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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.

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 stability

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

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

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

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., Halchenko, Y. O., Haxby, J. V. & Pollmann, S. (accepted). Statistical learning analysis in neuroscience: aiming for transparency. Frontiers in Neuroscience.

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. & 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, recursive feature elimination, RFE

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

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

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

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

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, similarity

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

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

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.

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

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

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, MRI

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

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

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

Pereira, F., Mitchell, T. & Botvinick, M. (in press). Machine learning classifiers and fMRI: A tutorial overview. Neuroimage.

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

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

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

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.

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

Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer: New York.

Keywords: support vector machine, SVM

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

Zou, H. & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B, 67, 301–320.

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