Neural decoding of visual stimuli varies with fluctuations in global network efficiency

AbstractPublic transport networks (PTNs) are critical in populated and rapidly densifying cities such as Hong Kong, Beijing, Shanghai, Mumbai, and Tokyo. Public transportation plays an indispensable role in urban resilience with an integrated, complex, and dynamically changeable network structure. Consequently, identifying and quantifying node criticality in complex PTNs is of great practical significance to improve network robustness from damage. Despite the proposition of various node criticality criteria to address this problem, few succeeded in more comprehensive aspects. Therefore, this paper presents an efficient and thorough ranking method, that is, entropy weight method (EWM)–technology for order preference by similarity to an ideal solution (TOPSIS), named EWM–TOPSIS, to evaluate node criticality by taking into account various node features in complex networks. Then we demonstrate it on the Mass Transit Railway (MTR) in Hong Kong by removing and recovering the top k critical nodes in descending order to compare the effectiveness of degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), and the proposed EWM–TOPSIS method. Four evaluation indicators, that is, the frequency of nodes with the same ranking (F), the global network efficiency (E), the size of the largest connected component (LCC), and the average path length (APL), are computed to compare the performance of the four methods and measure network robustness under different designed attack and recovery strategies. The results demonstrate that the EWM–TOPSIS method has more obvious advantages than the others, especially in the early stage.

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Decreased Global Network Efficiency in Young Male Smoker: An EEG Study during the Resting State

Frontiers in Psychology ◽

10.3389/fpsyg.2017.01605 ◽

2017 ◽

Vol 8 ◽

Cited By ~ 8

Author(s):

Shaoping Su ◽

Dahua Yu ◽

Jiadong Cheng ◽

Yajing Chen ◽

Xiaohua Zhang ◽

...

Keyword(s):

Resting State ◽

Young Male ◽

Global Network ◽

Network Efficiency ◽

Male Smoker

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Vulnerability Analysis of Link-Weighted Shanghai Metrorail Transit Network

Volume 13: Design, Reliability, Safety, and Risk ◽

10.1115/imece2018-86863 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yanjie Zhang ◽

Yalda Saadat ◽

Dongming Zhang ◽

Bilal M. Ayyub ◽

Hongwei Huang

Keyword(s):

Network Topology ◽

Structural Features ◽

Network Size ◽

Global Network ◽

Interval Length ◽

Network Efficiency ◽

Multiple Characteristic ◽

Shanghai Metro ◽

Metro System ◽

Metro Network

With the degradation of metrorail facilities and the increase in network size, it is urgently needed to perform vulnerability assessment to ensure the safe operation of the metro system. In this paper, a link-weighted network model is proposed by considering the physical interval length between neighboring metro stations as link weight factor. Firstly, the metro network was essentially mapped into a bipartite topological diagram that consists of nodes denoting metro stations and links representing metro routes including any tunnels or bridges. After analyzing the network for its complexity level, it was revealed that the metro network topology can be appropriately constructed by using the Space L method. On this basis, multiple characteristic indexes of the network were calculated to characterize network topology structural features. We then tested the state of Shanghai metro network under different failure scenarios by removing a fraction of nodes from the network. Quantitative vulnerability analyses were conducted according to the change in the topological structure of Shanghai metro network and the change in the corresponding global network efficiency due to disruptions. Finally, both the network efficiency of link-weighted and unweighted Shanghai metro network topology were calculated and compared. This study has identified the vulnerable metro stations, which could provide support for the reasonable resource allocation of maintenance work and the decision-making in emergency treatment after failure. In order to increase the adaptability to emergencies and improve the operational efficiency, it was proposed that during the planning, construction, and operation of the metro system, the management and protection of the vulnerable stations should be given increased attention.

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Altered Brain Functional Network in Subtypes of Parkinson's Disease: A Dynamic Perspective

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.710735 ◽

2021 ◽

Vol 13 ◽

Author(s):

Junlan Zhu ◽

Qiaoling Zeng ◽

Qiao Shi ◽

Jiao Li ◽

Shuwen Dong ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Functional Connectivity ◽

Dynamic Properties ◽

Network Connectivity ◽

Functional Network ◽

Global Network ◽

Network Efficiency ◽

Dominant Phenotype ◽

Window Approach

Background: Parkinson's disease (PD) is a highly heterogeneous disease, especially in the clinical characteristics and prognosis. The PD is divided into two subgroups: tremor-dominant phenotype and non-tremor-dominant phenotype. Previous studies reported abnormal changes between the two PD phenotypes by using the static functional connectivity analysis. However, the dynamic properties of brain networks between the two PD phenotypes are not yet clear. Therefore, we aimed to uncover the dynamic functional network connectivity (dFNC) between the two PD phenotypes at the subnetwork level, focusing on the temporal properties of dFNC and the variability of network efficiency.Methods: We investigated the resting-state functional MRI (fMRI) data from 29 tremor-dominant PD patients (PDTD), 25 non-tremor-dominant PD patients (PDNTD), and 20 healthy controls (HCs). Sliding window approach, k-means clustering, independent component analysis (ICA), and graph theory analysis were applied to analyze the dFNC. Furthermore, the relationship between alterations in the dynamic properties and clinical features was assessed.Results: The dFNC analyses identified four reoccurring states, one of them showing sparse connections (state I). PDTD patients stayed longer time in state I and showed increased FNC between BG and vSMN in state IV. Both PD phenotypes exhibited higher FNC between dSMN and FPN in state II and state III compared with the controls. PDNTD patients showed decreased FNC between BG and FPN but increased FNC in the bilateral FPN compared with both PDTD patients and controls. In addition, PDNTD patients exhibited greater variability in global network efficiency. Tremor scores were positively correlated with dwell time in state I along with increased FNC between BG and vSMN in state IV.Conclusions: This study explores the dFNC between the PDTD and PDNTD patients, which offers new evidence on the abnormal time-varying brain functional connectivity and their network destruction of the two PD phenotypes, and may help better understand the neural substrates underlying different types of PD.

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Disconnectome of the migraine brain: a “connectopathy” model

The Journal of Headache and Pain ◽

10.1186/s10194-021-01315-6 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Marcello Silvestro ◽

Alessandro Tessitore ◽

Giuseppina Caiazzo ◽

Fabrizio Scotto di Clemente ◽

Francesca Trojsi ◽

...

Keyword(s):

Pain Perception ◽

Clustering Coefficient ◽

Global Network ◽

Graph Analysis ◽

Healthy Controls ◽

Mri Imaging ◽

Network Efficiency ◽

Resting State Functional Connectivity ◽

Brain Connectome ◽

The Brain

Abstract Background In the past decades a plethora of studies has been conducted to explore resting-state functional connectivity (RS-FC) of the brain networks in migraine with conflicting results probably due to the variability and susceptibility of signal fluctuations across the course of RS-FC scan. On the other hand, the structural substrates enabling the functional communications among the brain connectome, characterized by higher stability and reproducibility, have not been widely investigated in migraine by means of graph analysis approach. We hypothesize a rearrangement of the brain connectome with an increase of both strength and density of connections between cortical areas specifically involved in pain perception, processing and modulation in migraine patients. Moreover, such connectome rearrangement, inducing an imbalance between the competing parameters of network efficiency and segregation, may underpin a mismatch between energy resources and demand representing the neuronal correlate of the energetically dysfunctional migraine brain. Methods We investigated, using diffusion-weighted MRI imaging tractography-based graph analysis, the graph-topological indices of the brain “connectome”, a set of grey matter regions (nodes) structurally connected by white matter paths (edges) in 94 patients with migraine without aura compared to 91 healthy controls. Results We observed in migraine patients compared to healthy controls: i) higher local and global network efficiency (p < 0.001) and ii) higher local and global clustering coefficient (p < 0.001). Moreover, we found changes in the hubs topology in migraine patients with: i) posterior cingulate cortex and inferior parietal lobule (encompassing the so-called neurolimbic-pain network) assuming the hub role and ii) fronto-orbital cortex, involved in emotional aspects, and visual areas, involved in migraine pathophysiology, losing the hub role. Finally, we found higher connection (edges) probability between cortical nodes involved in pain perception and modulation as well as in cognitive and affective attribution of pain experiences, in migraine patients when compared to healthy controls (p < 0.001). No correlations were found between imaging and clinical parameters of disease severity. Conclusion The imbalance between the need of investing resources to promote network efficiency and the need of minimizing the metabolic cost of wiring probably represents the mechanism underlying migraine patients’ susceptibility to triggers. Such changes in connectome topography suggest an intriguing pathophysiological model of migraine as brain “connectopathy”.

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Neural decoding of visual stimuli varies with fluctuations in global network efficiency

10.1101/107888 ◽

2017 ◽

Author(s):

Luca Cocchi ◽

Yang Zhengyi ◽

Zalesky Andrew ◽

Stelzer Johannes ◽

Luke Hearne ◽

...

Keyword(s):

Neural Activity ◽

Global Network ◽

Network Efficiency ◽

Time Resolved ◽

Sensory Stimuli ◽

Global Efficiency ◽

Low Efficiency ◽

Cortical Regions ◽

Visual Cortical ◽

The Impact

AbstractFunctional magnetic resonance imaging (fMRI) studies have shown that neural activity fluctuates spontaneously between different states of global synchronization over a timescale of several seconds. Such fluctuations generate transient states of high and low correlation across distributed cortical areas. It has been hypothesized that such fluctuations in global efficiency might alter patterns of activity in local neuronal populations elicited by changes in incoming sensory stimuli. To test this prediction, we used a linear decoder to discriminate patterns of neural activity elicited by face and motion stimuli presented periodically while participants underwent time-resolved fMRI. As predicted, decoding was reliably higher during states of high global efficiency than during states of low efficiency, and this difference was evident across both visual and non-visual cortical regions. The results indicate that slow fluctuations in global network efficiency are associated with variations in the pattern of activity across widespread cortical regions responsible for representing distinct categories of visual stimulus. More broadly, the findings highlight the importance of understanding the impact of global fluctuations in functional connectivity on specialised, stimulus driven neural processes.

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A Link Prediction Method Based on Neural Networks

Applied Sciences ◽

10.3390/app11115186 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5186

Author(s):

Keping Li ◽

Shuang Gu ◽

Dongyang Yan

Keyword(s):

Neural Network ◽

Link Prediction ◽

Prediction Models ◽

Prediction Method ◽

Network Evolution ◽

Global Network ◽

Network Efficiency ◽

Structure Reliability ◽

The Neural Network ◽

Network Method

Link prediction to optimize network performance is of great significance in network evolution. Because of the complexity of network systems and the uncertainty of network evolution, it faces many challenges. This paper proposes a new link prediction method based on neural networks trained on scale-free networks as input data, and optimized networks trained by link prediction models as output data. In order to solve the influence of the generalization of the neural network on the experiments, a greedy link pruning strategy is applied. We consider network efficiency and the proposed global network structure reliability as objectives to comprehensively evaluate link prediction performance and the advantages of the neural network method. The experimental results demonstrate that the neural network method generates the optimized networks with better network efficiency and global network structure reliability than the traditional link prediction models.

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Conflicts in Routing and UAV Autonomy

Australian Journal of Telecommunications and the Digital Economy ◽

10.18080/jtde.v6n4.169 ◽

2018 ◽

Vol 6 (4) ◽

pp. 96-108 ◽

Cited By ~ 1

Author(s):

Ogbonnaya Anicho ◽

Philip B Charlesworth ◽

Gurvinder S Baicher ◽

Atulya Nagar

Keyword(s):

Network Architecture ◽

Ad Hoc ◽

Area Coverage ◽

Global Network ◽

Network Efficiency ◽

Aerial Vehicle ◽

Routing Techniques ◽

Stability Issues ◽

Link Availability ◽

The Impact

Routing is very fundamental to the implementation of any networking or communications infrastructure. This paper, therefore, examines the conflicts and relevant considerations for implementing autonomous or self-organising unmanned aerial vehicles (UAVs) for communications area coverage, with particular emphasis on the impact of aerial vehicle autonomy algorithms on routing techniques for such networks. UAV networks can be deployed either as ad-hoc or infrastructure-based solutions. The mobility of UAVs introduces periodic topology changes, impacting link availability and routing paths. This work examines the implications of autonomous coordination of multiple UAVs on routing techniques and network architecture stability. The paper proposes a solution where routing techniques and UAV autonomy algorithms are integrated for improved global network efficiency for both ad-hoc and infrastructure-based scenarios. Integrating UAV autonomy algorithms with routing schemes may be an efficient method to mitigate link/topology stability issues and improve inter-UAV communication and network throughput, a key requirement for UAV networks. The implementation of inter-UAV links using optical, microwave or mmWave transmission is examined in the context of this work. The proposed integration may be crucial for communications coverage, where UAVs provide communications area coverage of a community of mobile or fixed users in either ad-hoc or infrastructure-based modes.

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Disrupted Structural Brain Connectome Is Related to Cognitive Impairment in Patients With Ischemic Leukoaraiosis

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.654750 ◽

2021 ◽

Vol 15 ◽

Author(s):

Tong Lu ◽

Zan Wang ◽

Ying Cui ◽

Jiaying Zhou ◽

Yuancheng Wang ◽

...

Keyword(s):

Cognitive Impairment ◽

Diffusion Tensor ◽

Structural Connectivity ◽

Mean Diffusivity ◽

Global Network ◽

Local Network ◽

Network Efficiency ◽

Brain Connectome ◽

Structural Connectome ◽

The Relationship

Ischemic leukoaraiosis (ILA) is related to cognitive impairment and vascular dementia in the elderly. One possible mechanism could be the disruption of white matter (WM) tracts and network function that connect distributed brain regions involved in cognition. The purpose of this study was to investigate the relationship between structural connectome and cognitive functions in ILA patients. A total of 89 patients with ILA (Fazekas score ≥ 3) and 90 healthy controls (HCs) underwent comprehensive neuropsychological examinations and diffusion tensor imaging scans. The tract-based spatial statistics approach was employed to investigate the WM integrity. Graph theoretical analysis was further applied to construct the topological architecture of the structural connectome in ILA patients. Partial correlation analysis was used to investigate the relationships between network measures and cognitive performances in the ILA group. Compared with HCs, the ILA patients showed widespread WM integrity disruptions. The ILA group displayed increased characteristic path length (Lp) and decreased global network efficiency at the level of the whole brain relative to HCs, and reduced nodal efficiencies, predominantly in the frontal–subcortical and limbic system regions. Furthermore, these structural connectomic alterations were associated with cognitive impairment in ILA patients. The association between WM changes (i.e., fractional anisotropy and mean diffusivity measures) and cognitive function was mediated by the structural connectivity measures (i.e., local network efficiency and Lp). In conclusion, cognitive impairment in ILA patients is related to microstructural disruption of multiple WM fibers and topological disorganization of structural networks, which have implications in understanding the relationship between ILA and the possible attendant cognitive impairment.

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Network Path Convergence Shapes Low-Level Processing in the Visual Cortex

Frontiers in Systems Neuroscience ◽

10.3389/fnsys.2021.645709 ◽

2021 ◽

Vol 15 ◽

Author(s):

Bálint Varga ◽

Bettina Soós ◽

Balázs Jákli ◽

Eszter Bálint ◽

Zoltán Somogyvári ◽

...

Keyword(s):

Visual Cortex ◽

Large Scale ◽

Global Network ◽

Network Efficiency ◽

Beta Band ◽

Low Level ◽

Cortical Areas ◽

Edge Betweenness ◽

Highly Correlated ◽

Visual Cortical

Hierarchical counterstream via feedforward and feedback interactions is a major organizing principle of the cerebral cortex. The counterstream, as a topological feature of the network of cortical areas, is captured by the convergence and divergence of paths through directed links. So defined, the convergence degree (CD) reveals the reciprocal nature of forward and backward connections, and also hierarchically relevant integrative properties of areas through their inward and outward connections. We asked if topology shapes large-scale cortical functioning by studying the role of CD in network resilience and Granger causal coupling in a model of hierarchical network dynamics. Our results indicate that topological synchronizability is highly vulnerable to attacking edges based on CD, while global network efficiency depends mostly on edge betweenness, a measure of the connectedness of a link. Furthermore, similar to anatomical hierarchy determined by the laminar distribution of connections, CD highly correlated with causal coupling in feedforward gamma, and feedback alpha-beta band synchronizations in a well-studied subnetwork, including low-level visual cortical areas. In contrast, causal coupling did not correlate with edge betweenness. Considering the entire network, the CD-based hierarchy correlated well with both the anatomical and functional hierarchy for low-level areas that are far apart in the hierarchy. Conversely, in a large part of the anatomical network where hierarchical distances are small between the areas, the correlations were not significant. These findings suggest that CD-based and functional hierarchies are interrelated in low-level processing in the visual cortex. Our results are consistent with the idea that the interplay of multiple hierarchical features forms the basis of flexible functional cortical interactions.

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