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

A central goal of cognitive neuroscience is to decode human brain activity—that is, 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 utilit...

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Autores principales: Rubin, Timothy N., Koyejo, Oluwasanmi, Gorgolewski, Krzysztof J., Jones, Michael N., Poldrack, Russell A., Yarkoni, Tal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683652/
https://www.ncbi.nlm.nih.gov/pubmed/29059185
http://dx.doi.org/10.1371/journal.pcbi.1005649
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author Rubin, Timothy N.
Koyejo, Oluwasanmi
Gorgolewski, Krzysztof J.
Jones, Michael N.
Poldrack, Russell A.
Yarkoni, Tal
author_facet Rubin, Timothy N.
Koyejo, Oluwasanmi
Gorgolewski, Krzysztof J.
Jones, Michael N.
Poldrack, Russell A.
Yarkoni, Tal
author_sort Rubin, Timothy N.
collection PubMed
description A central goal of cognitive neuroscience is to decode human brain activity—that is, 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—that is, 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 probabilistic 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 generate quantitative, context-sensitive interpretations of whole-brain patterns of brain activity.
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spelling pubmed-56836522017-11-30 Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition Rubin, Timothy N. Koyejo, Oluwasanmi Gorgolewski, Krzysztof J. Jones, Michael N. Poldrack, Russell A. Yarkoni, Tal PLoS Comput Biol Research Article A central goal of cognitive neuroscience is to decode human brain activity—that is, 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—that is, 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 probabilistic 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 generate quantitative, context-sensitive interpretations of whole-brain patterns of brain activity. Public Library of Science 2017-10-23 /pmc/articles/PMC5683652/ /pubmed/29059185 http://dx.doi.org/10.1371/journal.pcbi.1005649 Text en © 2017 Rubin et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Rubin, Timothy N.
Koyejo, Oluwasanmi
Gorgolewski, Krzysztof J.
Jones, Michael N.
Poldrack, Russell A.
Yarkoni, Tal
Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition
title Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition
title_full Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition
title_fullStr Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition
title_full_unstemmed Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition
title_short Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition
title_sort decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683652/
https://www.ncbi.nlm.nih.gov/pubmed/29059185
http://dx.doi.org/10.1371/journal.pcbi.1005649
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