Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience.

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On enabling real-time large-scale network simulation in GENI: the PrimoGENI approach

Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques ◽

10.4108/icst.simutools2010.8636 ◽

2010 ◽

Author(s):

Miguel A. Erazo ◽

Jason Liu

Keyword(s):

Real Time ◽

Large Scale ◽

Network Simulation ◽

Large Scale Network ◽

Scale Network

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A Novel Predication Approach for Network Security Situation Inspired by Immunity

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.20-23.849 ◽

2010 ◽

Vol 20-23 ◽

pp. 849-855 ◽

Cited By ~ 1

Author(s):

Yuan Quan Shi ◽

Tao Li ◽

Wen Chen ◽

Rui Rui Zhang

Keyword(s):

Network Security ◽

Large Scale ◽

Security Attacks ◽

Evolution Operators ◽

Large Scale Network ◽

Novel Approach ◽

Formal Definitions ◽

The Future ◽

Future Situation ◽

Scale Network

To effectively prevent large-scale network security attacks, a novel Predication Approach for Network Security Situation inspired by Immunity (PANSSI) is proposed. In this predication approach, the concepts and formal definitions of antigen and antibody in the network security situation predication domain are given; meanwhile, the mathematical models of some antibody evolution operators being related to PANSSI are exhibited. By analyzing time series and computing the affinity between antigen and antibody in artificial immune system, network security situation predication model is established, and then the future situation of network security attacks is predicted by it. Experimental results prove that PANSSI can forecast the future network security situation real-timely and correctly, and provides a novel approach for network security situation predication.

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Subnet Broadcast Polling Algorithm of Network Management Based on SNMP

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.347-350.2915 ◽

2013 ◽

Vol 347-350 ◽

pp. 2915-2918

Author(s):

Mei Gen Huang ◽

Zhi Lei Wang

Keyword(s):

Network Management ◽

Real Time ◽

Management System ◽

Large Scale ◽

Current Data ◽

Work Efficiency ◽

Network Management System ◽

Large Scale Network ◽

Scale Network ◽

Network Device

The current data collection of a network device in the network management system exists low real-time and long polling cycle, this paper proposes a subnet broadcast algorithm based on SNMP. The algorithm introduces the idea of Subnet division and broadcasting, when polling, the algorithm polled network devices by sending a SNMP data broadcast packet to each subnet, which reduced the polling packets, shortened the polling cycle and lightened the burden of management station, thus the proposed algorithm improves real-time and work efficiency for the large-scale network management system.

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Dynamic traffic assignment integration with real-time ramp metering for large-scale network management

2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) ◽

10.1109/mtits.2017.8005716 ◽

2017 ◽

Author(s):

Minha Lee ◽

Zheng Zhu ◽

Chenfeng Xiong ◽

Lei Zhang

Keyword(s):

Network Management ◽

Real Time ◽

Large Scale ◽

Traffic Assignment ◽

Dynamic Traffic Assignment ◽

Ramp Metering ◽

Dynamic Traffic ◽

Large Scale Network ◽

Scale Network

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Synthesizing Brain-network-inspired Interconnections for Large-scale Network-on-chips

ACM Transactions on Design Automation of Electronic Systems ◽

10.1145/3480961 ◽

2022 ◽

Vol 27 (1) ◽

pp. 1-30

Author(s):

Mengke Ge ◽

Xiaobing Ni ◽

Xu Qi ◽

Song Chen ◽

Jinglei Huang ◽

...

Keyword(s):

Large Scale ◽

Small World ◽

Brain Network ◽

Graph Processing ◽

Hop Count ◽

Scale Free ◽

Large Scale Network ◽

Average Latency ◽

Scale Network ◽

The Brain

Brain network is a large-scale complex network with scale-free, small-world, and modularity properties, which largely supports this high-efficiency massive system. In this article, we propose to synthesize brain-network-inspired interconnections for large-scale network-on-chips. First, we propose a method to generate brain-network-inspired topologies with limited scale-free and power-law small-world properties, which have a low total link length and extremely low average hop count approximately proportional to the logarithm of the network size. In addition, given the large-scale applications, considering the modularity of the brain-network-inspired topologies, we present an application mapping method, including task mapping and deterministic deadlock-free routing, to minimize the power consumption and hop count. Finally, a cycle-accurate simulator BookSim2 is used to validate the architecture performance with different synthetic traffic patterns and large-scale test cases, including real-world communication networks for the graph processing application. Experiments show that, compared with other topologies and methods, the brain-network-inspired network-on-chips (NoCs) generated by the proposed method present significantly lower average hop count and lower average latency. Especially in graph processing applications with a power-law and tightly coupled inter-core communication, the brain-network-inspired NoC has up to 70% lower average hop count and 75% lower average latency than mesh-based NoCs.

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Local vulnerability and global connectivity jointly shape neurodegenerative disease propagation

10.1101/449199 ◽

2018 ◽

Cited By ~ 3

Author(s):

Ying-Qiu Zheng ◽

Yu Zhang ◽

Yvonne Yau ◽

Yahar Zeighami ◽

Kevin Larcher ◽

...

Keyword(s):

Gene Expression ◽

Large Scale ◽

Structural Connectivity ◽

Brain Network ◽

Subcortical Structures ◽

Neurodegenerative Process ◽

Axonal Projections ◽

Large Scale Network ◽

Regional Vulnerability ◽

Scale Network

AbstractIt is becoming increasingly clear that brain network organization shapes the course and expression of neurodegenerative diseases. Parkinson’s disease (PD) is marked by progressive spread of atrophy from the midbrain to subcortical structures and eventually, to the cerebral cortex. Recent discoveries suggest that the neurodegenerative process involves the misfolding and prion-like propagation of endogenous α-synuclein via axonal projections. However, the mechanisms that translate local “synucleinopathy” to large-scale network dysfunction and atrophy remain unknown. Here we use an agent-based epidemic spreading model to integrate structural connectivity, functional connectivity and gene expression, and to predict sequential volume loss due to neurodegeneration. The dynamic model replicates the spatial and temporal patterning of empirical atrophy in PD and implicates the substantia nigra as the disease epicenter. We reveal a significant role for both connectome topology and geometry in shaping the distribution of atrophy. The model also demonstrates that SNCA and GBA transcription influence α-synuclein concentration and local regional vulnerability. Functional co-activation further amplifies the course set by connectome architecture and gene expression. Altogether, these results support the theory that the progression of PD is a multifactorial process that depends on both cell-to-cell spreading of misfolded proteins and regional vulnerability.

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An Efficient Approach For the Nodal Water Demand Estimation in Large-Scale Water Distribution Systems

10.21203/rs.3.rs-818509/v1 ◽

2021 ◽

Author(s):

Shipeng Chu ◽

Tuqiao Zhang ◽

Xinhong Zhou ◽

Tingchao Yu ◽

Yu Shao

Keyword(s):

Real Time ◽

Water Demand ◽

Water Distribution ◽

Large Scale ◽

Jacobian Matrix ◽

Hessian Matrix ◽

Demand Estimation ◽

Matrix Inversion ◽

Large Scale Network ◽

Scale Network

Abstract Real-time modeling of the water distribution system (WDS) is a critical step for the control and operation of such systems. The nodal water demand as the most important time-varying parameter must be estimated in real-time. The computational burden of nodal water demand estimation is intensive, leading to inefficiency for the modeling of the large-scale network. The Jacobian matrix computation and Hessian matrix inversion are the processes that dominate the main computation time. To address this problem, an approach to shorten the computational time for the real-time demand estimation in the large-scale network is proposed. The approach can efficiently compute the Jacobian matrix based on solving a system of linear equations, and a Hessian matrix inversion method based on matrix partition and Iterative Woodbury-Matrix-Identity Formula is proposed. The developed approach is applied to a large-scale network, of which the number of nodal water demand is 12523, and the number of measurements ranging from 10 to 2000. Results show that the time consumptions of both Jacobian computation and Hessian matrix inversion are significantly shortened compared with the existing approach.

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Netbench – large-scale network device testing with real-life traffic patterns

EPJ Web of Conferences ◽

10.1051/epjconf/201921408005 ◽

2019 ◽

Vol 214 ◽

pp. 08005

Author(s):

Stefan Nicolae Stancu ◽

Adam Lukasz Krajewski ◽

Mattia Cadeddu ◽

Marta Antosik ◽

Bernd Panzer-Steinde

Keyword(s):

Large Scale ◽

Network Performance ◽

Real Life ◽

Traffic Patterns ◽

Correct Operation ◽

Testing Framework ◽

Network Testing ◽

Large Scale Network ◽

Scale Network ◽

Large Scale Tests

Network performance is key to the correct operation of any modern data centre infrastructure or data acquisition (DAQ) system. Hence, it is crucial to ensure the devices employed in the network are carefully selected to meet the required needs. Specialized commercial testers implement standardized tests [1, 2], which benchmark the performance of network devices under reproducible, yet artificial conditions. Netbench is a network-testing framework, relying on commodity servers and NICs, that enables the evaluation of network devices performance for handling traffic-patterns that closely resemble real-life usage, at a reasonably affordable price. We will present the architecture of the Netbench framework, its capabilities and how they complement the use of specialized commercial testers (e.g. competing TCP flows that create temporary congestion provide a good benchmark of buffering capabilities in real-life scenarios). Last but not least, we will describe how CERN used Netbench for performing large scale tests with partial-mesh and full-mesh TCP flows [3], an essential validation point during its most recent high-end routers call for tender.

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