Small-World Brain Networks

By D. Bassett, E. Bullmore. The Neuroscientist, 2006

2,628 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b0e9ea2ce2ea9713c128f47db3f1aaf0c5c2f175

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Statistical and Nonlinear Physics

Citations in the corpus

GPU Kernels for Block-Sparse Weights (2017, background): “Another example is the human brain, whose anatomical and functional networks often also exhibit smallworldness [Bassett and Bullmore, 2006].”
Cliques and cavities in the human connectome (2016, background): “2014), though there are exceptions that give the brain its topological properties such as its small-world architecture (Bassett and Bullmore 2006; Pessoa 2014; Hilgetag and Goulas 2016; Muldoon et al. 2016a; Bassett and Bullmore 2016).”
Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer’s Disease (2009, background): “Several previous analyses have demonstrated that connectivity among cortical regions is not random or proportionate across regions but rather exhibits “small world” properties, including hubs (Watts and Strogatz, 1998; Sporns et al., 2004; Achard et al., 2006; Bassett and Bullmore, 2006).”
Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease (2008, background): “Small-world characterization is well-suited for analyzing anatomical and functional brain networks at the system level because these networks are complex and optimally connected to minimize information processing costs [32,33].”
A Predictive Network Model of Cerebral Cortical Connectivity Based on a Distance Rule (2013, neutral): “An example is the small-world (SW) hypothesis examined in earlier work claiming that the interareal network is sparse, exhibits short path lengths and high clustering, and that these properties form an organization principle of information integration in the cortex (Bassett and Bullmore, 2006; Bullm”
Exploring Randomly Wired Neural Networks for Image Recognition (2019, methodology): “Random graph modeling has been used as a tool to study the neural networks of human brains [2, 4, 3].”
Identification and Classification of Hubs in Brain Networks (2007, background): “Brain networks have been found to exhibit high levels of clustering combined with short average path lengths, a pattern indicative of a small-world architecture [7–11].”
Functional data analysis for density functions by transformation to a Hilbert space (2016, background): “corded and then processed to yield voxel-speciﬁc time courses of signal strength. Functional connectivity between voxels is customarily quantiﬁed in this area by the Pearson productmoment correlation [1, 5, 49] which, from a functional data analysis point of view, corresponds to a special case of dy”
Altered Resting State Complexity in Schizophrenia (2011, methodology): “…univariate estimates of the complexity of brain signals (Bullmore et al., 2009) and 2) the more recent multivariate approach based on the application of complex network theory (Bassett and Bullmore, 2006; Bassett and Bullmore, 2009; Bullmore and Bassett, 2011; Bullmore and Sporns, 2009).”
Spatio-Temporal Data Mining (2017, background): “For example, while positive correlations have generally been studied in brain imaging [Bassett and Bullmore 2006], negative correlations (also referred to as teleconnections) among distant locations have been studied in climate datasets [Yang and DelSole 2012].”
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; ”
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…”
Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory (2008, neutral): “Small-world topology is generally associated with global and local parallel information processing, sparse connectivity between nodes and low wiring costs (Bassett and Bullmore, 2006).”