Asymmetrical network analysis definition6/13/2023 The literature reviewed here suggests that graph-based network analyses are capable of uncovering system-level changes associated with different processes in the resting brain, which could provide novel insights into the understanding of the underlying physiological mechanisms of brain function. These network properties have also been found to change throughout normal development, aging, and in various pathological conditions. Among these is the knowledge that the brain’s intrinsic activity is organized as a small-world, highly efficient network, with significant modularity and highly connected hub regions. Application of these approaches to R-fMRI data has demonstrated non-trivial topological properties of functional networks in the human brain. In this review, we will summarize the recent advances in graph-based brain network analyses of R-fMRI signals, both in typical and atypical populations. Specifically, the topological organization of brain networks has been recently studied with graph theory. The properties of both the intra- and inter-regional connectivity of resting-state brain activity have been well documented, promoting our understanding of the brain as a complex network. Based on changes in the blood oxygen level-dependent signal, R-fMRI offers a novel way to assess the brain’s spontaneous or intrinsic (i.e., task-free) activity with both high spatial and temporal resolutions. In the past decade, resting-state functional MRI (R-fMRI) measures of brain activity have attracted considerable attention. Phyllis Green and Randolph Cõwen Institute for Pediatric Neuroscience, New York University Langone Medical Center, New York, NY, USA State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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