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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-10-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1005649 |
| _version_ | 1818579230718427136 |
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| author | Timothy N Rubin Oluwasanmi Koyejo Krzysztof J Gorgolewski Michael N Jones Russell A Poldrack Tal Yarkoni |
| author_facet | Timothy N Rubin Oluwasanmi Koyejo Krzysztof J Gorgolewski Michael N Jones Russell A Poldrack Tal Yarkoni |
| author_sort | Timothy N Rubin |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-16T06:58:24Z |
| format | Article |
| id | doaj.art-ac4fc189672844c7a1a2d4fe4e4d55fd |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-16T06:58:24Z |
| publishDate | 2017-10-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-ac4fc189672844c7a1a2d4fe4e4d55fd2022-12-21T22:40:14ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-10-011310e100564910.1371/journal.pcbi.1005649Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition.Timothy N RubinOluwasanmi KoyejoKrzysztof J GorgolewskiMichael N JonesRussell A PoldrackTal YarkoniA 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.https://doi.org/10.1371/journal.pcbi.1005649 |
| spellingShingle | Timothy N Rubin Oluwasanmi Koyejo Krzysztof J Gorgolewski Michael N Jones Russell A Poldrack Tal Yarkoni Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition. PLoS Computational Biology |
| 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 |
| url | https://doi.org/10.1371/journal.pcbi.1005649 |
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