Biological Cybernetics

, Volume 102, Issue 1, pp 57–69

Evaluating the effective connectivity of resting state networks using conditional Granger causality

  • Wei Liao
  • Dante Mantini
  • Zhiqiang Zhang
  • Zhengyong Pan
  • Jurong Ding
  • Qiyong Gong
  • Yihong Yang
  • Huafu Chen
Original Paper

DOI: 10.1007/s00422-009-0350-5

Cite this article as:
Liao, W., Mantini, D., Zhang, Z. et al. Biol Cybern (2010) 102: 57. doi:10.1007/s00422-009-0350-5

Abstract

The human brain has been documented to be spatially organized in a finite set of specific coherent patterns, namely resting state networks (RSNs). The interactions among RSNs, being potentially dynamic and directional, may not be adequately captured by simple correlation or anticorrelation. In order to evaluate the possible effective connectivity within those RSNs, we applied a conditional Granger causality analysis (CGCA) to the RSNs retrieved by independent component analysis (ICA) from resting state functional magnetic resonance imaging (fMRI) data. Our analysis provided evidence for specific causal influences among the detected RSNs: default-mode, dorsal attention, core, central-executive, self-referential, somatosensory, visual, and auditory networks. In particular, we identified that self-referential and default-mode networks (DMNs) play distinct and crucial roles in the human brain functional architecture. Specifically, the former RSN exerted the strongest causal influence over the other RSNs, revealing a top-down modulation of self-referential mental activity (SRN) over sensory and cognitive processing. In quite contrast, the latter RSN was profoundly affected by the other RSNs, which may underlie an integration of information from primary function and higher level cognition networks, consistent with previous task-related studies. Overall, our results revealed the causal influences among these RSNs at different processing levels, and supplied information for a deeper understanding of the brain network dynamics.

Keywords

Resting state networksEffective connectivityIndependent component analysisConditional Granger causality analysis

Supplementary material

422_2009_350_MOESM1_ESM.doc (486 kb)
ESM 1 (DOC 486 kb)

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Wei Liao
    • 1
  • Dante Mantini
    • 2
    • 3
    • 4
  • Zhiqiang Zhang
    • 5
  • Zhengyong Pan
    • 1
  • Jurong Ding
    • 1
  • Qiyong Gong
    • 6
  • Yihong Yang
    • 7
  • Huafu Chen
    • 1
  1. 1.Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China
  2. 2.Institute for Advanced Biomedical TechnologiesG. D’Annunzio University FoundationChietiItaly
  3. 3.Department of Clinical Sciences and Bio-imagingG. D’Annunzio UniversityChietiItaly
  4. 4.Laboratory for Neuro-PsychophysiologyK.U. Leuven Medical SchoolLeuvenBelgium
  5. 5.Department of Medical Imaging, Nanjing Jinling Hospital, Clinical School, Medical CollegeNanjing UniversityNanjingPeople’s Republic of China
  6. 6.Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan University, West China School of MedicineChengduPeople’s Republic of China
  7. 7.Neuroimaging Research BranchNational Institute on Drug Abuse, National Institutes of HealthBaltimoreUSA