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

• Published in: PLoS computational biology Vol. 13; no. 10; p. e1005649
• Main Authors: Rubin, Timothy N., Koyejo, Oluwasanmi, Gorgolewski, Krzysztof J., Jones, Michael N., Poldrack, Russell A., Yarkoni, Tal
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.10.2017; Public Library of Science (PLoS)
• Subjects: Bayesian analysis; Biology and Life Sciences; Brain; Brain mapping; Brain research; Brain waves; Cognition; Cognition & reasoning; Cognitive ability; Data analysis; Decoding; Dirichlet problem; Functional anatomy; Functional magnetic resonance imaging; Medical imaging; Medicine and Health Sciences; Nervous system; Neurosciences; Physical Sciences; Psychology; Research and Analysis Methods; Researchers; Social Sciences; Studies; United States > US
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1005649

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.