Noise during Rest Enables the Exploration of the Brain's Dynamic Repertoire

By Anandamohan Ghosh, Y. Rho, Anthony Randal McIntosh, Rolf Kötter, Viktor Jirsa. PLoS Comput. Biol., 2008

570 citations recorded by the source corpus.

Traditionally brain function is studied through measuring physiological responses in controlled sensory, motor, and cognitive paradigms. However, even at rest, in the absence of overt goal-directed behavior, collections of cortical regions consistently show temporally coherent activity. In humans, these resting state networks have been shown to greatly overlap with functional architectures present during consciously directed activity, which motivates the interpretation of rest activity as day dreaming, free association, stream of consciousness, and inner rehearsal. In monkeys, it has been shown though that similar coherent fluctuations are present during deep anesthesia when there is no consciousness. Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network. We specifically demonstrate that noise and time delays via propagation along connecting fibres are essential for the emergence of the coherent fluctuations of the default network. The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1–100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging. The combination of anatomical structure and time delays creates a space–time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/1b1e8c5f52d51deb4c5fcba5201525b5db801b31

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

Citations in the corpus

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).”
Predicting human resting-state functional connectivity from structural connectivity (2009, background): “Computational work has suggested that the underlying anatomical architecture of the cerebral cortex, including its cluster structure, shapes resting-state functional connectivity on multiple time scales (22, 33).”
What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology (2009, background): “Noise can lead to highly complex phenomena, and other very different benefits beyond signal processing [11,12].”
A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex (2015, unknown): “Most statistical analyses of connectivity (Bullmore and Sporns, 2009; Sporns, 2014) and computational models (Ghosh et al., 2008; Deco and Corbetta, 2011; Honey et al., 2007, 2009; Deco et al., 2014) have lacked comprehensive high-resolution data, relying either on collating qualitative tract-tracin”
DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI (2010, result): “The current results that DMN showed high activity is consistent with the conclusion that these regions represent the functional core underlying resting brain dynamics (Ghosh et al., 2008; Honey et al., 2009; Zuo et al., 2010).”
Functional connectivity dynamics: Modeling the switching behavior of the resting state (2015, background): “However, more elaborated models could also generate as well other types of behaviors, including the ultra-slow oscillations, known to characterize resting state dynamics (Deco et al., 2009; Ghosh et al., 2008) or chaotic behaviors (Honey et al., 2007).”
The Oscillating Brain: Complex and Reliable (2009, result): “…conclusion that these regions represent the functional core underlying resting brain dynamics, a notion recently supported by a computational model of the resting brain and network analyses combining functional and structural measures (Ghosh et al., 2008; Hagmann et al., 2008; Honey et al., 2009).”
Role of local network oscillations in resting-state functional connectivity (2011, methodology): “In Ghosh et al. (2008a, 2008b) and Knock et al. (2009), using the CoCoMac database”
Signature of consciousness in the dynamics of resting-state brain activity (2015, background): “As the coupling strength increases, a qualitative change in the dynamics occurs: instead of having a single stable state, the system becomes multistable; functional activity spontaneously breaks down into a diversity of states that is sequentially explored in a stochastic manner (16, 21, 23, 37, 38)”
Key role of coupling, delay, and noise in resting brain fluctuations (2009, background): “These results are fully consistent with the parameter ranges found in (13, 27), where the authors identified emergent resting-state networks characteristic for these regimes.”
The human connectome: Origins and challenges (2013, background): “, 2007), rich temporal structure emerges in models of the resting human brain as well (Cabral et al., 2011; Deco et al., 2009; Ghosh et al., 2008).”
Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, unknown): “Furthermore, using local FitzHugh–Nagumo dynamics, Ghosh et al. (2008a), Knock et al. (2009) explored the stability regions of the network as a function of the global coupling strength and conduction speed.”