Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain

NeuroImage

R. Todd Constable¹  Wenjing Luo²  Abigail S. Greene³ 

[1] MD/PhD program, Yale University School of Medicine, United States;Biomedical Engineering, Yale University School of Medicine, United States;Interdepartmental Neuroscience Program, Yale University School of Medicine, United States;

关键词: Graph theory;    Network neuroscience;    Functional connectivity;    Brain, atlas;    Functional parcellation;   

DOI  :  

来源: DOAJ

【 摘 要 】

Interest in understanding the organization of the brain has led to the application of graph theory methods across a wide array of functional connectivity studies. The fundamental basis of a graph is the node. Recent work has shown that functional nodes reconfigure with brain state. To date, all graph theory studies of functional connectivity in the brain have used fixed nodes. Here, using fixed-, group-, state-specific, and individualized- parcellations for defining nodes, we demonstrate that functional connectivity changes within the nodes significantly influence the findings at the network level. In some cases, state- or group-dependent changes of the sort typically reported do not persist, while in others, changes are only observed when node reconfigurations are considered. The findings suggest that graph theory investigations into connectivity contrasts between brain states and/or groups should consider the influence of voxel-level changes that lead to node reconfigurations; the fundamental building block of a graph.

【 授权许可】

Unknown