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PLoS Comput Biol. 2017 Oct 23;13(10):e1005649. doi: 10.1371/journal.pcbi.1005649. eCollection 2017 Oct.

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

Author information

Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States of America.
SurveyMonkey, San Mateo, CA, United States of America.
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America.
Department of Psychology, Stanford University, Stanford, CA, United States of America.
Department of Psychology, University of Texas at Austin, Austin, TX, United States of America.


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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