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.

Bandettini, P. A. (2009). Seven topics in functional magnetic resonance imaging. Journal of Integrative Neuroscience, 8, 371–403.
URL: http://www.ncbi.nlm.nih.gov/pubmed/19938211
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

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

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
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
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
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

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/

Garcia, S. & Fourcaud-Trocmé, N. (2009). OpenElectrophy: An Electrophysiological Data- and Analysis-Sharing Framework. Front Neuroinformatics, 3, 14.
DOI: http://dx.doi.org/10.3389/neuro.11.014.2009
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
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. (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., 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
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/ tibs/ElemStatLearn/

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

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

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

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
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

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

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

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

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

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

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.

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

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.
DOI: http://dx.doi.org/10.1016/j.neuroimage.2008.11.007
Pernet, C. R., Sajda, P. & Rousselet, G. A. (2011). Single-trial analyses: why bother?. Front Psychol, 2, 322.
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

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.
URL: http://raizadalab.org/publications.html
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

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

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.
DOI: http://dx.doi.org/10.1038/nrn2994
Shiffrin, R. (2010). Perspectives on Modeling in Cognitive Science. Topics in Cognitive Science, 2, 736–750.
DOI: http://dx.doi.org/10.1111/j.1756-8765.2010.01092.x
Spacek, M. & Swindale, N. (2009). Python in Neuroscience. The Neuromorphic Engineer.
DOI: http://dx.doi.org/10.2417/1200907.1682
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

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

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

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
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

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