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.. index:: references

.. _references:

**********
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.


.. _CPL+06:

**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: :keyword:`feature selection stability`

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


.. _Dem06:

**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


.. _EHJ+04:

**Efron, B., Trevor, H., Johnstone, I. & Tibshirani, R.** (2004). Least Angle Regression. *Annals of Statistics*, *32*, 407–499.
  Keywords: :keyword:`least angle regression`, :keyword:`LARS`

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


.. _GE03:

**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


.. _HHS+latest:

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

.. _HHS+IP:

**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.*


.. _HH08:

**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: :keyword:`support vector machine`, :keyword:`SVM`, :keyword:`recursive feature elimination`, :keyword:`RFE`

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


.. _HMH04:

**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


.. _HGF+01:

**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: :keyword:`split-correlation classifier`

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


.. _HR06:

**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


.. _KT05:

**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


.. _KGB06:

**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: :keyword:`searchlight`

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


.. _KCF+05:

**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: :keyword:`sparse multinomial logistic regression`, :keyword:`SMLR`

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


.. _LSC+05:

**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: :keyword:`SVM`

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


.. _MHN+04:

**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


.. _NH02:

**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


.. _NPD+06:

**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


.. _OJA+05:

**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


.. _OJA+07:

**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


.. _PP07:

**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


.. _SMM+08:

**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: :keyword:`support vector machine`, :keyword:`SVM`, :keyword:`sensitivity`

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


.. _Vap95:

**Vapnik, V.** (1995). The Nature of Statistical Learning Theory. Springer: New York.
  Keywords: :keyword:`support vector machine`, :keyword:`SVM`


.. _WCW+07:

**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: :keyword:`support vector machine`, :keyword:`SVM`, :keyword:`group analysis`

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




