<?xml version="1.0" encoding="utf-8"?><!DOCTYPE article  PUBLIC '-//OASIS//DTD DocBook XML V4.4//EN'  'http://www.docbook.org/xml/4.4/docbookx.dtd'><article><articleinfo><title>MvpaPapersOfInterest</title><revhistory><revision><revnumber>4</revnumber><date>2013-03-07 21:24:01</date><authorinitials>localhost</authorinitials><revremark>converted to 1.6 markup</revremark></revision><revision><revnumber>3</revnumber><date>2009-10-20 15:01:45</date><authorinitials>JohanCarlin</authorinitials></revision><revision><revnumber>2</revnumber><date>2009-10-20 15:01:26</date><authorinitials>JohanCarlin</authorinitials></revision><revision><revnumber>1</revnumber><date>2009-10-20 14:53:05</date><authorinitials>JohanCarlin</authorinitials></revision></revhistory></articleinfo><section><title>MVPA Papers of Interest</title><itemizedlist><listitem><para>Guyon, I., &amp; Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157-1182. </para></listitem><listitem><para>Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., FrÃ¼nd, I., Rieger, J. W., et al. (2009). PyMVPA: a unifying approach to the analysis of neuroscientific data. Front. Neuroinform., 3. Retrieved from <ulink url="http://www.frontiersin.org/neuroinformatics/paper/10.3389/neuro.11/003.2009/"/> </para></listitem><listitem><para>Haynes, J. D., &amp; Rees, G. (2006). Decoding mental states from brain activity in humans. Nat Rev Neurosci, 7, 523. </para></listitem><listitem><para>Kamitani, Y., &amp; Tong, F. (2006). Decoding Seen and Attended Motion Directions from Activity in the Human Visual Cortex. Curr Biol, 16, 1096-1102. </para></listitem><listitem><para>Kamitani, Y., &amp; Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nat Neurosci, 8, 679-685. </para></listitem><listitem><para>Kay, K. N., Naselaris, T., Prenger, R. J., &amp; Gallant, J. L. (2008). Identifying natural images from human brain activity. Nature, 452, 352-355. </para></listitem><listitem><para>Kriegeskorte, N., &amp; Bandettini, P. (2007). Analyzing for information, not activation, to exploit high-resolution fMRI. <ulink url="https://imaging.mrc-cbu.cam.ac.uk/imaging/MvpaPapersOfInterest/imaging/NeuroImage#">NeuroImage</ulink>, 38, 649-662. </para></listitem><listitem><para>Kriegeskorte, N., Goebel, R., &amp; Bandettini, P. (2006). Information-based functional brain mapping. PNAS, 103, 3863-3868. </para></listitem><listitem><para>Kriegeskorte, N., Mur, M., &amp; Bandettini, P. (2008). Representational similarity analysis - connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2, 1-28. </para></listitem><listitem><para>Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F., &amp; Baker, C. I. (2009). Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci, 12, 535. </para></listitem><listitem><para>Mitchell, T. M., Hutchinson, R., Niculescu, R. S., Pereira, F., &amp; Wang, X. (2004). Learning to Decode Cognitive States from Brain Images. Machine Learning, 57, 145-175. </para></listitem><listitem><para>Pereira, F., Mitchell, T., &amp; Botvinick, M. (2009). Machine learning classifiers and fMRI: A tutorial overview. <ulink url="https://imaging.mrc-cbu.cam.ac.uk/imaging/MvpaPapersOfInterest/imaging/NeuroImage#">NeuroImage</ulink>, 45, S199--S209. </para></listitem><listitem><para>Kriegeskorte, N., Cusack, R., Bandettini, P. (2009) &quot;How does an fMRI voxel sample the neuronal activity pattern: Compact-kernel or complex-spatiotemporal filter?&quot; <ulink url="https://imaging.mrc-cbu.cam.ac.uk/imaging/MvpaPapersOfInterest/imaging/NeuroImage#">NeuroImage</ulink>, in press. </para></listitem></itemizedlist><para>(<ulink url="https://imaging.mrc-cbu.cam.ac.uk/imaging/MvpaPapersOfInterest/imaging/JohanCarlin#">JohanCarlin</ulink>) </para></section></article>