The representation of object categories is a classical question in cognitive neuroscience and compelling evidence has identified specific brain regions showing preferential activation to categories of evolutionary significance. However, the potential contributions to category processing by tuning the connectivity patterns are largely unknown. Adopting a continuous multicategory paradigm, we obtained whole-brain functional connectivity (FC) patterns of each of four categories (faces, scenes, animals and tools) in healthy human adults and applied multivariate connectivity pattern classification analyses. We found that the whole-brain FC patterns made high-accuracy predictions of which category was being viewed. The decoding was successful even after the contributions of regions showing classical category-selective activations were excluded. We further identified the discriminative network for each category, which span way beyond the classical category-selective regions. Together, these results reveal novel mechanisms about how categorical information is represented in large-scale FC patterns, with general implications for the interactive nature of distributed brain areas underlying high-level cognition. Hum Brain Mapp 37:3685-3697, 2016. © 2016 Wiley Periodicals, Inc.
Keywords: functional connectivity; machine learning algorithm; support vector machine; visual categories.
© 2016 Wiley Periodicals, Inc.