Journal
HUMAN BRAIN MAPPING
Volume 37, Issue 10, Pages 3685-3697Publisher
WILEY-BLACKWELL
DOI: 10.1002/hbm.23268
Keywords
functional connectivity; machine learning algorithm; support vector machine; visual categories
Funding
- National Basic Research Program of China [201303837300, 2014CB846100]
- National Natural Science Foundation of China [31521063, 81171019]
- National Science Fund for Distinguished Young Scholars [81225012]
- Fok Ying Tong Education Foundation [141020]
- New Century Excellent Talents [12-0055]
Ask authors/readers for more resources
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. (c) 2016 Wiley Periodicals, Inc.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available