scholarly journals Partial synchronization patterns in brain networks

2021 ◽  
Author(s):  
Eckehard Schoell
Keyword(s):  
Network Topology ◽  
Epileptic Seizure ◽  
Neuronal Networks ◽  
Brain Networks ◽  
Brain Dynamics ◽  
Local Dynamics ◽  
Partial Synchronization ◽  
Chimera States ◽  
Unihemispheric Sleep ◽  
Complex Patterns

Abstract Partial synchronization patterns play an important role in the functioning of neuronal networks, both in pathological and in healthy states. They include chimera states, which consist of spatially coexisting domains of coherent (synchronized) and incoherent (desynchronized) dynamics, and other complex patterns. In this perspective article we show that partial synchronization scenarios are governed by a delicate interplay of local dynamics and network topology. Our focus is in particular on applications of brain dynamics like unihemispheric sleep and epileptic seizure.

scholarly journals Partial synchronization in empirical brain networks as a model for unihemispheric sleep

2019 ◽  
Vol 126 (5) ◽  
pp. 50007 ◽  
Author(s):  
Lukas Ramlow ◽  
Jakub Sawicki ◽  
Anna Zakharova ◽  
Jaroslav Hlinka ◽  
Jens Christian Claussen ◽  
...  
Keyword(s):  
Brain Networks ◽  
Partial Synchronization ◽  
Unihemispheric Sleep

Chimera states in neuronal networks with time delay and electromagnetic induction

2018 ◽  
Vol 93 (3) ◽  
pp. 1695-1704 ◽  
Author(s):  
Changhai Tian ◽  
Liang Cao ◽  
Hongjie Bi ◽  
Kesheng Xu ◽  
Zonghua Liu
Keyword(s):  
Time Delay ◽  
Electromagnetic Induction ◽  
Neuronal Networks ◽  
Chimera States

scholarly journals Chimera states in brain networks: Empirical neural vs. modular fractal connectivity

2018 ◽  
Vol 28 (4) ◽  
pp. 045112 ◽  
Author(s):  
Teresa Chouzouris ◽  
Iryna Omelchenko ◽  
Anna Zakharova ◽  
Jaroslav Hlinka ◽  
Premysl Jiruska ◽  
...  
Keyword(s):  
Brain Networks ◽  
Chimera States

Synaptic signal streams generated by ex vivo neuronal networks contain non‐random, complex patterns

2014 ◽  
Vol 38 (1) ◽  
pp. 184-194 ◽  
Author(s):  
Sangmook Lee ◽  
Jill M. Zemianek ◽  
Abraham Shultz ◽  
Anh Vo ◽  
Ben Y. Maron ◽  
...  
Keyword(s):  
Neuronal Networks ◽  
Ex Vivo ◽  
Random Complex ◽  
Complex Patterns

scholarly journals Spiral wave chimera states in regular and fractal neuronal networks

2020 ◽  
Vol 2 (1) ◽  
pp. 015006
Author(s):  
Moises S Santos ◽  
Paulo R Protachevicz ◽  
Iberê L Caldas ◽  
Kelly C Iarosz ◽  
Ricardo L Viana ◽  
...  
Keyword(s):  
Neuronal Networks ◽  
Spiral Wave ◽  
Chimera States

scholarly journals Spatial and temporal autocorrelation weave human brain networks

2021 ◽  
Author(s):  
Maxwell Shinn ◽  
Amber Hu ◽  
Laurel Turner ◽  
Stephanie Noble ◽  
Sophie Achard ◽  
...  
Keyword(s):  
Network Topology ◽  
Healthy Aging ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Brain Networks ◽  
Brain Network ◽  
Temporal Autocorrelation ◽  
Activity Networks ◽  
Graph Properties ◽  
Test Retest Reliability

Correlations are a basic object of analysis across neuroscience, but multivariate patterns of correlations can be difficult to interpret. For example, correlations are fundamental to understanding timeseries derived from resting-state functional magnetic resonance imaging (rs-fMRI), a proxy of brain activity. Networks constructed from regional correlations in rs-fMRI timeseries are often interpreted as brain connectivity, yet the links between brain networks and neurobiology have until now been largely speculative. Here, we show that the topology of rs-fMRI brain networks is caused by the spatial and temporal autocorrelation of the timeseries used to construct them. Spatial and temporal autocorrelation show high test-retest reliability, and are correlated with popular measures of network topology. A generative model of spatially and temporally autocorrelated timeseries exhibits similar network topology to brain networks, and when fit to individual subjects, it captures near the reliability limit of subject and regional variation. We demonstrate why spatial and temporal autocorrelation induce network structure, and highlight their ability to link graph properties to neurobiology during healthy aging. These results offer a reductionistic account of brain network complexity, explaining characteristic patterns in brain networks using timeseries statistics.

scholarly journals General Relationship of Global Topology, Local Dynamics, and Directionality in Large-Scale Brain Networks

2015 ◽  
Vol 11 (4) ◽  
pp. e1004225 ◽  
Author(s):  
Joon-Young Moon ◽  
UnCheol Lee ◽  
Stefanie Blain-Moraes ◽  
George A. Mashour
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
General Relationship ◽  
Local Dynamics ◽  
Global Topology ◽  
Relationship Of

scholarly journals Neuron particles capture network topology and behavior from single units

2021 ◽  
Author(s):  
Gaurav Gupta ◽  
Justin Rhodes ◽  
Roozbeh Kiani ◽  
Paul Bogdan
Keyword(s):  
Network Topology ◽  
Statistical Physics ◽  
Neuronal Networks ◽  
Spike Trains ◽  
Particle Model ◽  
Causal Information ◽  
Brain Functions ◽  
Neuronal Spike Trains ◽  
And Behavior ◽  
Spiking Activity

AbstractWhile networks of neurons, glia and vascular systems enable and support brain functions, to date, mathematical tools to decode network dynamics and structure from very scarce and partially observed neuronal spiking behavior remain underdeveloped. Large neuronal networks contribute to the intrinsic neuron transfer function and observed neuronal spike trains encoding complex causal information processing, yet how this emerging causal fractal memory in the spike trains relates to the network topology is not fully understood. Towards this end, we propose a novel statistical physics inspired neuron particle model that captures the causal information flow and processing features of neuronal spiking activity. Relying on synthetic comprehensive simulations and real-world neuronal spiking activity analysis, the proposed fractional order operators governing the neuronal spiking dynamics provide insights into the memory and scale of the spike trains as well as information about the topological properties of the underlying neuronal networks. Lastly, the proposed model exhibits superior predictions of animal behavior during multiple cognitive tasks.

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