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Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition

Timothy N. Rubin, Oluwasanmi Koyejo, Krzysztof J. Gorgolewski, Michael N. Jones, Russell A. Poldrack, Tal Yarkoni
doi: https://doi.org/10.1101/059618
Timothy N. Rubin
1Department of Psychological and Brain Sciences, Indiana University Bloomington
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Oluwasanmi Koyejo
2Department of Psychology, Stanford University
3Department of Computer Science, University of Illinois at UrbanaChampaign
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Krzysztof J. Gorgolewski
2Department of Psychology, Stanford University
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Michael N. Jones
1Department of Psychological and Brain Sciences, Indiana University Bloomington
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Russell A. Poldrack
2Department of Psychology, Stanford University
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Tal Yarkoni
4Department of Psychology, University of Texas at Austin
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  • For correspondence: tyarkoni@utexas.edu
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Abstract

A central goal of cognitive neuroscience is to decode human brain activity--i.e., to infer mental processes from observed patterns of whole-brain activation. Previous decoding efforts have focused on classifying brain activity into a small set of discrete cognitive states. To attain maximal utility, a decoding framework must be open-ended, systematic, and context-sensitive--i.e., capable of interpreting numerous brain states, presented in arbitrary combinations, in light of prior information. Here we take steps towards this objective by introducing a Bayesian decoding framework based on a novel topic model---Generalized Correspondence Latent Dirichlet Allocation---that learns latent topics from a database of over 11,000 published fMRI studies. The model produces highly interpretable, spatially-circumscribed topics that enable flexible decoding of whole-brain images. Importantly, the Bayesian nature of the model allows one to “seed” decoder priors with arbitrary images and text--enabling researchers, for the first time, to generative quantitative, context-sensitive interpretations of whole-brain patterns of brain activity.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted June 18, 2016.
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Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition
Timothy N. Rubin, Oluwasanmi Koyejo, Krzysztof J. Gorgolewski, Michael N. Jones, Russell A. Poldrack, Tal Yarkoni
bioRxiv 059618; doi: https://doi.org/10.1101/059618
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Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition
Timothy N. Rubin, Oluwasanmi Koyejo, Krzysztof J. Gorgolewski, Michael N. Jones, Russell A. Poldrack, Tal Yarkoni
bioRxiv 059618; doi: https://doi.org/10.1101/059618

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