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

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


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.

MeSH terms

  • Brain / anatomy & histology*
  • Brain / physiology*
  • Brain Mapping / methods*
  • Cognition*
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Magnetic Resonance Imaging / methods
  • Models, Neurological