Scale-free brain functional networks.

By Víctor M. Eguíluz, Dante R. Chialvo, Guillermo A. Cecchi, Marwan N. Baliki, Apkar Vania Apkarian. Physical Review Letters, 2003

1,462 citations recorded by the source corpus.

Functional magnetic resonance imaging is used to extract functional networks connecting correlated human brain sites. Analysis of the resulting networks in different tasks shows that (a) the distribution of functional connections, and the probability of finding a link versus distance are both scale-free, (b) the characteristic path length is small and comparable with those of equivalent random networks, and (c) the clustering coefficient is orders of magnitude larger than those of equivalent random networks. All these properties, typical of scale-free small-world networks, reflect important functional information about brain states.

Subfields: Cognitive Neuroscience, Computational Theory and Mathematics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/1a432dcd894d1e20e6f46d308e130009b536ca3d

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 Computational Theory and Mathematics

In Statistical and Nonlinear Physics

Citations in the corpus

Resting state fMRI: A review of methods and clinical applications (2012, positive): “Small world networks have high clustering coefficients implying high levels of local connections (ie, cliques or groups) and an overall short distance between any 2 nodes, or a small average path length.(40-42) Another method used to analyze RS-fMRI data is clustering algorithms.”
Emergent complex neural dynamics (2010, background): “Notice the fat tails in the brain data, (reported earlier in refs[69, 70]) which are only replicated when the Ising model is posed at critical temperature.”
Disrupted small-world networks in schizophrenia. (2008, result): “This issue is supported by many previous studies based on MEG (Stam, 2004), EEG (Micheloyannis et al., 2006b) and fMRI (Eguiluz et al., 2005; Salvador et al., 2005a; Achard et al., 2006).”
Small-world networks and functional connectivity in Alzheimer's disease. (2006, background): “…in animals, fMRI BOLD signals, or MEG recordings have also been shown to have small-world characteristics (Stephan and others 2000; Dodel and others 2002; Stam 2004; Eguiluz and others 2005; Salvador, Suckling, Coleman, and others 2005; Salvador, Suckling, Schwarzbauer, and Bullmore 2005).”
Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. (2007, methodology): “functional magnetic resonance imaging (fMRI) (Eguiluz et al. 2005; Salvador, Suckling, Coleman, et al. 2005; Achard et al. 2006) studies have consistently demonstrated that human brain”
Brain Anatomical Network and Intelligence (2009, methodology): “and structural human brain networks exhibit small-world properties [13–16,20,29–32,39].”
Efficiency of Functional Brain Networks and Intellectual Performance (2009, background): “Recent studies have shown that the functional connections of the brain network are organized in a highly efficient small-world manner (Sporns et al., 2004; Stam, 2004; Eguíluz et al., 2005; Achard et al., 2006; van den Heuvel et al., 2008b).”
The Metastable Brain (2014, neutral): “Such coordination happens dynamically, with ensembles of various sizes coming together and disbanding incessantly (Eguı́luz et al., 2005; Kozma et al., 2005; Plenz and Chialvo, 2009).”
The temporal structures and functional significance of scale-free brain activity (2010, positive): “Notably, functional brain networks extracted from voxel-byvoxel spontaneous fMRI signal correlations have a connectivity distribution that follows power-law scaling with an exponent close to 2, suggesting a scale-free network topology (Eguı́luz et al., 2005; van den Heuvel et al., 2008).”
Spatial Networks (2010, background): “1); n/a n/a n/a Human brain [120] 4891 4.”
Self-organized criticality as a fundamental property of neural systems (2014, background): “Correlations can also be used to construct functional connectivity maps, whose power-law distributed properties might relate to self-organized criticality (e.g., Eguiluz et al., 2005; Bassett et al., 2006; Expert et al., 2010; Lee et al., 2010; Van De Ville et al., 2010).”
Complex brain networks: graph theoretical analysis of structural and functional systems (2009, background): “…small-world properties of the resulting whole-brain networks 68 . almost simultaneously, another study reported small-world properties of functional networks derived from a set of activated voxels in fmRI data; this voxel-level network was also reported to have a scale-free degree distribution 69 .”
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, methodology): “Network measures of healthy human functional brain connectivity have previously been explored with simple correlation (Eguiluz et al., 2005), partial correlation (Salvador et al.”
Loss of ‘Small-World’ Networks in Alzheimer's Disease: Graph Analysis of fMRI Resting-State Functional Connectivity (2010, background): “The global functional topology can then be expressed in essential organizational principles, like clustering and connectedness [28,49,50,51,52,53,54] and be used to interpret global functional dynamics within a network model [18,23].”