%0 Journal Article %1 rubin2017decoding %A Rubin, Timothy N. %A Koyejo, Oluwasanmi %A Gorgolewski, Krzysztof J. %A Jones, Michael N. %A Poldrack, Russell A. %A Yarkoni, Tal %D 2017 %I Public Library of Science %J PLOS Computational Biology %K whole-brain bayesian decoding lda %N 10 %P e1005649+ %R 10.1371/journal.pcbi.1005649 %T Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition %U http://dx.doi.org/10.1371/journal.pcbi.1005649 %V 13 %X 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. A central goal of cognitive neuroscience is to decode human brain activity—i.e., to be able to infer mental processes from observed patterns of whole-brain activity. However, existing approaches to brain decoding suffer from a number of important limitations—for example, they often work only in one narrow domain of cognition, and cannot be easily generalized to novel contexts. Here we address such limitations by introducing a simple probabilistic framework based on a novel topic modeling approach. We use our approach to extract a set of highly interpretable latent ” topics” from a large meta-analytic database of over 11,000 published fMRI studies. Each topic is associated with a single brain region and a set of semantically coherent cognitive functions. We demonstrate how these topics can be used to automatically ” decode” brain activity in an open-ended way, enabling researchers to draw tentative conclusions about mental function on the basis of virtually any pattern of whole-brain activity. We highlight several important features of our framework, notably including the ability to take into account knowledge of the experimental context and/or prior experimenter belief.

@article{rubin2017decoding, abstract = {{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. A central goal of cognitive neuroscience is to decode human brain activity—i.e., to be able to infer mental processes from observed patterns of whole-brain activity. However, existing approaches to brain decoding suffer from a number of important limitations—for example, they often work only in one narrow domain of cognition, and cannot be easily generalized to novel contexts. Here we address such limitations by introducing a simple probabilistic framework based on a novel topic modeling approach. We use our approach to extract a set of highly interpretable latent ” topics” from a large meta-analytic database of over 11,000 published fMRI studies. Each topic is associated with a single brain region and a set of semantically coherent cognitive functions. We demonstrate how these topics can be used to automatically ” decode” brain activity in an open-ended way, enabling researchers to draw tentative conclusions about mental function on the basis of virtually any pattern of whole-brain activity. We highlight several important features of our framework, notably including the ability to take into account knowledge of the experimental context and/or prior experimenter belief.}}, added-at = {2018-12-07T09:10:16.000+0100}, author = {Rubin, Timothy N. and Koyejo, Oluwasanmi and Gorgolewski, Krzysztof J. and Jones, Michael N. and Poldrack, Russell A. and Yarkoni, Tal}, biburl = {https://www.bibsonomy.org/bibtex/2f6c9c2bfb180d31ae1d0b3538a9415d8/jpvaldes}, citeulike-article-id = {14468694}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1005649}, day = 23, doi = {10.1371/journal.pcbi.1005649}, interhash = {a9c47f700ca909943ae28fb478761224}, intrahash = {f6c9c2bfb180d31ae1d0b3538a9415d8}, journal = {PLOS Computational Biology}, keywords = {whole-brain bayesian decoding lda}, month = oct, number = 10, pages = {e1005649+}, posted-at = {2017-10-30 07:05:01}, priority = {2}, publisher = {Public Library of Science}, timestamp = {2018-12-07T09:35:36.000+0100}, title = {{Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition}}, url = {http://dx.doi.org/10.1371/journal.pcbi.1005649}, volume = 13, year = 2017 }