References

[CGG+12]Connolly, A. C., Guntupalli, J. S., Gors, J., Hanke, M., Halchenko, Y. O., Wu, Y. C., Abdi, H., and Haxby, J. V. The Representation of Biological Classes in the Human Brain. Journal of Neuroscience, 32(8):2608–2618, February 2012. [ Representational similiarity analysis applied to fMRI data of participants viewing pictures of six animals ]
[CS03]Cox, D. D. and Savoy, R. L. Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage, 19(2 Pt 1):261–270, 2003. [ First illustration of classification of fMRI data ]
[DM04]Delorme, A. and Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1):9–21, March 2004.
[EGSKM98]Edelman, S., Grill-Spector, K., Kushnir, T., and Malach, R. Toward direct visualization of the internal shape representation space by fMRI. Psychobiology, 26(4):309–321, 1998. [ Classic MVPA paper, applying representational similarity analysis ]
[GEF06]Goebel, R., Esposito, F., and Formisano, E. Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: From single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis. Human brain mapping, 27(5):392–401, May 2006.
[HHS+09a]Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V., and Pollmann, S. PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7(1):37–53, 2009. doi:10.1007/s12021-008-9041-y. [ Announcement of PyMVPA (www.pymvpa.org) ]
[HHS+09b]Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Fründ, I., Rieger, J. W., Herrmann, C. S., Haxby, J. V., Hanson, S. J., and Pollmann, S. PyMVPA: A Unifying Approach to the Analysis of Neuroscientific Data. Frontiers in neuroinformatics, 3:3, 2009. [ PyMVPA applied to analysis of fMRI, MEEG and electrophysiological data ]
[HGF+01]Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., and Pietrini, P. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293(5539):2425–2430, September 2001. [ Classic MVPA paper, showing evidence for distributed representations of difference classes in the human brain ]
[KOP16]Kaiser, D., Oosterhof, N. N., and Peelen, M. V. The neural dynamics of attentional selection in natural scenes. Journal of neuroscience, 36(41):10522–10528, 2016.
[KD14]King, J.-R. and Dehaene, S. Characterizing the dynamics of mental representations: the temporal generalization method. Trends in cognitive sciences, 2014.
[KGB06]Kriegeskorte, N., Goebel, R., and Bandettini, P. Information-based functional brain mapping. Proceedings of the National Academy of Sciences of the United States of America, 103(10):3863–3868, March 2006. [ Paper introducing the ‘searchlight’ concept applied to fMRI volumetric data ]
[KMR+08]Kriegeskorte, N., Mur, M., Ruff, D. A., Kiani, R., Bodurka, J., Esteky, H., Tanaka, K., and Bandettini, P. A. Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron, 60(6):1126–1141, December 2008. [ Application of representational similarity analysis to human fMRI and monkey neurophysiological data ]
[LTO+14]Leske, S., Tse, A., Oosterhof, N. N., Hartmann, T., Müller, N., Keil, J., and Weisz, N. The strength of alpha and beta oscillations parametrically scale with the strength of an illusory auditory percept. NeuroImage, 88:69–78, March 2014. [ Illustration of MEEG time-frequency-channel searchlight ]
[MBK09]Mur, M., Bandettini, P. A., and Kriegeskorte, N. Revealing representational content with pattern-information fMRI–an introductory guide. Social cognitive and affective neuroscience, 4(1):101–109, 2009.
[NPDH06]Norman, K. A., Polyn, S. M., Detre, G. J., and Haxby, J. V. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in cognitive sciences, 10(9):424–430, 2006. doi:10.1016/j.tics.2006.07.005.
[OFMS11]Oostenveld, R., Fries, P., Maris, E., and Schoffelen, J.-M. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Computational Intelligence and Neuroscience, 2011:156869, 2011.
[OCH16]Oosterhof, N. N., Connolly, A. C., and Haxby, J. V. CoSMoMVPA: multi-modal multivariate pattern analysis of neuroimaging data in Matlab / GNU Octave. Frontiers in Neuroinformatics, 2016. doi:10.3389/fninf.2016.00027.
[OTD12]Oosterhof, N. N., Tipper, S. P., and Downing, P. E. Visuo-motor imagery of specific manual actions: A multi-variate pattern analysis fMRI study. NeuroImage, 63(1):262–271, October 2012. [ Illustration of how a-symmetric decoding accuracy may arise ]
[OWDD11]Oosterhof, N. N., Wiestler, T., Downing, P. E., and Diedrichsen, J. A comparison of volume-based and surface-based multi-voxel pattern analysis. NeuroImage, 56(2):593–600, May 2011. [ ‘searchlight’ concept applied to fMRI surface-based data ]
[OWD+10]Oosterhof, N. N., Wiggett, A. J., Diedrichsen, J., tipper, S. P., and Downing, P. E. Surface-based information mapping reveals crossmodal vision-action representations in human parietal and occipitotemporal cortex. Journal of neurophysiology, 104(2):1077–1089, August 2010. [ Illustration of cross-decoding ]
[PWD06]Peelen, M. V., Wiggett, A. J., and Downing, P. E. Patterns of fMRI activity dissociate overlapping functional brain areas that respond to biological motion. Neuron, 49(6):815–822, 2006. doi:doi:10.1016/j.neuron.2006.02.004.
[PMB09]Pereira, F., Mitchell, T., and Botvinick, M. Machine learning classifiers and fMRI: A tutorial overview. NeuroImage, 45(1):S199–S209, 2009. doi:10.1016/j.neuroimage.2008.11.007.
[SN09]Smith, S. M. and Nichols, T. E. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage, 44(1):83–98, 2009.
[SCT13]Stelzer, J., Chen, Y., and Turner, R. Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control. NeuroImage, 65(C):69–82, January 2013.