The application of graph theoretical analysis to complex networks in the brain.

By Jaap C. Reijneveld, S. Ponten, Henk W. Berendse, Cornelis J. Stam. Clinical Neurophysiology, 2007

522 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/932be1dd78f8d0d291224d17ca01b547f6c0f9e9

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In Cognitive Neuroscience

In Statistical and Nonlinear Physics

Citations in the corpus

Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain (2008, unknown): “…graphs have been suggested, as well as measures of global and local communication efficiency (Achard and Bullmore, 2007; Latora and Marchiori, 2001; Reijneveld et al., 2007; Stam and Reijneveld, 2007) and used as an effective method to control for disconnected nodes that are likely to arise with…”
The Neuroscience of Intelligence (2016, methodology): “The method is called graph analysis (Reijneveld et al., 2007; Stam & Reijneveld, 2007), a more mathematically sophisticated approach that determines how every voxel (also called a node in graph analysis) is correlated to all other voxels and how strong the connections are (connections are called edg”
Loss of ‘Small-World’ Networks in Alzheimer's Disease: Graph Analysis of fMRI Resting-State Functional Connectivity (2010, methodology): “Nevertheless, controlling for K is the most rigorous way to compare networks in terms of ‘pure’ topology [18,20].”
Modularity of Mind (2017, methodology): “There are a number of methods of testing this kind of modularity (Boccaletti et al. 2006; Fortunato 2010; Reijneveld et al. 2007; Schaeffer 2007).”
Comparison of Characteristics between Region- and Voxel-Based Network Analyses in Resting-State fMRI Data (2009, unknown): “In a small-world network, the efficacy of long distance communication can be quantified by a metric known as the characteristic path length L (Stam and Reijneveld 2007; Watts and Strogatz 1998).”
Modular and Hierarchically Modular Organization of Brain Networks (2010, background): “Several recent reviews (Bassett and Bullmore, 2006; Reijneveld et al., 2007; Bullmore and Sporns, 2009; He and Evans, 2010; Rubinov and Sporns, 2010; Wang et al., 2010; Bullmore and Bassett, 2010) have focused on the data analytic methods that can be used to extract complex networks from structural…”
Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks (2010, background): “Recent studies have demonstrated the smallworld organization in normal brain as well as disorganization in pathological brain using these techniques that have excellent temporal resolution, providing outstanding information on the dynamics of the networks [20,52,53].”
Exploring the brain network: a review on resting-state fMRI functional connectivity. (2010, background): “4 explains the notion of a graph and some of its key graph properties, including the clustering-coefficient, characteristic path length, node degree and degree distribution, centrality and modularity (Reijneveld et al., 2007; Sporns et al., 2004; Stam and Reijneveld, 2007).”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “Studies on modeling the human brain as a complex system have grown remarkably as neuroscientists seek to understand the comprehensive information underlying cognition, behavior, and perception (Bassett and Bullmore, 2006; Reijneveld et al., 2007; Bullmore and Sporns, 2009, 2012; He and Evans, 2010; ”
Normalized Cut Group Clustering of Resting-State fMRI Data (2008, methodology): “Furthermore, the used graph representation of connected voxels can be used to examine the organization of the functionally connected resting-state brain [19,43] and possible disruptions in network organization in patients [39].”
Networks of the Brain: Quantitative Analysis and Modeling (2010, unknown): “More detailed and formal treatments can be found elsewhere (Reijneveld et al., 2007; Bullmore and Sporns, 2009; Rubinov and Sporns, 2010).”
Aberrant Frontal and Temporal Complex Network Structure in Schizophrenia: A Graph Theoretical Analysis (2010, methodology): “…the brain is represented as a network of nodes, reflecting the different brain regions, and connections between the nodes, representing white matter tracts (for review on brain networks, see Reijneveld et al., 2007, Bullmore and Sporns, 2009); Figure 1 shows the graph measures used in this paper.”
Synchronization in complex networks (2008, background): “The application of graph theoretical approaches, and the characterization of the functional activity by neural synchronization, have significantly contributed to the understanding of complex networks in the brain [202, 203].”