References
This list aims to be a collection of literature, that is of particular interest
in the context of multivarite 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., 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. (in press). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics.
- Introduction into the analysis of fMRI data using PyMVPA.
- 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., Matsuka, T. & Haxby, J. (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., Gobbini, M., Furey, M., Ishai, A., Schouten, J. & 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
- 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
- Kriegeskorte, N., Goebel, R. & Bandettini, P. (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
- 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
- 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
- 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
- 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
- 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