neuroimage.2004.05.020
Hassabis, D., Spreng, R. N., Rusu, A. A., Robbins, C. A., Mar, R. A. & Schacter, D. L. (2013). Imagine all the people: How the brain creates and uses personality models to predict behavior. Cerebral Cortex.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1093/cercor/bht042

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/

DOI: http://dx.doi.org/10.1007/b94608

URL: http://www-stat.stanford.edu/%7Etibs/ElemStatLearn/

Haxby, J. V., Connolly, A. C. & Guntupalli, J. S. (2014). Decoding neural representational spaces using multivariate pattern analysis. Annual review of neuroscience, 37, 435-456.
Keywords: pymvpa-reference
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.

Keywords: split-correlation classifier

DOI: http://dx.doi.org/10.1126/science.1063736

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.

Keywords: pymvpa, hyperalignment

DOI: http://dx.doi.org/10.1016/j.neuron.2011.08.026

URL: http://www.cell.com/neuron/abstract/S0896-6273%2811%2900781-1

Haynes, J. & Rees, G. (2006). Decoding mental states from brain activity in humans. Nature Reviews Neuroscience, 7, 523–534.

Review of decoding studies, emphasizing the importance of ethical issues concerning the privacy of personal thought.

DOI: http://dx.doi.org/10.1038/nrn1931

Hebart, M. N., Görgen, K. & Haynes, J. The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data. Frontiers in Neuroinformatics, 8.
DOI: http://dx.doi.org/10.3389/fninf.2014.00088
Heitmeyer, C. L., Pickett, M., Leonard, E. I., Archer, M. M., Ray, I., Aha, D. W. & Trafton, J. G. (2014). Building high assurance human-centric decision systems. Autom Softw Eng, 22, 159-197.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1007/s10515-014-0157-z

URL: http://dx.doi.org/10.1007/s10515-014-0157-z

Helfinstein, S. M., Schonberg, T., Congdon, E., Karlsgodt, K. H., Mumford, J. A., Sabb, F. W., Cannon, T. D., London, E. D., Bilder, R. M. & Poldrack, R. A. (2014). Predicting risky choices from brain activity patterns. Proceedings of the National Academy of Sciences, 111, 2470-2475.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1073/pnas.1321728111

URL: http://www.pnas.org/content/111/7/2470.abstract

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.

Keywords: pymvpa, fmri

DOI: http://dx.doi.org/10.3389/fninf.2012.00024

URL: http://www.frontiersin.org/Neuroinformatics/10.3389/fninf.2012.00024/full

Hollmann, M., Rieger, J. W., Baecke, S., Lützkendorf, R., Müller, C., Adolf, D. & Bernarding, J. (2011). Predicting decisions in human social interactions using real-time fMRI and pattern classification. PloS one, 6, e25304.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.1371/journal.pone.0025304

Huffman, D. J. & Stark, C. E. (2014). Multivariate pattern analysis of the human medial temporal lobe revealed representationally categorical cortex and representationally agnostic hippocampus. Hippocampus, 24, 1394-1403.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1002/hipo.22321

URL: http://dx.doi.org/10.1002/hipo.22321

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.

Keywords: hypothesis testing

DOI: http://dx.doi.org/10.1371/journal.pmed.0020124

Jain, A. & Kemp, C. C. (2012). Improving robot manipulation with data-driven object-centric models of everyday forces. Autonomous Robots, 1-17.

DOI: http://dx.doi.org/10.1007/s10514-013-9344-1

URL: http://www.hrl.gatech.edu/pdf/improve_everyday_forces.pdf

Jimura, K. & Poldrack, R. (2011). Analyses of regional-average activation and multivoxel pattern information tell complementary stories. Neuropsychologia.
DOI: http://dx.doi.org/10.1016/j.neuropsychologia.2011.11.007
Jimura, K., Cazalis, F., Stover, E. R. S. & Poldrack, R. A. (2014). The neural basis of task switching changes with skill acquisition. Front. Hum. Neurosci., 8.

Keywords: pymvpa

DOI: http://dx.doi.org/10.3389/fnhum.2014.00339

URL: http://dx.doi.org/10.3389/fnhum.2014.00339

Jurica, P. & van Leeuwen, C. (2009). OMPC: an open-source MATLAB-to-Python compiler. Frontiers in Neuroinformatics, 3, 5.
DOI: http://dx.doi.org/10.3389/neuro.11.005.2009
Jäkel, F., Schölkopf, B. & Wichmann, F. A. (2009). Does Cognitive Science Need Kernels?. Trends in Cognitive Sciences, 13, 381–388.

A summary of the relationship of machine learning and cognitive science. Moreover it also points out the role of kernel-based methods in this context.

Keywords: kernel methods, similarity

DOI: http://dx.doi.org/10.1016/j.tics.2009.06.002

URL: http://www.sciencedirect.com/science/article/B6VH9-4X4R9BC-1/2/e2e90008d0a8887878c72777462335fd

Kamitani, Y. & Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nature Neuroscience, 8, 679–685.

One of the two studies showing the possibility to read out orientation information from visual cortex.

DOI: http://dx.doi.org/10.1038/nn1444

Kaplan, J. T. & Meyer, K. (2012). Multivariate pattern analysis reveals common neural patterns across individuals during touch observation. Neuroimage, 60, 204-212.
DOI: http://dx.doi.org/10.1016/j.neuroimage.2011.12.059
Kasabov, N. K. (2014). NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Networks, 52, 62-76.

DOI: http://dx.doi.org/10.1016/j.neunet.2014.01.006

URL: http://dx.doi.org/10.1016/j.neunet.2014.01.006

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.

Keywords: pymvpa, eeg

DOI: http://dx.doi.org/10.3389/fpsyg.2011.00198

URL: http://www.frontiersin.org/perception_science/10.3389/fpsyg.2011.00198/full

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.
Kim, N. Y., Lee, S. M., Erlendsdottir, M. C. & McCarthy, G. (2014). Discriminable spatial patterns of activation for faces and bodies in the fusiform gyrus. Front. Hum. Neurosci., 8.

Keywords: pymvpa

DOI: http://dx.doi.org/10.3389/fnhum.2014.00632

URL: http://dx.doi.org/10.3389/fnhum.2014.00632

Klein, M. E. & Zatorre, R. J. (2014). Representations of Invariant Musical Categories Are Decodable by Pattern Analysis of Locally Distributed BOLD Responses in Superior Temporal and Intraparietal Sulci. Cerebral Cortex.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1093/cercor/bhu003

URL: http://dx.doi.org/10.1093/cercor/bhu003

Kohler, P. J., Fogelson, S. V., Reavis, E. A., Meng, M., Guntupalli, J. S., Hanke, M., Halchenko, Y. O., Connolly, A. C., Haxby, J. V. & Tse, P. U. (2013). Pattern classification precedes region-average hemodynamic response in early visual cortex. NeuroImage, 78, 249-260.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1016/j.neuroimage.2013.04.019

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.

Paper introducing the searchlight algorithm.

Keywords: searchlight

DOI: http://dx.doi.org/10.1073/pnas.0600244103

Kriegeskorte, N., Mur, M. & Bandettini, P. A. (2008). Representational similarity analysis - connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2, 4.
DOI: http://dx.doi.org/10.3389/neuro.06.004.2008
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.

Keywords: sparse multinomial logistic regression, SMLR

DOI: http://dx.doi.org/10.1109/TPAMI.2005.127

URL: http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=15943426

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.

Keywords: pymvpa, fMRI

DOI: http://dx.doi.org/10.1177/0956797611417000

Kubilius, J., Wagemans, J. & Beeck, H. P. O. d. (2014). Encoding of configural regularity in the human visual system. Journal of Vision, 14, 11-11.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1167/14.9.11

URL: http://dx.doi.org/10.1167/14.9.11

LaConte, S., Strother, S., Cherkassky, V., Anderson, J. & Hu, X. (2005). Support vector machines for temporal classification of block design fMRI data. NeuroImage, 26, 317–329.

Comprehensive evaluation of preprocessing options with respect to SVM-classifier (and others) performance on block-design fMRI data.

Keywords: SVM

DOI: http://dx.doi.org/10.1016/j.neuroimage.2005.01.048

Laconte, S. M. (2010). Decoding fMRI brain states in real-time. NeuroImage.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.1016/j.neuroimage.2010.06.052

URL: http://www.ncbi.nlm.nih.gov/pubmed/20600972

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.

Keywords: handwritten character recognition, multilayer neural networks, MNIST, statistical learning

DOI: http://dx.doi.org/10.1109/5.726791

Lee, S. M. & McCarthy, G. (2014). Functional Heterogeneity and Convergence in the Right Temporoparietal Junction. Cerebral Cortex.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1093/cercor/bhu292

URL: http://dx.doi.org/10.1093/cercor/bhu292

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.
Keywords: pymvpa-reference
Lescroart, M. D. & Biederman, I. (2013). Cortical representation of medial axis structure. Cerebral Cortex, 23, 629-637.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1093/cercor/bhs046

Liang, M., Mouraux, A., Hu, L. & Iannetti, G. (2013). Primary sensory cortices contain distinguishable spatial patterns of activity for each sense. Nature communications, 4.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1038/ncomms2979

Man, K., Kaplan, J. T., Damasio, A. & Meyer, K. (2012). Sight and sound converge to form modality-invariant representations in temporoparietal cortex. The Journal of Neuroscience, 32, 16629-16636.
DOI: http://dx.doi.org/10.1523/JNEUROSCI.2342-12.2012
Manelis, A. & Reder, L. M. (2013). He Who Is Well Prepared Has Half Won The Battle: An fMRI Study of Task Preparation. Cerebral Cortex.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1093/cercor/bht262

URL: http://cercor.oxfordjournals.org/content/early/2013/10/02/cercor.bht262.abstract

Manelis, A., Hanson, C. & Hanson, S. J. (2010). Implicit memory for object locations depends on reactivation of encoding-related brain regions. Human Brain Mapping.

Keywords: pymvpa, implicit memory, fMRI

DOI: http://dx.doi.org/10.1002/hbm.20992

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.

Keywords: pymvpa, fMRI

DOI: http://dx.doi.org/10.1093/cercor/bhr151

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.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.1007/s10334-010-0228-5

URL: http://www.ncbi.nlm.nih.gov/pubmed/20972883

McNamee, D., Liljeholm, M., Zika, O. & O’Doherty, J. P. (2015). Characterizing the Associative Content of Brain Structures Involved in Habitual and Goal-Directed Actions in Humans: A Multivariate fMRI Study. The Journal of Neuroscience, 35, 3764-3771.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1523/JNEUROSCI.4677-14.2015

McNamee, D., Rangel, A. & O’Doherty, J. P. (2013). Category-dependent and category-independent goal-value codes in human ventromedial prefrontal cortex. Nature neuroscience, 16, 479-485.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1038/nn.3337

Merrill, J., Sammler, D., Bangert, M., Goldhahn, D., Lohmann, G., Turner, R. & Friederici, A. D. (2012). Perception of words and pitch patterns in song and speech. Frontiers in psychology, 3, 76.
DOI: http://dx.doi.org/10.3389/fpsyg.2012.000
Meyer, K. & Kaplan, J. T. (2011). Cross-Modal Multivariate Pattern Analysis. Journal of visualized experiments: JoVE.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.3791/3307

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.

DOI: http://dx.doi.org/10.1093/cercor/bhq289

URL: http://www.ncbi.nlm.nih.gov/pubmed/21330469

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.
DOI: http://dx.doi.org/10.1038/nn.2533
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.
DOI: http://dx.doi.org/10.1023/B:MACH.0000035475.85309.1b
Mittner, M., Boekel, W., Tucker, A. M., Turner, B. M., Heathcote, A. & Forstmann, B. U. (2014). When the Brain Takes a Break: A Model-Based Analysis of Mind Wandering. Journal of Neuroscience, 34, 16286-16295.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1523/jneurosci.2062-14.2014

URL: http://dx.doi.org/10.1523/JNEUROSCI.2062-14.2014

Mur, M., Bandettini, P. A. & Kriegeskorte, N. (2009). Revealing representational content with pattern-information fMRI–an introductory guide. Social Cognitive and Affective Neuroscience.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.1093/scan/nsn044

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)

DOI: http://dx.doi.org/10.1002/hbm.1058

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.
DOI: http://dx.doi.org/10.1016/j.tics.2006.07.005
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.
DOI: http://dx.doi.org/10.1162/0898929053467550
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.
DOI: http://dx.doi.org/10.1162/jocn.2007.19.11.1735
Olivetti, E., Greiner, S. & Avesani, P. (2012). Induction in Neuroscience with Classification: Issues and Solutions. Machine Learning and Interpretation in Neuroimaging, 42-50.
DOI: http://dx.doi.org/10.1007/978-3-642-34713-9_6

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.

Oosterhof, N. N., Wiestler, T., Downing, P. E. & Diedrichsen, J. (2011). A comparison of volume-based and surface-based multi-voxel pattern analysis. NeuroImage, 56, 593-600.

Parkinson, C., Liu, S. & Wheatley, T. (2014). A Common Cortical Metric for Spatial, Temporal, and Social Distance. Journal of Neuroscience, 34, 1979-1987.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1523/jneurosci.2159-13.2014

URL: http://dx.doi.org/10.1523/JNEUROSCI.2159-13.2014

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. & Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.

Keywords: pymvpa-reference

URL: http://dl.acm.org/citation.cfm?id=1953048.2078195

Pereira, F. & Botvinick, M. (2011). Information mapping with pattern classifiers: a comparative study. Neuroimage, 56, 476-496.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.1016/j.neuroimage.2010.05.026

Pereira, F., Mitchell, T. & Botvinick, M. (2009). Machine learning classifiers and fMRI: A tutorial overview. NeuroImage, 45, 199–209.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.1016/j.neuroimage.2008.11.007

URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2892746/

Pernet, C. R., Sajda, P. & Rousselet, G. A. (2011). Single-trial analyses: why bother?. Front Psychol, 2, 322.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.3389/fpsyg.2011.00322

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.

DOI: http://dx.doi.org/10.1093/cercor/bhk020

Plitt, M., Savjani, R. R. & Eagleman, D. M. (2014). Are corporations people too? The neural correlates of moral judgments about companies and individuals. Social Neuroscience, 10, 113-125.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1080/17470919.2014.978026

URL: http://dx.doi.org/10.1080/17470919.2014.978026

Pollmann, S., Zinke, W., Baumgartner, F., Geringswald, F. & Hanke, M. (2014). The right temporo-parietal junction contributes to visual feature binding. NeuroImage, 101, 289-297.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1016/j.neuroimage.2014.07.021

URL: http://dx.doi.org/10.1016/j.neuroimage.2014.07.021

Raizada, R. D. & Connolly, A. C. (2012). What makes different people’s representations alike: neural similarity-space solves the problem of across-subject fMRI decoding. Journal of Cognitive Neuroscience, 24, 868-877.
URL: http://raizadalab.org/publications.html
Rueschemeyer, S., Ekman, M., van Ackeren, M. & Kilner, J. (2014). Observing, Performing, and Understanding Actions: Revisiting the Role of Cortical Motor Areas in Processing of Action Words. Journal of Cognitive Neuroscience.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1162/jocn_a_00576

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.

Keywords: support vector machine, SVM, sensitivity

DOI: http://dx.doi.org/10.1016/j.jneumeth.2008.04.008

Schlegel, A., Alexander, P., Fogelson, S. V., Li, X., Lu, Z., Kohler, P. J., Riley, E., Tse, P. U. & Meng, M. (2015). The artist emerges: Visual art learning alters neural structure and function. NeuroImage, 105, 440-451.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1016/j.neuroimage.2014.11.014

URL: http://dx.doi.org/10.1016/j.neuroimage.2014.11.014

Schlichting, M. L. & Preston, A. R. (2014). Memory reactivation during rest supports upcoming learning of related content. Proceedings of the National Academy of Sciences, 111, 15845-15850.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1073/pnas.1404396111

URL: http://dx.doi.org/10.1073/pnas.1404396111

Scholkopf, B. & Smola, A. (2001). Learning with Kernels: Support Vector Machines, Regularization. MIT Press: Cambridge, MA.

Good coverage of kernel methods and associated statistical learning aspects (e.g. error bounds)

Keywords: statistical learning, kernel methods, error estimation

Schrouff, J., Rosa, M. J., Rondina, J., Marquand, A., Chu, C., Ashburner, J., Phillips, C., Richiardi, J. & Mourão-Miranda, J. (2013). PRoNTo: Pattern Recognition for Neuroimaging Toolbox. Neuroinformatics, 1-19.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.1007/s12021-013-9178-1

Schönwiesner, M., Dechent, P., Voit, D., Petkov, C. I. & Krumbholz, K. (2014). Parcellation of Human and Monkey Core Auditory Cortex with fMRI Pattern Classification and Objective Detection of Tonotopic Gradient Reversals. Cerebral Cortex.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1093/cercor/bhu124

URL: http://dx.doi.org/10.1093/cercor/bhu124

Sha, L., Haxby, J. V., Abdi, H., Guntupalli, J. S., Oosterhof, N. N., Halchenko, Y. O. & Connolly, A. C. (2014). The Animacy Continuum in the Human Ventral Vision Pathway. Journal of Cognitive Neuroscience.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1162/jocn_a_00733

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.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.1038/nrn2994

URL: http://www.ncbi.nlm.nih.gov/pubmed/21331082

Shiffrin, R. (2010). Perspectives on Modeling in Cognitive Science. Topics in Cognitive Science, 2, 736–750.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.1111/j.1756-8765.2010.01092.x

Smith, D. V., Clithero, J. A., Rorden, C. & Karnath, H. (2013). Decoding the anatomical network of spatial attention. Proceedings of the National Academy of Sciences, 110, 1518-1523.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1073/pnas.1210126110

Sobhani, M., Fox, G. R., Kaplan, J. & Aziz-Zadeh, L. (2012). Interpersonal liking modulates motor-related neural regions. PloS one, 7, e46809.
DOI: http://dx.doi.org/10.1371/journal.pone.0046809
Spacek, M. & Swindale, N. (2009). Python in Neuroscience. The Neuromorphic Engineer.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.2417/1200907.1682

Stelzer, J., Chen, Y. & Turner, R. (2012). Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control. NeuroImage, 65, 69-82.

Keywords: pymvpa-reference

DOI: http://dx.doi.org/10.1016/j.neuroimage.2012.09.063

Strnad, L., Peelen, M. V., Bedny, M. & Caramazza, A. (2013). Multivoxel Pattern Analysis Reveals Auditory Motion Information in MT+ of Both Congenitally Blind and Sighted Individuals. PloS one, 8, e63198.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1371/journal.pone.0063198

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.

Keywords: pymvpa, psychosis, MRI

DOI: http://dx.doi.org/10.1016/j.biopsych.2009.07.019

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.

DOI: http://dx.doi.org/10.1109/IROS.2009.5354290

Van der Laan, L. N., De Ridder, D. T., Viergever, M. A. & Smeets, P. A. (2012). Appearance matters: neural correlates of food choice and packaging aesthetics. PloS one, 7, e41738.
DOI: http://dx.doi.org/10.1371/journal.pone.0041738
Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer: New York.
Keywords: support vector machine, SVM
Varma, S. & Simon, R. (2006). Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics, 7, 91.

Demonstration of overfitting and introducing the bias in the error estimation using cross-validation on entire dataset for performing model selection.

Keywords: statistical learning, model selection, error estimation, hypothesis testing

DOI: http://dx.doi.org/10.1186/1471-2105-7-91

URL: http://www.ncbi.nlm.nih.gov/pubmed/16504092

Vickery, T. J., Chun, M. M. & Lee, D. (2011). Ubiquity and Specificity of Reinforcement Signals throughout the Human Brain . Neuron *, *72, 166-177.

DOI: http://dx.doi.org/10.1016/j.neuron.2011.08.011

URL: http://www.sciencedirect.com/science/article/pii/S089662731100732X

Viswanathan, S., Cieslak, M. & Grafton, S. T. (2012). On the geometric structure of fMRI searchlight-based information maps. arXiv preprint arXiv:1210.6317.

Wang, Q., Luo, S., Monterosso, J., Zhang, J., Fang, X., Dong, Q. & Xue, G. (2014). Distributed Value Representation in the Medial Prefrontal Cortex during Intertemporal Choices. Journal of Neuroscience, 34, 7522-7530.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1523/jneurosci.0351-14.2014

URL: http://dx.doi.org/10.1523/JNEUROSCI.0351-14.2014

Wang, Z., Childress, A. R., Wang, J. & Detre, J. A. (2007). Support vector machine learning-based fMRI data group analysis. NeuroImage, 36, 1139–51.

Keywords: support vector machine, SVM, group analysis

DOI: http://dx.doi.org/10.1016/j.neuroimage.2007.03.072

Watson, D. M., Hartley, T. & Andrews, T. J. (2014). Patterns of response to visual scenes are linked to the low-level properties of the image. NeuroImage, 99, 402-410.

Keywords: pymvpa

DOI: http://dx.doi.org/10.1016/j.neuroimage.2014.05.045

URL: http://dx.doi.org/10.1016/j.neuroimage.2014.05.045

Woolgar, A., Thompson, R., Bor, D. & Duncan, J. (2010). Multi-voxel coding of stimuli, rules, and responses in human frontoparietal cortex. NeuroImage.

DOI: http://dx.doi.org/10.1016/j.neuroimage.2010.04.035

URL: http://www.ncbi.nlm.nih.gov/pubmed/20406690

Wright, D. (2009). Ten Statisticians and Their Impacts for Psychologists. Perspectives on Psychological Science, 4, 587–597.

Historical excurse into the life of 10 prominent statisticians of XXth century and their scientific contributions.

Keywords: statistics, hypothesis testing

DOI: http://dx.doi.org/10.1111/j.1745-6924.2009.01167.x

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.

Keywords: feature selection, statistical learning

URL: http://www-stat.stanford.edu/%7Ehastie/Papers/B67.2%20(2005)%20301-320%20Zou%20%26%20Hastie.pdf