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- W2951570501 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." @default.
- W2951570501 created "2019-06-27" @default.
- W2951570501 creator A5015349126 @default.
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- W2951570501 creator A5090952017 @default.
- W2951570501 date "2017-10-23" @default.
- W2951570501 modified "2023-10-16" @default.
- W2951570501 title "Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition" @default.
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- W2951570501 doi "https://doi.org/10.1371/journal.pcbi.1005649" @default.
- W2951570501 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5683652" @default.
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