The small world of the cerebral cortex

By Olaf Sporns, Jonathan D. Zwi. Neuroinformatics, 2007

1,359 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a8a18497987e8b4715cba7cd6d2f8e6a1d58b2fa

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Complex network measures of brain connectivity: Uses and interpretations (2010, background): “, 2009) quantify brain networks with a small number of neurobiologically meaningful and easily computable measures (Sporns and Zwi, 2004; Achard et al., 2006; Bassett et al., 2006; He et al., 2007; Hagmann et al., 2008).”
Small-World Brain Networks Revisited (2016, background): “…analysis of C elegans , pio-neering topological studies of mammalian cortical networks used databases of tract-tracing experiments to demonstrate that the cat and macaque inter-areal anatomical networks shared similar small-world properties of short path length and high clustering [13, 14].”
Network Properties Revealed through Matrix Functions (2010, background): “Large networks arise, for example, • in the cell, connecting genes [59], proteins [25, 60], or other genomic quantities [2, 37], • in the brain, connecting neurological regions [16, 47, 65], • in epidemiology, connecting individuals who come into contact [14], • in zoology, connecting animals that i”
Communication Efficiency and Congestion of Signal Traffic in Large-Scale Brain Networks (2014, methodology): “Latticized networks were generated using a modified version of the same algorithm, whereby the edges were swapped only if they moved closer to the main diagonal as a result [7].”
A Weighted and Directed Interareal Connectivity Matrix for Macaque Cerebral Cortex (2012, background): “Keywords: connection, cortex, graph, monkey, network”
Small-World Brain Networks (2006, methodology): “A similar observation has been made in analysis of anatomical connectivity matrices and proposed as a new way to classify cortical areas as either highly specialized (locally clustered connections) or integrative (long-distance connections; Sporns and Zwi 2004).”
Structural and functional clusters of complex brain networks (2006, background): “, short average pathlength and high clustering coefficient, indicating optimal organization for effective inter-area communication and for achieving high functional complexity [15,18].”
Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain (2008, neutral): “…and Lnet≈Lrandom, with Crandom defined as the clustering-coefficient of a random network Grandom of similar size of Gnet (Sporns, 2006; Sporns et al., 2004) and with a similar average connection degree k and connectivity distribution P(k) as Gnet (Sporns and Zwi, 2004; Stam and Reijneveld, 2007).”
The application of graph theoretical analysis to complex networks in the brain. (2007, neutral): “The authors speculate that this type of network architecture emerges as an adaptation to rich environments and promotes the development of a 'dynamic core' (Sporns et al., 2000b; Sporns and Tononi, 2002; Sporns and Zwi, 2004).”
On the relationship between emotion and cognition (2008, background): “Advances in our understanding of brain connectivity suggest that a given brain region is only a few synapses away from every other brain regio”
Brain Anatomical Network and Intelligence (2009, methodology): “The cortical networks of other mammalian brains [12,38] as well as functional Table 1.”
Hemisphere- and gender-related differences in small-world brain networks: A resting-state functional MRI study (2011, background): “The coexistence of functional segregation and functional integration ensures the effective integration of multiple segregated sources of information in the brain (Tononi et al., 1994; Sporns et al., 2004; Sporns and Zwi, 2004).”
The Ubiquity of Small-World Networks (2011, methodology): “(Sporns and Zwi 2004) found in the brain connectivity toolbox (http://www.”
Development of Large-Scale Functional Brain Networks in Children (2009, methodology): “To evaluate the network for small-world properties, we compared the clustering coefficient and the characteristic path length of the network with corresponding values (Cran, Lran) obtained and averaged across 1,000 random networks with the same number of nodes and degree distribution [98].”