Abstract
MultiVoxel Pattern Analysis (MVPA) is presented as a successful alternative to the General Linear Model (GLM) for fMRI data analysis. We report different experiments using MVPA to master several key parameters. We found that 1) different feature selections provide similar classification accuracies with different interpretation depending on the underlying hypotheses, 2) paradigms should be created to maximize both Signal to Noise Ratio (SNR) and number of examples and 3) smoothing leads to opposite effects on classification depending on the spatial scale at which information is encoded and should be used with extreme caution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kamitani, Y., Tong, F.: Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8(5), 679–685 (2005)
Gerardin, P., Kourtzi, Z., Mamassian, P.: Prior knowledge of illumination for 3D perception in the human brain. Proc. Natl. Acad. Sci. U.S.A. 107(37), 16309–16314 (2010)
Reddy, L., Tsuchiya, N., Serre, T.: Reading the mind’s eye: decoding category information during mental imagery. Neuroimage 50(2), 818–825 (2010)
Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Schouten, J.L., Pietrini, P.: Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293(5539), 2425–2430 (2001)
Ethofer, T., Van De Ville, D., Scherer, K., Vuilleumier, P.: Decoding of emotional information in voice-sensitive cortices. Curr. Biol. 19(12), 1028–1033 (2009)
Brouwer, G.J., Heeger, D.J.: Decoding and reconstructing color from responses in human visual cortex. J. Neurosci. 29(44), 13992–14003 (2009)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1-3), 389–422 (2002)
De Martino, F., Valente, G., Staeren, N., Ashburner, J., Goebel, R., Formisano, E.: Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. Neuroimage 43(1), 44–58 (2008)
Norman, K.A., Polyn, S.M., Detre, G.J., Haxby, J.V.: Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10(9), 424–430 (2006)
Mumford, J.A., Turner, B.O., Ashby, F.G., Poldrack, R.A.: Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses. Neuroimage 59(3), 2636–2643 (2012)
Coutanche, M.N., Thompson-Schill, S.L.: The advantage of brief fMRI acquisition runs for multi-voxel pattern detection across runs. Neuroimage 61(4), 1113–1119 (2012)
LaConte, S., Strother, S., Cherkassky, V., Anderson, J., Hu, X.: Support vector machines for temporal classification of block design fMRI data. Neuroimage 26(2), 317–329 (2005)
Etzel, J.A., Valchev, N., Keysers, C.: The impact of certain methodological choices on multivariate analysis of fMRI data with support vector machines. Neuroimage 54(2), 1159–1167 (2011)
Freeman, J., Brouwer, G.J., Heeger, D.J., Merriam, E.P.: Orientation decoding depends on maps, not columns. J. Neurosci. 31(13), 4792–4804 (2011)
Kriegeskorte, N., Cusack, R., Bandettini, P.: How does an fMRI voxel sample the neuronal activity pattern: compact-kernel or complex spatiotemporal filter? Neuroimage 49(3), 1965–1976 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ruiz, M.J., Hupé, JM., Dojat, M. (2012). Use of Pattern-Information Analysis in Vision Science: A Pragmatic Examination. In: Wang, F., Shen, D., Yan, P., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2012. Lecture Notes in Computer Science, vol 7588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35428-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-35428-1_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35427-4
Online ISBN: 978-3-642-35428-1
eBook Packages: Computer ScienceComputer Science (R0)