2020
DOI: 10.1101/2020.12.28.424574
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Brain-scale emergence of slow-wave synchrony and highly responsive asynchronous states based on biologically realistic population models simulated in The Virtual Brain

Abstract: Understanding the many facets of the organization of brain dynamics at large scales remains largely unexplored. Here, we construct a brain-wide model based on recent progress in biologically-realistic population models obtained using mean-field techniques. We use The Virtual Brain (TVB) as a simulation platform and incorporate mean-field models of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons. Such models can capture the main intrinsic firing properties of central neurons, such as adaptati… Show more

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Cited by 35 publications
(70 citation statements)
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“…Whole-brain models of neuronal activity have significantly increased our understanding of how functional brain states emerge from their underlying structural substrate, and have provided new mechanistic insights into how brain function is affected when other factors are altered such as neuromodulation [28,53,54] disruption [38,55], or external stimuli [56,57]. Adding to these findings, the present results provide a causal link between a localised connectome-based degeneration model of aging and age-variations of high-order functional interdependencies.…”
Section: Discussionsupporting
confidence: 58%
“…Whole-brain models of neuronal activity have significantly increased our understanding of how functional brain states emerge from their underlying structural substrate, and have provided new mechanistic insights into how brain function is affected when other factors are altered such as neuromodulation [28,53,54] disruption [38,55], or external stimuli [56,57]. Adding to these findings, the present results provide a causal link between a localised connectome-based degeneration model of aging and age-variations of high-order functional interdependencies.…”
Section: Discussionsupporting
confidence: 58%
“…At this scale, the temporal dissection exists, and the variation of a parameter or slow variables enables the transitions to the ictal state and its return to a "healthy" regime [48,51]. As such models are at the scale of a large region, it is possible to connect them to create a large scale network describing the whole brain [52,53].…”
Section: From Mean-field To Large Scale Modelsmentioning
confidence: 99%
“…Given that even a simple, constant perturbation can provide clear insights into the complexity of brain dynamics ( 7, 18, 25, 29 ), we reasoned that a model-based strength-dependent perturbation would be able to reveal more detailed causal mechanistic principles of brain dynamics. Therefore, we show that using strength-dependent perturbations - instead of the classical flat, constant perturbations - provides the means to disentangling alternative model-based hypotheses about the underlying empirical dynamics.…”
Section: Introductionmentioning
confidence: 99%