Unveiling the rich-club phenomenon in urban mobility networks through the spatiotemporal characteristics of passenger flow

2021 ◽  
Vol 584 ◽  
pp. 126377
Author(s):  
Yifan Zhang ◽  
S. Thomas Ng
Keyword(s):  
Urban Mobility ◽  
Rich Club ◽  
Passenger Flow ◽  
Spatiotemporal Characteristics ◽  
The Rich

scholarly journals Effect of Multishell Diffusion MRI Acquisition Strategy and Parcellation Scale on Rich-Club Organization of Human Brain Structural Networks

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 970
Author(s):  
Maedeh Khalilian ◽  
Kamran Kazemi ◽  
Mahshid Fouladivanda ◽  
Malek Makki ◽  
Mohammad Sadegh Helfroush ◽  
...  
Keyword(s):  
Human Brain ◽  
Clustering Coefficient ◽  
Quality Data ◽  
B Values ◽  
Rich Club ◽  
Global Efficiency ◽  
Acquisition Strategy ◽  
The Rich ◽  
Structural Networks ◽  
Single Shell

The majority of network studies of human brain structural connectivity are based on single-shell diffusion-weighted imaging (DWI) data. Recent advances in imaging hardware and software capabilities have made it possible to acquire multishell (b-values) high-quality data required for better characterization of white-matter crossing-fiber microstructures. The purpose of this study was to investigate the extent to which brain structural organization and network topology are affected by the choice of diffusion magnetic resonance imaging (MRI) acquisition strategy and parcellation scale. We performed graph-theoretical network analysis using DWI data from 35 Human Connectome Project subjects. Our study compared four single-shell (b = 1000, 3000, 5000, 10,000 s/mm2) and multishell sampling schemes and six parcellation scales (68, 200, 400, 600, 800, 1000 nodes) using five graph metrics, including small-worldness, clustering coefficient, characteristic path length, modularity and global efficiency. Rich-club analysis was also performed to explore the rich-club organization of brain structural networks. Our results showed that the parcellation scale and imaging protocol have significant effects on the network attributes, with the parcellation scale having a substantially larger effect. Regardless of the parcellation scale, the brain structural networks exhibited a rich-club organization with similar cortical distributions across the parcellation scales involving at least 400 nodes. Compared to single b-value diffusion acquisitions, the deterministic tractography using multishell diffusion imaging data consisting of shells with b-values higher than 5000 s/mm2 resulted in significantly improved fiber-tracking results at the locations where fiber bundles cross each other. Brain structural networks constructed using the multishell acquisition scheme including high b-values also exhibited significantly shorter characteristic path lengths, higher global efficiency and lower modularity. Our results showed that both parcellation scale and sampling protocol can significantly impact the rich-club organization of brain structural networks. Therefore, caution should be taken concerning the reproducibility of connectivity results with regard to the parcellation scale and sampling scheme.

scholarly journals Identification of key regulators in prostate cancer from gene expression datasets of patients

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Irengbam Rocky Mangangcha ◽  
Md. Zubbair Malik ◽  
Ömer Küçük ◽  
Shakir Ali ◽  
R. K. Brojen Singh
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Regulatory Pathways ◽  
Scale Free ◽  
Topological Parameters ◽  
Rich Club ◽  
Self Organized ◽  
Mutational Status ◽  
Interactome Network ◽  
The Rich

Abstract Identification of key regulators and regulatory pathways is an important step in the discovery of genes involved in cancer. Here, we propose a method to identify key regulators in prostate cancer (PCa) from a network constructed from gene expression datasets of PCa patients. Overexpressed genes were identified using BioXpress, having a mutational status according to COSMIC, followed by the construction of PCa Interactome network using the curated genes. The topological parameters of the network exhibited power law nature indicating hierarchical scale-free properties and five levels of organization. Highest degree hubs (k ≥ 65) were selected from the PCa network, traced, and 19 of them was identified as novel key regulators, as they participated at all network levels serving as backbone. Of the 19 hubs, some have been reported in literature to be associated with PCa and other cancers. Based on participation coefficient values most of these are connector or kinless hubs suggesting significant roles in modular linkage. The observation of non-monotonicity in the rich club formation suggested the importance of intermediate hubs in network integration, and they may play crucial roles in network stabilization. The network was self-organized as evident from fractal nature in topological parameters of it and lacked a central control mechanism.

scholarly journals The Complexity of Urban CO2 Emission Network: An Exploration of the Yangtze River Middle Reaches Megalopolis, China

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zuo Zhang ◽  
Zhe Wang ◽  
Wei Zhang ◽  
Yanzhong Liu ◽  
Zhi Li ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Co2 Emission ◽  
Yangtze River ◽  
Urban Agglomeration ◽  
The Yangtze River ◽  
Low Carbon ◽  
Rich Club ◽  
Production And Consumption ◽  
Structural Equivalence ◽  
The Rich

With their focus on human production and consumption activities, cities incur massive energy consumption and CO2 emissions. An intercity connection is a typical complex system in which the interaction between cities is crucial for developing low-carbon outputs within the urban agglomeration. This paper presents the construction of the CO2 emission network of an urban agglomeration in the Yangtze River middle reaches megalopolis, based on the gravity model. Combined with social network analysis (SNA), a multilevel analysis framework is proposed to deal with the complexity, spatiality, and visualization of the CO2 emission network with reference to the network features, structural equivalence, and the rich-club phenomenon. The following results emerged: firstly, the spatial structure of the CO2 emissions was characterized by low robustness and compactness, indicating disunity among the studied cities. Secondly, there was found to be a strong correlation between regionalism and intercity connections, with geographically close cities playing a similar role in the network. Thirdly, the “rich-club” cities, including Wuhan, Changsha, Xiaogan, and Zhuzhou, dominated the connections, covering more than 87.1% of the network in the Yangtze River Middle Reaches Megalopolis.

scholarly journals The rich club of the brain in bipolar disorder

2015 ◽  
Vol 9 ◽  
Author(s):  
Lord Anton ◽  
Roberts Gloria ◽  
Breakspear Michael ◽  
Mitchell Phillip
Keyword(s):  
Bipolar Disorder ◽  
Rich Club ◽  
The Rich ◽  
The Brain

scholarly journals Task-related effective connectivity reveals that the cortical rich club gates cortex-wide communication

2017 ◽  
Author(s):  
Mario Senden ◽  
Niels Reuter ◽  
Martijn P. van den Heuvel ◽  
Rainer Goebel ◽  
Gustavo Deco ◽  
...  
Keyword(s):  
Information Exchange ◽  
Effective Connectivity ◽  
Functional Integration ◽  
Brain Regions ◽  
Rich Club ◽  
State Dependent ◽  
Gating Mechanism ◽  
The Rich ◽  
Cortical Regions ◽  
Peripheral Regions

AbstractHigher cognition may require the globally coordinated integration of specialized brain regions into functional networks. A collection of structural cortical hubs - referred to as the rich club - has been hypothesized to support task-specific functional integration. In the present paper, we use a whole-cortex model to estimate directed interactions between 68 cortical regions from fMRI activity for four different tasks (reflecting different cognitive domains) and resting state. We analyze the state-dependent input and output effective connectivity of the structural rich club and relate these to whole-cortex dynamics and network reconfigurations. We find that the cortical rich club exhibits an increase in outgoing effective connectivity during task performance as compared to rest while incoming connectivity remains constant. Increased outgoing connectivity targets a sparse set of peripheral regions with specific regions strongly overlapping between tasks. At the same time, community detection analyses reveal massive reorganizations of interactions among peripheral regions, including those serving as target of increased rich club output. This suggests that while peripheral regions may play a role in several tasks, their concrete interplay might nonetheless be task-specific. Furthermore, we observe that whole-cortex dynamics are faster during task as compared to rest. The decoupling effects usually accompanying faster dynamics appear to be counteracted by the increased rich club outgoing effective connectivity. Together our findings speak to a gating mechanism of the rich club that supports fast-paced information exchange among relevant peripheral regions in a task-specific and goal-directed fashion, while constantly listening to the whole network.

Configuration and dynamics of dominant inspiratory multineuronal activity patterns during eupnea and gasping generation in vitro

2021 ◽  
Author(s):  
Josué de Jesús Juárez-Vidales ◽  
Jesús Esteban Pérez-Ortega ◽  
Jonathan Julio Ismael Lorea-Hernández ◽  
Felipe A. Méndez-Salcido ◽  
Fernando Pena-Ortega
Keyword(s):  
Activity Patterns ◽  
Respiratory Neurons ◽  
Core Network ◽  
Population Activity ◽  
Scale Free ◽  
Rich Club ◽  
Wide Range ◽  
Multineuronal Activity ◽  
The Rich

The preBötzinger complex (preBötC), located within the ventral respiratory column, produces inspiratory bursts in varying degrees of synchronization/amplitude. This wide range of population burst patterns reflects the flexibility of the preBötC neurons, which is expressed in variations in the onset/offset times of their activations and their activity during the population bursts, with respiratory neurons exhibiting a large cycle-to-cycle timing jitter both at the population activity onset and at the population activity peak; suggesting that respiratory neurons are stochastically activated before and during the inspiratory bursts. However, it is still unknown whether this stochasticity is maintained while evaluating the coactivity of respiratory neuronal ensembles. Moreover, the preBötC topology also remains unknown. Here, by simultaneously recording tens of preBötC neurons and using coactivation analysis during the inspiratory periods, we found that the preBötC has a scale-free configuration (mixture of not many highly connected nodes -hubs- with abundant poorly connected elements) exhibiting the rich-club phenomenon (hubs more likely interconnected with each other). PreBötC neurons also produce multineuronal activity patterns (MAPs) that are highly stable and change during the hypoxia-induced reconfiguration. Moreover, preBötC contains a coactivating core network shared by all its MAPs. Finally, we found a distinctive pattern of sequential coactivation of core network neurons at the beginning of the inspiratory periods, indicating that, when evaluated at the multicellular level, the coactivation of respiratory neurons seems not to be stochastic.

Revisiting the Rich Club of the Internet AS-Level Topology

2013 ◽  
Vol E96.B (3) ◽  
pp. 900-904
Author(s):  
Yangyang WANG ◽  
Jun BI ◽  
Jianping WU
Keyword(s):  
The Internet ◽  
Rich Club ◽  
The Rich

scholarly journals Geometric explanation of the rich-club phenomenon in complex networks

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Máté Csigi ◽  
Attila Kőrösi ◽  
József Bíró ◽  
Zalán Heszberger ◽  
Yury Malkov ◽  
...  
Keyword(s):  
Complex Networks ◽  
Rich Club ◽  
Geometric Explanation ◽  
The Rich

scholarly journals Resilience of Core-Periphery Networks in the Case of Rich-Club

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Matteo Cinelli ◽  
Giovanna Ferraro ◽  
Antonio Iovanella
Keyword(s):  
Complex Networks ◽  
Information Flow ◽  
Scenario Analysis ◽  
Decision Process ◽  
High Density ◽  
Network Efficiency ◽  
Rich Club ◽  
The Core ◽  
Network Measures ◽  
The Rich

Core-periphery networks are structures that present a set of central and densely connected nodes, namely, the core, and a set of noncentral and sparsely connected nodes, namely, the periphery. The rich-club refers to a set in which the highest degree nodes show a high density of connections. Thus, a network that displays a rich-club can be interpreted as a core-periphery network in which the core is made up of a number of hubs. In this paper, we test the resilience of networks showing a progressively denser rich-club and we observe how this structure is able to affect the network measures in terms of both cohesion and efficiency in information flow. Additionally, we consider the case in which, instead of making the core denser, we add links to the periphery. These two procedures of core and periphery thickening delineate a decision process in the placement of new links and allow us to conduct a scenario analysis that can be helpful in the comprehension and supervision of complex networks under the resilience perspective. The advantages of the two procedures, as well as their implications, are discussed in relation to both network efficiency and node heterogeneity.

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