Heterogeneity of time delays determines synchronization of coupled oscillators

Petkoski, Spase; Spiegler, Andreas; Proix, Timothée; Aram, Parham; Temprado, Jean-Jacques; Jirsa, Viktor K.

doi:10.1103/physreve.94.012209

Cited by 52 publications

(82 citation statements)

References 49 publications

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“…The delayed feedback of neural activity to the network has been shown to be able to suppress noise induced dynamics and thus to stabilize brain activity [118]. The type of delay, and its spatial distribution, have in general a pronounced influence on network activity [119].…”

Section: Network With Delay Couplingmentioning

confidence: 99%

Chaos in time delay systems, an educational review

2019

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The time needed to exchange information in the physical world induces a delay term when the respective system is modeled by differential equations. Time delays are hence ubiquitous, being furthermore likely to induce instabilities and with it various kinds of chaotic phases. Which are then the possible types of time delays, induced chaotic states, and methods suitable to characterize the resulting dynamics? This review presents an overview of the field that includes an in-depth discussion of the most important results, of the standard numerical approaches and of several novel tests for identifying chaos. Special emphasis is placed on a structured representation that is straightforward to follow. Several educational examples are included in addition as entry points to the rapidly developing field of time delay systems.

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Section: Network With Delay Couplingmentioning

confidence: 99%

Chaos in time delay systems, an educational review

2019

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“…Some neural mass models (NMM) are linked to (and still reflect to a certain degree) neurophysiological processes at the microscopic scale while others mathematically describe the observed lumped biological behaviour not differentiating between underlying neurophysiological processes (phenomenological models). Time delays in the interaction between nodes (66,68,69,81) are critical for the spatiotemporal organization of the evolving activity patterns in the brain (82,83). Measured functional brain data such as EEG, MEG or functional MRI (fMRI) are used to tune the mathematical models -i.e.…”

mentioning

confidence: 99%

Linking molecular pathways and large-scale computational modeling to assess candidate disease mechanisms and pharmacodynamics in Alzheimer’s disease

Stefanovski

Triebkorn

Spiegler

et al. 2019

Preprint

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Introduction. While the prevalence of neurodegenerative diseases associated with dementia such as Alzheimer's disease (AD) increases, our knowledge on the underlying mechanisms, outcome predictors, or therapeutic targets is limited. In this work, we demonstrate how computational multi--scale brain modelling links phenomena of different scales and therefore identifies potential disease mechanisms leading the way to improved diagnostics and treatment. Methods. The Virtual Brain (TVB; thevirtualbrain.org) neuroinformatics platform allows standardized large--scale structural connectivity--based simulations of whole brain dynamics. We provide proof of concept for a novel approach that quantitatively links the effects of altered molecular pathways onto neuronal population dynamics. As a novelty, we connect chemical compounds measured with positron emission tomography (PET) with neural function in TVB addressing the phenomenon of hyperexcitability in AD related to the protein amyloid beta (Abeta). We construct personalized virtual brains based on individual PET derived distributions of Abeta in patients with mild cognitive impairment (MCI, N=8) and Alzheimer's Disease (AD, N=10) and in age--matched healthy controls (HC, N=15) using data from ADNI--3 data base (http://adni.lni.usc.edu). In the personalized virtual brains, individual Abeta burden modulates regional inhibition, leading to disinhibition and hyperexcitation with high Abeta loads. We analyze simulated regional neural activity and electroencephalograms (EEG). Results. Known empirical alterations of EEG in patients with AD compared to HCs were reproduced by simulations. The virtual AD group showed slower frequencies in simulated local field potentials and EEG compared to MCI and HC groups. The heterogeneity of the Abeta load is crucial for the virtual EEG slowing which is absent for control models with homogeneous Abeta distributions. Slowing phenomena primarily affect the network hubs, independent of the spatial distribution of Abeta. Modeling the N--methyl--D--aspartate (NMDA) receptor antagonism of memantine in local population models, reveals potential functional reversibility of the observed large--scale alterations (reflected by EEG slowing) in virtual AD brains. Discussion. We demonstrate how TVB enables the simulation of systems effects caused by pathogenetic molecular candidate mechanisms in human virtual brains.

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“…Even though delayed interactions due to axonal transmission can be of crucial importance for the observed dynamics of the oscillatory systems (Ghosh et al, 2008; Petkoski et al, 2016, 2018), the impact of these delays is much less pronounced for low frequencies compared with them, as it is the case here. Moreover, the tracing used for obtaining the AMBA Connectome (Oh et al, 2014) does not allow tracking the length of the white fibers.…”

Section: Modelsmentioning

confidence: 61%

“…We thus explore the possibility of up to 5 times of weights of the damaged links to be redistributed along the rest of the links of the nodes directly connected with the infarct area, in order to also allow for over-compensation of the lost direct connectivity. The absence of time-delays and the focus on the phase locking, makes the model insensitive on the chosen frequencies (Petkoski et al, 2016), which are therefore fixed in the simulations. The natural time-variability of parameters Petkoski and Stefanovska (2012) is assumed to be stochastic (Petkoski et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

Experimental and computational study on motor control and recovery after stroke: towards a constructive loop between experimental and virtual embodied neuroscience

Mascaro

Falotico

Petkoski

et al. 2020

Preprint

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2Being able to replicate real experiments with computational simulations is a unique opportunity 3 to refine and validate models with experimental data and redesign the experiments based on 4 simulations. However, since it is technically demanding to model all components of an experiment, 5 traditional approaches to modeling reduce the experimental setups as much as possible. In this 6 1 Allegra Mascaro, Falotico, Petkoski et al. Towards closed-loop experiments and simulationsstudy, our goal is to replicate all the relevant features of an experiment on motor control and 7 motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous 8 integration of new experimental data into a computational modeling framework. First, results show 9 that we could reproduce experimental object displacement with high accuracy via the simulated 10 embodiment in the virtual world by feeding a spinal cord model with experimental registration of the 11 cortical activity. Second, by using computational models of multiple granularities, our preliminary 12 results show the possibility of simulating several features of the brain after stroke, from the 13 local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies 14 are proposed to merge the two pipelines. We further suggest that additional models could be 15 integrated into the framework thanks to the versatility of the proposed approach, thus allowing 16 many researchers to achieve continuously improved experimental design. 17 18 response of the environment itself, in that the output of the brain is relevant only if it has the ability to 19 impact the future and hence the input the brain receives. This "closed-loop" can be simulated in a virtual 20 world, where simulated experiments reproduce actions (output from the brain) that have consequences 21 (future input to the brain) (Zrenner et al., 2016). To the aim of reproducing in silico the complexity of real 22 experiments, different levels of modeling shall be integrated. However, since modeling all components of an 23 experiment is very difficult, traditional approaches of computational neuroscience reduce the experimental 24 setups as much as possible. An "Embodied brain" (or "task dynamics", see Zrenner et al. (2016)) approach Allegra Mascaro, Falotico, Petkoski et al. Towards closed-loop experiments and simulationscould overcome these limits by associating the modelled brain activity with the generation of behavior 25 within a virtual or real environment, i.e. an entailment between an output of the brain and a feedback 26 signal into the brain (Tessadori et al., 2012; DeMarse et al., 2001; Reger et al., 2000). The experimenter 27 can interfere with the flow of information between the neural system and environment on the one hand 28 and the state and transition dynamics of the environment on the other. Closing the loop can be performed 29 effectively by (i) validating the models on experimental data, and (ii) designing new experiments based on 30 the hypotheses f...

show abstract

Heterogeneity of time delays determines synchronization of coupled oscillators

Cited by 52 publications

References 49 publications

Chaos in time delay systems, an educational review

Chaos in time delay systems, an educational review

Linking molecular pathways and large-scale computational modeling to assess candidate disease mechanisms and pharmacodynamics in Alzheimer’s disease

Experimental and computational study on motor control and recovery after stroke: towards a constructive loop between experimental and virtual embodied neuroscience

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