Complex brain networks: graph theoretical analysis of structural and functional systems

By E. Bullmore, Olaf Sporns. 2009

11,689 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Weight Agnostic Neural Networks (2019, methodology): “The connectome can be viewed as a graph [12, 42, 110], and analyzed using rich tools from graph theory, network science and computer simulation.”
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].”
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science (2017, background): “These have been extensively studied in (Bullmore and Sporns, 2009) and include scale-freeness (Barabási and Albert, 1999) and small-worldness (Watts and Strogatz, 1998).”
Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex (2012, background): “Graph theoretical analysis provides a powerful mathematical framework for characterizing topological properties of the complex graphs/networks and is being translated to explore human brain networks derived frommulti-modal neuroimaging data (Bullmore and Sporns, 2009; He and Evans, 2010).”
Process Overlap Theory: A Unified Account of the General Factor of Intelligence (2016, background): “…hubs among parietal and prefrontal regions, providing a potential explanation for their well-documented activation by many cognitive functions” (Bullmore & Sporns, 2009, p. 190), and “studies on the network of areas of the primate and human cerebral cortex showed that the PFC, especially the…”
A review of modularization techniques in artificial neural networks (2019, methodology): “2014) is biologically inspired as evidence suggests that the evolution of the brain, guided by the minimization of wiring length, besides improving information transfer efficiency, produces modular structure (Clune et al. 2013; Bullmore and Sporns 2009).”
Network neuroscience (2017, background): “These analyses have consistently revealed nonrandom topological attributes, such as high clustering and short path length 3,47 , and network communities (modules) linked by highly connected hub nodes 48 that are in turn densely linked, forming an integrative core 49 or rich club 50 .”
Networks of the Brain: Quantitative Analysis and Modeling (2010, neutral): “More detailed and formal treatments can be found elsewhere (Reijneveld et al., 2007; Bullmore and Sporns, 2009; Rubinov and Sporns, 2010).”
Age- and gender-related differences in the cortical anatomical network (2009, background): “Graph theoretical analysis provides a unique tool to reveal intrinsic attributes of the connectivity patterns in a complex network/ graph and is being translated to explore brain organization (Bullmore and Sporns, 2009; He et al., 2009a).”
Phantom percepts: Tinnitus and pain as persisting aversive memory networks (2011, background): “These connectivity studies could be further evaluated by applying network science methodology (100) and thus could lead to identifying ideal targets and stimulation designs for neuromodulation.”
Altered Resting State Complexity in Schizophrenia (2011, methodology): “In both cases, network organization is most often examined over a wide range of sparsity values (Bassett and Bullmore, 2009; Bullmore and Bassett, 2011; Bullmore and Sporns, 2009).”
Task-Based Core-Periphery Organization of Human Brain Dynamics (2012, background): “To study regional specificity of the functional time series and putative interactions between brain areas, it is common to apply a standardized atlas to raw fMRI data [7,80,81].”