scholarly journals Spatial multi-scaled chimera states of cerebral cortex network and its inherent structure-dynamics relationship in human brain

2020 ◽  
Author(s):  
S Huo ◽  
C Tian ◽  
M Zheng ◽  
S Guan ◽  
C S Zhou ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Human Brain ◽  
Spatial Scales ◽  
Brain Network ◽  
Chimera States ◽  
Hierarchical Trees ◽  
Neural Mass ◽  
Functional Patterns ◽  
New Feature ◽  
Global And Local

Abstract Human cerebral cortex displays various dynamics patterns under different states, however the mechanism how such diverse patterns can be supported by the underlying brain network is still not well understood. Human brain has a unique network structure with different regions of interesting to perform cognitive tasks. Using coupled neural mass oscillators on human cortical network and paying attention to both global and local regions, we observe a new feature of chimera states with multiple spatial scales and a positive correlation between the synchronization preference of local region and the degree of symmetry of the connectivity of the region in the network. Further, we use the concept of effective symmetry in the network to build structural and dynamical hierarchical trees and find a close matching between them. These results help to explain the multiple brain rhythms observed in experiments and suggest a generic principle for complex brain network as a structure substrate to support diverse functional patterns.

scholarly journals A two-layered brain network model and its chimera state

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ling Kang ◽  
Changhai Tian ◽  
Siyu Huo ◽  
Zonghua Liu
Keyword(s):  
Cerebral Cortex ◽  
Network Model ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Coupling Phase ◽  
Chimera States ◽  
Chimera State ◽  
Brain Functions ◽  
Coupling Strengths ◽  
Left And Right

Abstract Based on the data of cerebral cortex, we present a two-layered brain network model of coupled neurons where the two layers represent the left and right hemispheres of cerebral cortex, respectively, and the links between the two layers represent the inter-couplings through the corpus callosum. By this model we show that abundant patterns of synchronization can be observed, especially the chimera state, depending on the parameters of system such as the coupling strengths and coupling phase. Further, we extend the model to a more general two-layered network to better understand the mechanism of the observed patterns, where each hemisphere of cerebral cortex is replaced by a highly clustered subnetwork. We find that the number of inter-couplings is another key parameter for the emergence of chimera states. Thus, the chimera states come from a matching between the structure parameters such as the number of inter-couplings and clustering coefficient etc and the dynamics parameters such as the intra-, inter-coupling strengths and coupling phase etc. A brief theoretical analysis is provided to explain the borderline of synchronization. These findings may provide helpful clues to understand the mechanism of brain functions.

scholarly journals A Comparative Study of Theoretical Graph Models for Characterizing Structural Networks of Human Brain

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaojin Li ◽  
Xintao Hu ◽  
Changfeng Jin ◽  
Junwei Han ◽  
Tianming Liu ◽  
...  
Keyword(s):  
Comparative Study ◽  
Human Brain ◽  
Brain Networks ◽  
Brain Network ◽  
Graph Models ◽  
Graph Properties ◽  
Local Graph ◽  
Structural Networks ◽  
Global And Local ◽  
Structural Brain

Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs) are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL) to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI) data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY) and scale-free gene duplication model (SF-GD), that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network.

scholarly journals Stochastic synchronization of dynamics on the human connectome

2020 ◽  
Author(s):  
James C. Pang ◽  
Leonardo L. Gollo ◽  
James A. Roberts
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Brain Network ◽  
Scale Model ◽  
Stochastic Perturbations ◽  
Stochastic Synchronization ◽  
Large Scale Model ◽  
Synchronization Effect ◽  
Global And Local ◽  
The Brain

AbstractSynchronization is a collective mechanism by which oscillatory networks achieve their functions. Factors driving synchronization include the network’s topological and dynamical properties. However, how these factors drive the emergence of synchronization in the presence of potentially disruptive external inputs like stochastic perturbations is not well understood, particularly for real-world systems such as the human brain. Here, we aim to systematically address this problem using a large-scale model of the human brain network (i.e., the human connectome). The results show that the model can produce complex synchronization patterns transitioning between incoherent and coherent states. When nodes in the network are coupled at some critical strength, a counterintuitive phenomenon emerges where the addition of noise increases the synchronization of global and local dynamics, with structural hub nodes benefiting the most. This stochastic synchronization effect is found to be driven by the intrinsic hierarchy of neural timescales of the brain and the heterogeneous complex topology of the connectome. Moreover, the effect coincides with clustering of node phases and node frequencies and strengthening of the functional connectivity of some of the connectome’s subnetworks. Overall, the work provides broad theoretical insights into the emergence and mechanisms of stochastic synchronization, highlighting its putative contribution in achieving network integration underpinning brain function.

scholarly journals Cognitive chimera states in human brain networks

2019 ◽  
Vol 5 (4) ◽  
pp. eaau8535 ◽  
Author(s):  
Kanika Bansal ◽  
Javier O. Garcia ◽  
Steven H. Tompson ◽  
Timothy Verstynen ◽  
Jean M. Vettel ◽  
...  
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Network ◽  
Brain Dynamics ◽  
Cognitive Systems ◽  
Regional Variability ◽  
Chimera States ◽  
Regional Brain ◽  
The Impact

The human brain is a complex dynamical system, and how cognition emerges from spatiotemporal patterns of regional brain activity remains an open question. As different regions dynamically interact to perform cognitive tasks, variable patterns of partial synchrony can be observed, forming chimera states. We propose that the spatial patterning of these states plays a fundamental role in the cognitive organization of the brain and present a cognitively informed, chimera-based framework to explore how large-scale brain architecture affects brain dynamics and function. Using personalized brain network models, we systematically study how regional brain stimulation produces different patterns of synchronization across predefined cognitive systems. We analyze these emergent patterns within our framework to understand the impact of subject-specific and region-specific structural variability on brain dynamics. Our results suggest a classification of cognitive systems into four groups with differing levels of subject and regional variability that reflect their different functional roles.

scholarly journals Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain

2016 ◽  
Vol 113 (4) ◽  
pp. E469-E478 ◽  
Author(s):  
Fenna M. Krienen ◽  
B. T. Thomas Yeo ◽  
Tian Ge ◽  
Randy L. Buckner ◽  
Chet C. Sherwood
Keyword(s):  
Cerebral Cortex ◽  
Human Brain ◽  
Long Range ◽  
Network Architecture ◽  
Large Scale ◽  
Nonhuman Primates ◽  
Brain Network ◽  
Molecular Architecture ◽  
Gene Sets ◽  
Molecular Profiles

The human brain is patterned with disproportionately large, distributed cerebral networks that connect multiple association zones in the frontal, temporal, and parietal lobes. The expansion of the cortical surface, along with the emergence of long-range connectivity networks, may be reflected in changes to the underlying molecular architecture. Using the Allen Institute’s human brain transcriptional atlas, we demonstrate that genes particularly enriched in supragranular layers of the human cerebral cortex relative to mouse distinguish major cortical classes. The topography of transcriptional expression reflects large-scale brain network organization consistent with estimates from functional connectivity MRI and anatomical tracing in nonhuman primates. Microarray expression data for genes preferentially expressed in human upper layers (II/III), but enriched only in lower layers (V/VI) of mouse, were cross-correlated to identify molecular profiles across the cerebral cortex of postmortem human brains (n = 6). Unimodal sensory and motor zones have similar molecular profiles, despite being distributed across the cortical mantle. Sensory/motor profiles were anticorrelated with paralimbic and certain distributed association network profiles. Tests of alternative gene sets did not consistently distinguish sensory and motor regions from paralimbic and association regions: (i) genes enriched in supragranular layers in both humans and mice, (ii) genes cortically enriched in humans relative to nonhuman primates, (iii) genes related to connectivity in rodents, (iv) genes associated with human and mouse connectivity, and (v) 1,454 gene sets curated from known gene ontologies. Molecular innovations of upper cortical layers may be an important component in the evolution of long-range corticocortical projections.

Humans and the Environment

2013 ◽  
Keyword(s):  
Spatial Scales ◽  
Empirical Studies ◽  
Human Action ◽  
Cultural Practice ◽  
Environmental Archaeology ◽  
Archaeological Data ◽  
Temporal And Spatial ◽  
Global And Local ◽  
The Relationship ◽  
The Way

The environment has always been a central concept for archaeologists and, although it has been conceived in many ways, its role in archaeological explanation has fluctuated from a mere backdrop to human action, to a primary factor in the understanding of society and social change. Archaeology also has a unique position as its base of interest places it temporally between geological and ethnographic timescales, spatially between global and local dimensions, and epistemologically between empirical studies of environmental change and more heuristic studies of cultural practice. Drawing on data from across the globe at a variety of temporal and spatial scales, this volume resituates the way in which archaeologists use and apply the concept of the environment. Each chapter critically explores the potential for archaeological data and practice to contribute to modern environmental issues, including problems of climate change and environmental degradation. Overall the volume covers four basic themes: archaeological approaches to the way in which both scientists and locals conceive of the relationship between humans and their environment, applied environmental archaeology, the archaeology of disaster, and new interdisciplinary directions.The volume will be of interest to students and established archaeologists, as well as practitioners from a range of applied disciplines.

scholarly journals Dynamics of the human brain network revealed by time-frequency effective connectivity in fNIRS

2017 ◽  
Vol 8 (11) ◽  
pp. 5326 ◽  
Author(s):  
Grégoire Vergotte ◽  
Kjerstin Torre ◽  
Venkata Chaitanya Chirumamilla ◽  
Abdul Rauf Anwar ◽  
Sergiu Groppa ◽  
...  
Keyword(s):  
Human Brain ◽  
Effective Connectivity ◽  
Brain Network ◽  
Time Frequency

scholarly journals Broadband Criticality of Human Brain Network Synchronization

2009 ◽  
Vol 5 (3) ◽  
pp. e1000314 ◽  
Author(s):  
Manfred G. Kitzbichler ◽  
Marie L. Smith ◽  
Søren R. Christensen ◽  
Ed Bullmore
Keyword(s):  
Human Brain ◽  
Brain Network ◽  
Network Synchronization

scholarly journals Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

2015 ◽  
Vol 9 ◽  
Author(s):  
Xerxes D. Arsiwalla ◽  
Riccardo Zucca ◽  
Alberto Betella ◽  
Enrique Martinez ◽  
David Dalmazzo ◽  
...  
Keyword(s):  
Human Brain ◽  
Real Time ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network ◽  
Large Scale Simulation ◽  
Time Interaction

scholarly journals Canard solutions in neural mass models: consequences on critical regimes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elif Köksal Ersöz ◽  
Fabrice Wendling
Keyword(s):  
Temporal Dynamics ◽  
Spatial Scales ◽  
Singular Perturbation Theory ◽  
Geometric Singular Perturbation Theory ◽  
Critical Transitions ◽  
Intracerebral Eeg ◽  
Neural Mass ◽  
Mesoscopic Level ◽  
Eeg Recordings ◽  
Physiological Background

AbstractMathematical models at multiple temporal and spatial scales can unveil the fundamental mechanisms of critical transitions in brain activities. Neural mass models (NMMs) consider the average temporal dynamics of interconnected neuronal subpopulations without explicitly representing the underlying cellular activity. The mesoscopic level offered by the neural mass formulation has been used to model electroencephalographic (EEG) recordings and to investigate various cerebral mechanisms, such as the generation of physiological and pathological brain activities. In this work, we consider a NMM widely accepted in the context of epilepsy, which includes four interacting neuronal subpopulations with different synaptic kinetics. Due to the resulting three-time-scale structure, the model yields complex oscillations of relaxation and bursting types. By applying the principles of geometric singular perturbation theory, we unveil the existence of the canard solutions and detail how they organize the complex oscillations and excitability properties of the model. In particular, we show that boundaries between pathological epileptic discharges and physiological background activity are determined by the canard solutions. Finally we report the existence of canard-mediated small-amplitude frequency-specific oscillations in simulated local field potentials for decreased inhibition conditions. Interestingly, such oscillations are actually observed in intracerebral EEG signals recorded in epileptic patients during pre-ictal periods, close to seizure onsets.

Sign in / Sign up

Export Citation Format

Share Document