title: Understanding structural-functional relationships in the human brain: a large-scale network perspective.
abstract: Relating the brain's structural connectivity (SC) to its functional connectivity (FC) is a fundamental goal in neuroscience because it is capable of aiding our understanding of how the relatively fixed SC architecture underlies human cognition and diverse behaviors. With the aid of current noninvasive imaging technologies (e.g., structural MRI, diffusion MRI, and functional MRI) and graph theory methods, researchers have modeled the human brain as a complex network of interacting neuronal elements and characterized the underlying structural and functional connectivity patterns that support diverse cognitive functions. Specifically, research has demonstrated a tight SC-FC coupling, not only in interregional connectivity strength but also in network topologic organizations, such as community, rich-club, and motifs. Moreover, this SC-FC coupling exhibits significant changes in normal development and neuropsychiatric disorders, such as schizophrenia and epilepsy. This review summarizes recent progress regarding the SC-FC relationship of the human brain and emphasizes the important role of large-scale brain networks in the understanding of structural-functional associations. Future research directions related to this topic are also proposed.
authors: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, China Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, China.
authors: Zhijiang
authors: Z
authors: Wang
authors: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, China Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, China.
authors: Zhengjia
authors: Z
authors: Dai
authors: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, China Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, China.
authors: Gaolang
authors: G
authors: Gong
authors: Department of Physics, Centre for Nonlinear Studies and The Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong Research Centre, HKBU Institute of Research and Continuing Education, Virtual University Park Building, South Area Hi-tech Industrial Park, Shenzhen, China.
authors: Changsong
authors: C
authors: Zhou
authors: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, China Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, China yong.he@bnu.edu.cn.
authors: Yong
authors: Y
authors: He
mesh_terms: D001921:Brain / Q000166:cytology* / Q000502:physiology*; D063132:Connectome; D056324:Diffusion Tensor Imaging; D006801:Humans; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology* / Q000502:physiology*
keywords: connectome; functional connectivity; graph theory; module; rich club; structural connectivity