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

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,

SUBMITTER: Rubin TN 

PROVIDER: S-EPMC5683652 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature