4.7 Article

A Stepwise Multivariate Granger Causality Method for Constructing Hierarchical Directed Brain Functional Network

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2022.3202535

关键词

Brain modeling; Functional magnetic resonance imaging; Relays; Organizations; Mathematical models; Magnetic resonance; Life sciences; Brain functional networks; functional magnetic resonance imaging (fMRI); Granger causality; hierarchic directed network; stepwise multivariate Granger causality (SMGC)

资金

  1. National Natural Science Foundation of China [62173070, 82121003, 62036003, 62006038, U1808204]
  2. Ministry of Science and Technology of China [2018AAA0100705, 2021ZD0201701]
  3. Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine [ZYYCXTD-D-202003]

向作者/读者索取更多资源

The study proposed a new approach called SMGC to model the directed and hierarchical features of brain functional network and explore causal relationships. The simulation study and application to real datasets demonstrated that the method can capture the complex hierarchical organization of the brain network.
The directed brain functional network construction gives us the new insights into the relationships between brain regions from the causality point of view. The Granger causality analysis is one of the powerful methods to model the directed network. The complex brain network is also hierarchically constructed, which is particularly suited to facilitate segregated functions and the global integration of the segregated functions. Therefore, it is of great interest to explore new approach to model the hierarchical architecture of the directed network. In the present study, we proposed a new approach, namely, stepwise multivariate Granger causality (SMGC), considering both the directed and hierarchical features of brain functional network to explore the stepwise causal relationship in the network. The simulation study demonstrated that the diverse and complex hierarchical organization could be embedded in the apparently simple directed network. The proposed SMGC method could capture the multiple hierarchy of the directed network. When applying to the real functional magnetic resonance imaging (fMRI) datasets, the core triple resting-state networks in human brain showed within-network directed connections in the first-level directed network and rich and diverse between-network pathways in the second-level hierarchical network. The default mode network (DMN) had a prominent role in the resting-state acting as both the causal source and the important relay station. Further exploratory research on the adaption of directed hierarchical network in athletes suggested the enhanced bidirectional communication between the DMN and the central executive network (CEN) and the enhanced directed connections from the salience network (SN) to the CEN in the athlete group. The SMGC approach is capable of capturing the hierarchical architecture of the brain directed functional network, which refreshes the new stepwise causal relationship in the directed network. This might shed light on the potential application for exploring the altered hierarchical organization of brain directed network in neuropsychiatric disorders.

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