Emerging concepts for the dynamical organization of resting-state activity in the brain

By Gustavo Deco, Viktor Jirsa, Anthony Randal McIntosh. Nature Reviews Neuroscience, 2010

1,761 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/71b8a4831d31f248413f539d44b75b20da3eec7c

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

The Virtual Brain: a simulator of primate brain network dynamics (2013, methodology): “Thus the simulation side of TVB has evolved out of a research program seeking to identify and reproduce realistic whole brain network dynamics, on the basis of empirical connectivity and neural field models (Jirsa and Stefanescu, 2010; Deco et al., 2011).”
Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty (2011, neutral): “…functional brain networks and connectivity (Beckmann and Smith, 2004; Calhoun et al., 2001, 2003); (Bullmore and Sporns, 2009; Cole et al., 2010; Deco et al., 2011; Friston et al., 1997; Greicius et al., 2003; McIntosh, 2000; Rubinov and Sporns, 2010; Sun et al., 2004; Supekar et al., 2008;…”
The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core (2016, positive): “This theoretical framework has been largely successful in explaining the highly structured dynamics arising from spontaneous brain activity in the so-called resting-state-networks (RSN) 19–21 , even if the resting brain never truly rests 20 .”
Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques (2013, methodology): “Additional work will have to focus on identifying activity profiles, transition states, and task-related changes to the dynamic behavior using novel analysis strategies— particularly at the group level [Chang and Glover, 2010; Deco et al., 2011; Sato et al., 2006].”
Communication Efficiency and Congestion of Signal Traffic in Large-Scale Brain Networks (2014, background): “For example, nonlinear dynamic models can capture the physiological complexity of neural activity to a much greater extent [5, 6, 10, 11].”
The structural-functional connectome and the default mode network of the human brain (2014, result): “In agreement with the above, results from studies that simulated functional connectivity based on empirical structural connectivity have led to the hypothesis that human neuroanatomy allows for certain functional modes to be active over time (Deco et al., 2011).”
Abnormal intrinsic brain functional network dynamics in Parkinson’s disease (2017, background): “It has been indicated that dynamic functional connectivity may reflect aspects of neural system functional capacity (Deco et al., 2011; Kucyi et al., 2017) and thus, may Figure 3 Functional connectivity state results.”
Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, background): “The existence of multiple configurations of FC unfolding from the same SC leads us to view the live brain from the perspective of dynamical systems’ theory as a multi-stable system: the same neuroanatomical structure supports a large repertoire of stable dynamical states (Deco et al., 2011).”
Criticality in the brain: A synthesis of neurobiology, models and cognition. (2017, background): “It has been suggested that near-critical dynamics in the default mode operate to increase the “dynamic repertoire” of the brain when subjects are at rest (Deco and Jirsa, 2012; Deco et al., 2011) – that is, to increase the number of proximal “cognitive sets” or stored memories (Scarpetta and de Cand”
Functional network organization of the human brain (2011, background): “Advances in neuroimaging that facilitate the study of brain relationships in humans have stimulated an enormous amount of scientific and medical interest in recent years (Biswal et al., 1995; Bullmore and Sporns, 2009; Deco et al., 2011; Dosenbach et al., 2010).”
Neural and Computational Mechanisms of Action Processing: Interaction between Visual and Motor Representations. (2015, background): “Recently, such models are also often discussed under the term ‘‘neural mass models’’ (e.g., Deco et al., 2011).”
Tools of the trade: psychophysiological interactions and functional connectivity. (2012, methodology): “PPI analysis was originally proposed by Friston and colleagues in 1997, and has become more commonly used in recent years, perhaps as neuroscientists increasingly think about the brain in terms of networks and interactions between brain regions (Bullmore and Sporns, 2009; Deco et al., 2011; Friston,”
Functional connectivity dynamics: Modeling the switching behavior of the resting state (2015, background): “Modeling studies (Deco et al., 2009; Deco et al. 2011; Ghosh et al., 2008; Honey et al., 2007) have demonstrated the importance of the interplay between anatomical structure, local neural dynamics and noise in the emergence of resting-state inter-regional correlations described by functional…”