Pairwise Interactions among Brain Regions Organize Large-Scale Functional Connectivity during Execution of Various Tasks.

Journal: Neuroscience
Published Date:

Abstract

Spatially separated brain areas interact with each other to form networks with coordinated activities, supporting various brain functions. Interaction structures among brain areas have been widely investigated through pairwise measures. However, interactions among multiple (e.g., triple and quadruple) areas cannot be reduced to pairwise interactions. Such higher order interactions (HOIs), e.g., exclusive-or (XOR) operation, are widely implemented in computation systems and are crucial for effective information processing. However, it is currently unclear whether any HOIs are present in large-scale brain functional networks when subjects are executing specific tasks. Here we analyzed functional magnetic resonance imaging (fMRI) data collected from human subjects executing various perceptual, motor, and cognitive tasks. We found that HOI strength in the macroscopic functional networks was very weak for all tasks, suggesting that major brain activities do not rely on HOIs on the macroscopic level at the timescale of hundreds of milliseconds. These weak HOIs during tasks were further investigated with a neural network model activated by external inputs, which suggested that weak pairwise interactions among brain areas organized the system without involving HOIs. Taken together, these results demonstrated the dominance of pairwise interactions in organizing coordinated activities among different brain areas to support various brain functions.

Authors

  • Weikun Niu
    Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Xuhui Huang
    Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190 Beijing, China; Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, 100190 Beijing, China. Electronic address: xuhui.huang@ia.ac.cn.
  • Kaibin Xu
    Chinese Academy of Sciences, Institute of Automation, Beijing, China.
  • Tianzi Jiang
    Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190 Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 100190 Beijing, China; University of Chinese Academy of Sciences, 100049 Beijing, China.
  • Shan Yu
    Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190 Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 100190 Beijing, China; University of Chinese Academy of Sciences, 100049 Beijing, China. Electronic address: shan.yu@nlpr.ia.ac.cn.