Brain graphs: graphical models of the human brain connectome.

By E. Bullmore, D. Bassett. Annual Review of Clinical Psychology, 2011

1,164 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c6b745c7ecc3fc89d0df71727e1a0f456be7187a

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Citations in the corpus

Task-Based Core-Periphery Organization of Human Brain Dynamics (2012, background): “the entire human brain remained challenging until recent applications of network theory to neuroimaging data [5].”
A review of modularization techniques in artificial neural networks (2019, background): “…of relative connections between neurons and that functionality emerges from intra-modular and inter-modular interactions revolutionized the way we research nervous systems and transformed the idea of a connectome (Bullmore and Bassett, 2011; Sporns, 2011) into a key area of brain research.”
Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks (2011, methodology): “All metrics in the MEG networks were normalized by their median values in 10 random networks matched for degree distribution (Bullmore and Bassett, 2011).”
Structural connectomics in brain diseases (2013, positive): “The nodes of such a brain network represent cortical, subcortical and cerebellar structures, while the edges represent connections between grey matter regions (Bullmore and Sporns, 2009; Bullmore and Bassett, 2011).”
Cliques and cavities in the human connectome (2016, background): “While traditional notions from graph theory and network science preferentially focus on local properties of the network at individual vertices or edges [14–16, 68], here we”
GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks (2012, background): “applicable to any modality, scale, or volume of neuroscientific data [25].”
Altered Resting State Complexity in Schizophrenia (2011, methodology): “The construction of binary graphs from functional connectivity correlation matrices has historically required the choice of a threshold or range of thresholds over which to assess network structure (Bullmore and Bassett, 2011).”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, background): “, 2012), and has been extensively explored by using resting state functional connectivity (RSFC) approaches (Biswal et al., 1995; Bullmore and Bassett, 2011; Fox and Raichle, 2007; Wang et al., 2010).”
Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data (2011, background): “These brain networks have consistently demonstrated many non-trivial topological properties, such as small-worldness, modularity and highly connected hubs (for reviews, see [3,4,5,6,7]), and exhibited distinct alterations associated with different neurocognitive disorders (for reviews, see [8,9]).”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “…information about the topological architecture of human brain networks, such as small-worldness, modular organization, and highly connected or centralized hubs (Bullmore and Sporns, 2009, 2012; He and Evans, 2010; Meunier et al., 2010; Bullmore and Bassett, 2011; van den Heuvel and Sporns, 2013).”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, positive): “Excellent introductions to the basic concepts of graph theory and their application to neuroscience have been provided elsewhere (Albert and Barabasi, 2002; Bullmore and Bassett, 2011; Bullmore and Sporns, 2009; Newman, 2003; Sporns, 2011b, 2012; Sporns et al., 2004; Stam and Reijneveld, 2007).”
Schizophrenia, neuroimaging and connectomics (2012, methodology): “Node definition The connectivity matrix, C, can be represented in graph form, termed a brain graph (Bullmore and Bassett, 2011), as a collection of nodes interconnected by edges (Fig.”
Modular and Hierarchically Modular Organization of Brain Networks (2010, methodology): “…(Guimerà et al., 2004), it is always necessary to compare the results obtained from analysis of a brain network with the modularity of appropriate “null model” networks, such as classical Erdös–Renyí graphs, or randomly rewired versions of the observed brain networks (Bullmore and Bassett, 2010).”
Two’s company, three (or more) is a simplex (2016, background): “Over the last decade, network models have demonstrated broad utility in uncovering fundamental architectural principles (Bassett and Bullmore, 2006; Bullmore and Bassett, 2011) and their implications for cognition (Medaglia et al, 2015) and disease (Stam, 2014).”