Small worlds inside big brains

By Olaf Sporns, Christopher J. Honey. Proceedings of the National Academy of Sciences of the United States of America, 2006

313 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/c77fb6b3889b4d5f1104dff1acf960b39213c7fd

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Citations in the corpus

The Organization of Local and Distant Functional Connectivity in the Human Brain (2010, background): “When applied to the study of connectional anatomy, analyses consistently reveal that cortical networks exhibit ‘‘small world’’ properties [32,33].”
Global Workspace Dynamics: Cortical “Binding and Propagation” Enables Conscious Contents (2013, background): “Other authors report that C-T connectivities can be described by small-world mathematics (Sporns and Honey, 2006), and Tononi’s formal measure phi provides a global index of connectivity in the C-T core.”
Disrupted small-world networks in schizophrenia. (2008, background): “…The human brain is a large, dynamic functional network with an economical, small-world architecture and is characterized by high local clustering of connections between neighbouring nodes but with short path lengths between any pair of nodes (Sporns and Honey, 2006; Achard and Bullmore, 2007).”
The application of graph theoretical analysis to complex networks in the brain. (2007, unknown): “These results were fairly consistent when the degree k was varied between 10 and 20, although both C and L increased as a function of k. Graph theoretical properties of MEG recordings in healthy subjects were studied more extensively in a recent paper (Bassett et al., 2006; Sporns and Honey, 2006).”
Complex network measures of brain connectivity: Uses and interpretations (2010, background): “optimal balance of functional integration and segregation (Sporns and Honey, 2006).”
Cortical High-Density Counterstream Architectures (2013, background): “(C) The small-world coefficient Cp=Co Lp=Lo (33, 136) corresponding to each lattice rewiring.”
Functional Brain Networks Develop from a “Local to Distributed” Organization (2009, result): “As previously reported [21,48,49], relative to comparable lattice and completely random graphs, the adult graph architecture showed high clustering coefficients and short path lengths, consistent with the ‘small world’ architecture (Figure 3B and 3C).”
Development of Large-Scale Functional Brain Networks in Children (2009, background): “Small-world characterization is well-suited for analyzing functional brain networks at the systems level because these networks are complex and optimally connected to minimize information processing costs [36,52].”
Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain (2010, methodology): “Given the small world properties of the human brain [7,8], graph-based methods provide a valuable tool for elucidating network structures.”
Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG (2015, unknown): “We note that the assumption of sparse functional connectivity in brain space has biological plausibility [44]–[46].”
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].”