publication . Article . 2016

CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave

Oosterhof, Nikolaas N.; Connolly, Andrew C.; Haxby, James V.;
Open Access English
  • Published: 01 Jul 2016 Journal: Frontiers in Neuroinformatics, volume 10 (issn: 1662-5196, eissn: 1662-5196, Copyright policy)
  • Publisher: Frontiers Media S.A.
Abstract
Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hy...
Subjects
free text keywords: electroencephalography, open source, software, Neuroscience, Methods, cognitive neuroscience, magnetoencephalography, multi-variate pattern analysis, functional magnetic resonance imaging
63 references, page 1 of 5

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Chan, A. M., Halgren, E., Marinkovic, K., & Cash, S. S. (2011). Decoding word and category-specific spatiotemporal representations from MEG and EEG. NeuroImage, 54(4), 3028-3039.

Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2, 27:1-27:27.

Chen, Y., Namburi, P., Elliott, L. T., Heinzle, J., Soon, C. S., Chee, M. W., & Haynes, J. D. (2011). Cortical surfacebased searchlight decoding. NeuroImage, 56, 582--592.

Chumbley, J. R., & Friston, K. J. (2009). False discovery rate revisited: FDR and topological inference using Gaussian random fields. NeuroImage, 44(1), 62-70.

Cichy, R. M., Pantazis, D., & Oliva, A. (2014). Resolving human object recognition in space and time. Nature neuroscience, 17(3), 455-462. [OpenAIRE]

Clithero, J. A., Smith, D. V., Carter, R. M., & Huettel, S. A. (2011). Within- and cross-participant classifiers reveal different neural coding of information. NeuroImage, 56(2), 699-708.

Connolly, A. C., Guntupalli, J. S., Gors, J., Hanke, M., Halchenko, Y. O., Wu, Y. C., . . . Haxby, J. V. (2012). The Representation of Biological Classes in the Human Brain. Journal of Neuroscience, 32(8), 2608-2618. [OpenAIRE]

Cox, D. D., & Savoy, R. L. (2003). 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.

Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and biomedical research, an international journal, 29(3), 162-173.

Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9-21. [OpenAIRE]

Detre, G. J., Polyn, S. M., Moore, C. D., Natu, V. S., Singer, B. D., Cohen, J. D., . . . Norman, K. A. (2006). The multi-voxel pattern analysis (mvpa) toolbox. In Poster presented at the annual meeting of the organization for human brain mapping (florence, italy). available at: http://www.csbmb.princeton.edu/mvpa.

Eddins, S. (2013). MATLAB xUnit Test Framework. http://www.mathworks.it/matlabcentral/fileexchange/22846- matlab-xunit-test-framework.

Edelman, S., Grill-Spector, K., Kushnir, T., & Malach, R. (1998). Toward direct visualization of the internal shape representation space by fMRI. Psychobiology, 26(4), 309-321.

63 references, page 1 of 5
Abstract
Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hy...
Subjects
free text keywords: electroencephalography, open source, software, Neuroscience, Methods, cognitive neuroscience, magnetoencephalography, multi-variate pattern analysis, functional magnetic resonance imaging
63 references, page 1 of 5

Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10, 433-436.

Brandl, B., Ronacher, A., Shimizukawa, T., Neuhäuser, D., Waltman, J., Ruana, R., . . . et al. (2008). Sphinx. https://github.com/sphinx-doc/sphinx.

Chan, A. M., Halgren, E., Marinkovic, K., & Cash, S. S. (2011). Decoding word and category-specific spatiotemporal representations from MEG and EEG. NeuroImage, 54(4), 3028-3039.

Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2, 27:1-27:27.

Chen, Y., Namburi, P., Elliott, L. T., Heinzle, J., Soon, C. S., Chee, M. W., & Haynes, J. D. (2011). Cortical surfacebased searchlight decoding. NeuroImage, 56, 582--592.

Chumbley, J. R., & Friston, K. J. (2009). False discovery rate revisited: FDR and topological inference using Gaussian random fields. NeuroImage, 44(1), 62-70.

Cichy, R. M., Pantazis, D., & Oliva, A. (2014). Resolving human object recognition in space and time. Nature neuroscience, 17(3), 455-462. [OpenAIRE]

Clithero, J. A., Smith, D. V., Carter, R. M., & Huettel, S. A. (2011). Within- and cross-participant classifiers reveal different neural coding of information. NeuroImage, 56(2), 699-708.

Connolly, A. C., Guntupalli, J. S., Gors, J., Hanke, M., Halchenko, Y. O., Wu, Y. C., . . . Haxby, J. V. (2012). The Representation of Biological Classes in the Human Brain. Journal of Neuroscience, 32(8), 2608-2618. [OpenAIRE]

Cox, D. D., & Savoy, R. L. (2003). 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.

Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and biomedical research, an international journal, 29(3), 162-173.

Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9-21. [OpenAIRE]

Detre, G. J., Polyn, S. M., Moore, C. D., Natu, V. S., Singer, B. D., Cohen, J. D., . . . Norman, K. A. (2006). The multi-voxel pattern analysis (mvpa) toolbox. In Poster presented at the annual meeting of the organization for human brain mapping (florence, italy). available at: http://www.csbmb.princeton.edu/mvpa.

Eddins, S. (2013). MATLAB xUnit Test Framework. http://www.mathworks.it/matlabcentral/fileexchange/22846- matlab-xunit-test-framework.

Edelman, S., Grill-Spector, K., Kushnir, T., & Malach, R. (1998). Toward direct visualization of the internal shape representation space by fMRI. Psychobiology, 26(4), 309-321.

63 references, page 1 of 5
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publication . Article . 2016

CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave

Oosterhof, Nikolaas N.; Connolly, Andrew C.; Haxby, James V.;