The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Revealing representational content with pattern-information fMRI—an introductory guide
Open accessAbstract
Conventional statistical analysis methods for functional magnetic resonance imaging (fMRI) data are very successful at detecting brain regions that are activated as a whole during specific mental activities. The overall activation of a region is usually taken to indicate involvement of the region in the task. However, such activation analysis does not consider the multivoxel patterns of activity within a brain region. These patterns of activity, which are thought to reflect neuronal population codes, can be investigated by pattern-information analysis. In this framework, a region's multivariate pattern information is taken to indicate representational content. This tutorial introduction motivates pattern-information analysis, explains its underlying assumptions, introduces the most widespread methods in an intuitive way, and outlines the basic sequence of analysis steps.
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Cites
Statistical parametric maps in functional imaging: A general linear approach
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. ‐., Frith, C. D., & Frackowiak, R. S. J. (1994) · Human Brain Mapping · doi:10.1002/hbm.460020402 · Cited by 9,688
Neurophysiological investigation of the basis of the fMRI signal
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001) · Nature · doi:10.1038/35084005 · Cited by 6,551
What we can do and what we cannot do with fMRI
Logothetis, N. K. (2008) · Nature · doi:10.1038/nature06976 · Cited by 3,612
Beyond mind-reading: multi-voxel pattern analysis of fMRI data
Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.07.005 · Cited by 2,271
Decoding the visual and subjective contents of the human brain
Kamitani, Y., & Tong, F. (2005) · Nature Neuroscience · doi:10.1038/nn1444 · Cited by 1,829
Machine learning classifiers and fMRI: A tutorial overview
Pereira, F., Mitchell, T., & Botvinick, M. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.11.007 · Cited by 1,624
Predicting Human Brain Activity Associated with the Meanings of Nouns
Mitchell, T. M., Shinkareva, S. V., Carlson, A., Chang, K.-M., Malave, V. L., Mason, R. A., & Just, M. A. (2008) · Science · doi:10.1126/science.1152876 · Cited by 1,180
Category-Specific Cortical Activity Precedes Retrieval During Memory Search
Polyn, S. M., Natu, V. S., Cohen, J. D., & Norman, K. A. (2005) · Science · doi:10.1126/science.1117645 · Cited by 690
Cited by
Machine learning for neuroimaging with scikit-learn
Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., & Varoquaux, G. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00014 · Cited by 2,384
A Toolbox for Representational Similarity Analysis
Nili, H., Wingfield, C., Walther, A., Su, L., Marslen-Wilson, W., & Kriegeskorte, N. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003553 · Cited by 937
Self-citationRepresentational geometry: integrating cognition, computation, and the brain
Kriegeskorte, N., & Kievit, R. A. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.06.007 · Cited by 911
Self-citationComparison of multivariate classifiers and response normalizations for pattern-information fMRI
Misaki, M., Kim, Y., Bandettini, P. A., & Kriegeskorte, N. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.051 · Cited by 458
Self-citationA Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives
Haynes, J.-D. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.025 · Cited by 402
Metadata
- Authors
- Mur, M., Bandettini, P. A., & Kriegeskorte, N.
- Venue
- Social Cognitive and Affective Neuroscience
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-01-17
- Volume
- 4
- Issue
- 1
- Pages
- 101-109
- ISSN
- 1749-5016 (ISSN-L)
- 1749-5024
- OpenAlex
- W2145217719
- Cited by
- 414
- References
- 8
- FWCI
- 11.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
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