Dynamic causality analysis on default mode network

  • Authors:

  • Rongrong Cao;Dongjuan Zhu;Qinqin Huang;Xunheng Wang;Zongcai Ruan

  • Affiliations:

  • Research Center for Learning Science, Southeast University, Nanjing, China;Research Center for Learning Science, Southeast University, Nanjing, China;Research Center for Learning Science, Southeast University, Nanjing, China;Research Center for Learning Science, Southeast University, Nanjing, China;Research Center for Learning Science, Southeast University, Nanjing, China

  • Venue:
  • AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
  • Year:
  • 2012

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Abstract

In most studies of functional connectivity, the default mode network (DMN) is seen as a node and is analyzed with other nodes, or just examines the static interactions among the components of DMN. Few studies have analyzed the dynamic interactions among the components of DMN. In order to evaluate the dynamic connectivity within the components, Conditional-Granger causality analysis (CGCA) based on sliding window is applied to the components of DMN extracted by kernel independent component analysis (KICA). The results suggest that the connectivity of DMN changed significantly during the process of entering the resting-state. Especially, the left inferior parietal lobes (lIPL) are found significant activity and affect other regions at the beginning of entering the resting-state. Specifically, under the resting-state, lIPL's influence on other regions is suppressed by the medial prefrontal cortex (mPFC).