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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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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. |
format | Online Article Text |
id | pubmed-5683652 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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