Key role of coupling, delay, and noise in resting brain fluctuations

By Gustavo Deco, Viktor Jirsa, Anthony Randal McIntosh, Olaf Sporns, Rolf Kötter. Proceedings of the National Academy of Sciences of the United States of America, 2009

784 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/b387c1bf4dbe5b80d621b824ee301c0cf2c4e31c

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In Cognitive Neuroscience

Citations in the corpus

Criticality in the brain: A synthesis of neurobiology, models and cognition. (2017, background): “The principles supporting the emergence of these patterns of activity are not yet fully understood but recent studies using neuroimaging techniques such as functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) (Deco et al., 2009; LinkenkaerHansen et al., 2001; Stam and de Bruin”
Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, positive): “Some variants of this model embedded in BNMs can be found in the literature (Deco et al., 2009; Daffertshofer and van Wijk, 2011).”
Dynamic models of large-scale brain activity (2017, methodology): “The resulting ensemble dynamics reflect this mix of nonlinear neural dynamics, interneural coupling and noise 16,17 .”
Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations (2013, background): “spatiotemporal patterns, oscillations, multistability, and chaos (Roxin et al., 2005; Ghosh et al., 2008; Deco et al., 2009).”
Can structure predict function in the human brain? (2010, positive): “…by slower and mutually interdependent changes in SC and FC. Computational models of large-scale neural dynamics suggest that rapid changes in FC can occur in the course of spontaneous activity, even while SC remains unaltered (e.g. Honey et al., 2007; Ghosh et al., 2008; Deco et al., 2009).”
The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core (2016, positive): “Here we aim to provide a better understanding of the dynamics using brain network computational modelling which has emerged as a powerful tool for investigating the causal dynamics of the human brain, when carefully constrained by functional (FC) and structural connectivity (SC) obtained from empiri”
Generative Models of Cortical Oscillations: Neurobiological Implications of the Kuramoto Model (2010, positive): “…processes in real neuronal systems are arguably of great importance because of the mounting evidence for the functional role of noise in neurophysiological recordings (Faisal et al., 2008), perceptual performance (Moss et al., 2004) and computational models of the brain (Deco et al., 2009).”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, positive): “Dynamical models, involving the simulation of biophysically plausible neural mass models on empirically derived anatomical connectivity architectures have also proved useful for understanding the relationship between connectome structure and function in health (Breakspear et al., 2010; Cabral et al.”
Reliable Intrinsic Connectivity Networks: Test-Retest Evaluation Using ICA and Dual Regression Approach (2009, positive): “Several recent theoretical models support the existence of anti-correlations (Honey et al., 2007; Steyn-Ross et al., 2009; Deco et al., 2006), suggesting that negative connectivity may be a true organizational characteristic of the resting state brain, with neural origins (Popa et al.”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, methodology): “A series of models of resting-state dynamics in macaque and human cortices (Deco et al., 2009; Ghosh et al., 2008; Honey et al., 2007), using different implementations for node dynamics and interaction terms, has yielded a largely consistent set of results (reviewed in Deco et al.”
Learning sculpts the spontaneous activity of the resting human brain (2009, background): “Analysis of large-scale neural network models indicates that spontaneous network anticorrelation is an efficient computational state to facilitate independent task recruitment and switching (44).”
Communication dynamics in complex brain networks (2017, background): “Large-scale nonlinear models have proved versatile, and recapitulate a range of phenomena, including electrical and haemodynamic functional connectivity patterns 66,133 , clustering into intrinsic connectivity networks 134 .”