scholarly journals Optimally controlling the human connectome: the role of network topology

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Richard F. Betzel ◽  
Shi Gu ◽  
John D. Medaglia ◽  
Fabio Pasqualetti ◽  
Danielle S. Bassett
Keyword(s):  
Network Topology

scholarly journals On Synchronizing Coupled Retinogeniculocortical Pathways: A Toy Model

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
B. L. Mayer ◽  
L. H. A. Monteiro
Keyword(s):  
Neural Network ◽  
Visual Cortex ◽  
Corpus Callosum ◽  
Neuronal Activity ◽  
Network Topology ◽  
Cerebral Hemisphere ◽  
State Transition ◽  
Regular Lattice ◽  
Visual Cortices

A Newman-Watts graph is formed by including random links in a regular lattice. Here, the emergence of synchronization in coupled Newman-Watts graphs is studied. The whole neural network is considered as a toy model of mammalian visual pathways. It is composed by four coupled graphs, in which a coupled pair represents the lateral geniculate nucleus and the visual cortex of a cerebral hemisphere. The hemispheres communicate with each other through a coupling between the graphs representing the visual cortices. This coupling makes the role of the corpus callosum. The state transition of neurons, supposed to be the nodes of the graphs, occurs in discrete time and it follows a set of deterministic rules. From periodic stimuli coming from the retina, the neuronal activity of the whole network is numerically computed. The goal is to find out how the values of the parameters related to the network topology affect the synchronization among the four graphs.

scholarly journals Role of network topology in the synchronization of power systems

2012 ◽  
Vol 85 (7) ◽  
Author(s):  
S. Lozano ◽  
L. Buzna ◽  
A. Díaz-Guilera
Keyword(s):  
Power Systems ◽  
Network Topology

scholarly journals The Role of Brain Connectome Imaging in the Estimation of Depressive Relapse Risk

2018 ◽  
Vol 190 (11) ◽  
pp. 1036-1043 ◽  
Author(s):  
Felix Brandl ◽  
Chun Meng ◽  
Claus Zimmer ◽  
Christian Sorg
Keyword(s):  
Network Topology ◽  
Relapse Risk ◽  
Functional Connections ◽  
Brain Connectome ◽  
Current State ◽  
Potential Biomarker ◽  
Key Points ◽  
State Of Research ◽  
Depressive Episodes

Background About two-thirds of all patients with major depressive disorder (MDD) suffer from depressive relapse, the mechanisms of which are still poorly understood. In recent years, analyses of the brain’s connectome have increasingly been employed to identify potential biomarkers of depressive relapse. The term “connectome” refers to the map of all structural or functional connections in the brain. It can be investigated by structural or functional magnetic resonance imaging followed by graph theory-based analysis to characterize network topology on the global and regional level. Methods This review is based on a selective literature search in PubMed representing the current state of research, as well as on an already published study which was awarded the Promotionspreis of the Deutsche Röntgengesellschaft. Results and Conclusion Numerous studies point to altered network topology, e. g., of default-mode network and striatum, as being crucial for the pathophysiology of MDD. Our group was able to show that striatal centrality (or hubness) is associated with the number of depressive episodes, which is one of the best predictors for depressive relapse. These data suggest aberrant striatal network topology as a potential biomarker for depressive relapse risk. The translation of these promising findings into clinical routine diagnostics is promoted by several methodological advantages, while some unresolved issues still hinder this process. Key points  Citation Format

Sub-critical crack growth in silicate glasses: Role of network topology

2015 ◽  
Vol 107 (14) ◽  
pp. 141901 ◽  
Author(s):  
Morten M. Smedskjaer ◽  
Mathieu Bauchy
Keyword(s):  
Crack Growth ◽  
Network Topology ◽  
Silicate Glasses ◽  
Critical Crack

scholarly journals Connective Core Structures in Cognitive Networks: the Role of Hubs

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 961 ◽  
Author(s):  
Carlos Arruda Arruda Baltazar ◽  
Maria Isabel Barros Guinle ◽  
Cora Jirschik Caron ◽  
Edson Amaro ◽  
Birajara Soares Machado
Keyword(s):  
Complex Network ◽  
Network Topology ◽  
Information Integration ◽  
Effective Connectivity ◽  
Cognitive Networks ◽  
Invariant Structure ◽  
Network Effectiveness ◽  
Efficient Information ◽  
Behavioral States

Complex network analysis applied to the resting brain has shown that sets of highly interconnected networks with coherent activity may support a default mode of brain function within a global workspace. Perceptual processing of environmental stimuli induces architectural changes in network topology with higher specialized modules. Evidence shows that during cognitive tasks, network topology is reconfigured and information is broadcast from modular processors to a connective core, promoting efficient information integration. In this paper, we explored how the brain adapts its effective connectivity within the connective core and across behavioral states. We used complex network metrics to identify hubs and proposed a method of classification based on the effective connectivity patterns of information flow. Finally, we interpreted the role of the connective core and each type of hub on the network effectiveness. We also calculated the complexity of electroencephalography microstate sequences across different tasks. We observed that divergent hubs contribute significantly to the network effectiveness and that part of this contribution persists across behavioral states, forming an invariant structure. Moreover, we found that a large quantity of multiple types of hubs may be associated with transitions of functional networks.

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