Connection strength of the macaque connectome augments topological and functional network attributes

Mammalian brains constitute complex organized networks of neural projections. On top of their binary topological organization, the strength (or weight) of these neural projections can be highly variable across connections and is thus likely of additional importance to the overall topological and functional organization of the network. Here we investigated the specific distribution pattern of connection strength in the macaque connectome. We performed weighted and binary network analysis on the cortico-cortical connectivity of the macaque provided by the unique tract-tracing dataset of Markov and colleagues (2014) and observed in both analyses a small-world, modular and rich club organization. Moreover, connectivity strength showed a distribution augmenting the architecture identified in the binary network version by enhancing both local network clustering and the central infrastructure for global topological communication and integration. Functional consequences of this topological distribution were further examined using the Kuramoto model for simulating interactions between brain regions and showed that the connectivity strength distribution across connections enhances synchronization within modules and between rich club hubs. Together, our results suggest that neural pathway strength promotes topological properties in the macaque connectome for local processing and global network integration.

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Connectomics-Based Functional Network Alterations in both Depressed Patients with Suicidal Behavior and Healthy Relatives of Suicide Victims

Scientific Reports ◽

10.1038/s41598-019-50881-y ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 4

Author(s):

Gerd Wagner ◽

Feliberto de la Cruz ◽

Stefanie Köhler ◽

Fabricio Pereira ◽

Stéphane Richard-Devantoy ◽

...

Keyword(s):

Functional Connectivity ◽

Suicidal Behavior ◽

Functional Organization ◽

Local Network ◽

Healthy Controls ◽

Suicide Attempters ◽

Rich Club ◽

Depressed Patients ◽

Global Efficiency ◽

Clustering Coefficients

Abstract Understanding the neural mechanisms of suicidal behavior is crucial. While regional brain alterations have previously been reported, knowledge about brain functional connectomics is currently limited. Here, we investigated differences in global topologic network properties and local network-based functional organization in both suicide attempters and suicide relatives. Two independent samples of depressed suicide attempters (N = 42), depressed patient controls (N = 43), healthy controls (N = 66) as well as one sample of healthy relatives of suicide victims (N = 16) and relatives of depressed patients (N = 16) were investigated with functional magnetic resonance imaging in the resting-state condition. Graph theory analyses were performed. Assortativity, clustering coefficients, global efficiency, and rich-club coefficients were calculated. A network-based statistic approach was finally used to examine functional connectivity matrices. In comparison to healthy controls, both patient groups showed significant reduction in assortativity, and decreased functional connectivity in largely central and posterior brain networks. Suicide attempters only differed from patient controls in terms of higher rich-club coefficients for the highest degree nodes. Compared to patient relatives and healthy controls, suicide relatives showed reduced assortativity, reduced clustering coefficients, increased global efficiency, and increased rich-club coefficients for the highest degree nodes. Suicide relatives also showed reduced functional connectivity in one anterior and one posterior sub-network in comparison to healthy controls, and in a largely anterior brain network in comparison to patient relatives. In conclusion, these results suggest that the vulnerability to suicidal behavior may be associated with heritable deficits in global brain functioning – characterized by weak resilience and poor segregation - and in functional organization with reduced connectivities affecting the ventral and dorsal prefrontal cortex, the anterior cingulate, thalamus, striatum, and possibly the insula, fusiform gyrus and the cerebellum.

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Risk Aversion and Social Networks

Review of Network Economics ◽

10.1515/rne-2014-0029 ◽

2014 ◽

Vol 13 (2) ◽

Cited By ~ 3

Author(s):

Jaromír Kovářík ◽

Marco J. van der Leij

Keyword(s):

Risk Aversion ◽

Undergraduate Students ◽

Network Structure ◽

Network Formation ◽

Empirical Finding ◽

Local Network ◽

Network Clustering ◽

Strongly Correlated ◽

Social Network Structure ◽

The One

AbstractThis paper first investigates empirically the relationship between risk aversion and social network structure in a large group of undergraduate students. We find that risk aversion is strongly correlated to local network clustering, that is, the probability that one has a social tie to friends of friends. We then propose a network formation model that generates this empirical finding, suggesting that locally superior information on benefits makes it more attractive for risk averse individuals to link to friends of friends. Finally, we discuss implications of this model. The model generates a positive correlation between local network clustering and benefits, even if benefits are distributed independently ex ante. This provides an alternative explanation of this relationship to the one given by the social capital literature. We also establish a linkage between the uncertainty of the environment and the network structure: risky environments generate more clustered and more unequal networks in terms of connectivity.

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The asymmetric effects of local and global network ties on firms’ innovation performance

Journal of Business and Industrial Marketing ◽

10.1108/jbim-10-2016-0252 ◽

2018 ◽

Vol 33 (3) ◽

pp. 377-389 ◽

Cited By ~ 2

Author(s):

Lei Wang ◽

Jun Li ◽

Shaoqing Huang

Keyword(s):

Structural Equation ◽

Network Effects ◽

Institutional Environment ◽

Innovation Performance ◽

Global Network ◽

Local Network ◽

Network Ties ◽

Content Type ◽

Asymmetric Effects ◽

Absorptive Capacities

Purpose The purpose of this paper is to develop and empirically test a theoretical framework examining how local network ties and global network ties affect firms’ innovation performance via their absorptive capacities. Design/methodology/approach The conceptual framework is empirically tested in a field study with multi-source data collected from a sample of 297 manufacturing firms located in four. Manufacturing clusters in the south-eastern Yangtze River Delta of China. Hypotheses were tested with the use of path analysis with maximum likelihood robust estimates through the structural equation modelling approach. Findings The asymmetry between local network ties (LNT) and global network ties (GNT) in terms of influences on firms’ innovation performance is confirmed by empirical tests. LNT not only significantly and positively contribute to firms’ innovation performance directly but also enhance it indirectly via absorptive capability, whereas GNT exhibit only marginal influence on innovation performance. GNT are shown to boost innovation performance (IP) only indirectly via firms’ absorptive capacities. Knowledge heterogeneity and the difference between domestic and multinational firms’ institutional environment are considered to be the main causes of the asymmetric effects. Originality/value While the previous literature either focused on the mediating role of firms’ knowledge absorptive capacities or investigated the effects of social networks separately, this study incorporates both mechanisms into a single analytical framework to better account for the interactions between network effects and absorptive capacities. The results challenge some previous studies positing that GNT are stronger determinants than LNT in shaping a local firm’s innovation capacity in emerging economies, and the findings emphasize the importance of absorptive capacity in helping local enterprises to leverage external linkages to enhance firm’s innovation performance.

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GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks

Bioinformatics ◽

10.1093/bioinformatics/btaa459 ◽

2020 ◽

Vol 36 (Supplement_1) ◽

pp. i464-i473

Author(s):

Kapil Devkota ◽

James M Murphy ◽

Lenore J Cowen

Keyword(s):

Network Structure ◽

Biological Networks ◽

Link Prediction ◽

Prediction Method ◽

Global Network ◽

Local Network ◽

Supplementary Information ◽

Network Data ◽

Ppi Network ◽

Diffusion State

Abstract Motivation One of the core problems in the analysis of biological networks is the link prediction problem. In particular, existing interactions networks are noisy and incomplete snapshots of the true network, with many true links missing because those interactions have not yet been experimentally observed. Methods to predict missing links have been more extensively studied for social than for biological networks; it was recently argued that there is some special structure in protein–protein interaction (PPI) network data that might mean that alternate methods may outperform the best methods for social networks. Based on a generalization of the diffusion state distance, we design a new embedding-based link prediction method called global and local integrated diffusion embedding (GLIDE). GLIDE is designed to effectively capture global network structure, combined with alternative network type-specific customized measures that capture local network structure. We test GLIDE on a collection of three recently curated human biological networks derived from the 2016 DREAM disease module identification challenge as well as a classical version of the yeast PPI network in rigorous cross validation experiments. Results We indeed find that different local network structure is dominant in different types of biological networks. We find that the simple local network measures are dominant in the highly connected network core between hub genes, but that GLIDE’s global embedding measure adds value in the rest of the network. For example, we make GLIDE-based link predictions from genes known to be involved in Crohn’s disease, to genes that are not known to have an association, and make some new predictions, finding support in other network data and the literature. Availability and implementation GLIDE can be downloaded at https://bitbucket.org/kap_devkota/glide. Supplementary information Supplementary data are available at Bioinformatics online.

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P14.53 Deconstructing pathologically increased MEG network clustering in glioma patients

Neuro-Oncology ◽

10.1093/neuonc/noz126.288 ◽

2019 ◽

Vol 21 (Supplement_3) ◽

pp. iii79-iii79

Author(s):

S D Kulik ◽

J Derks ◽

T Numan ◽

A Hillebrand ◽

P C de Witt Hamer ◽

...

Keyword(s):

Brain Network ◽

Global Network ◽

Tumor Localization ◽

Network Clustering ◽

Phase Lag ◽

Functional Brain ◽

Global Average ◽

Entire Brain ◽

Functional Brain Network ◽

Local Clustering

Abstract Introduction Functional brain networks in glioma patients are characterized by higher global clustering than healthy controls, indicating stronger connectivity in triads of brain regions when averaging across the entire brain. However, this could be due to either primary increased local clustering of (peri)tumor regions or higher local clustering throughout the entire brain. METHODS Magnetoencephalography recordings of 71 glioma patients and 53 HCs were analyzed by calculating functional connectivity with the phase lag index between source-localized time series of 78 cortical regions of the automated anatomical labelling atlas. Per participant, we calculated (1) global average clustering, (2) local clustering of tumor and non-tumor regions, and (3) Euclidean distance between tumor centroids and of all other region centroids. RESULTS Glioma patients had higher global average clustering (p=0.002) than HCs. This increase was indeed global: there was no difference between tumor and non-tumor regions (p=0.154) and no association between distance and local clustering (p=0.759). When splitting patients into high (top 25%, n=18) and normal global clustering (other 75%, n=53) to more specifically pick up on the determinants of pathological global average clustering, again no localized or distance-dependent effects were found. High clustering patients were younger than patients with normal global clustering (p=0.027). Posthoc analysis into tumor localization preference for particular network regions in the entire patient cohort revealed greater tumor occurrence in regions with high clustering in HC (p<0.001), while patients with high global clustering showed tumors localized in regions with lower clustering in HC (p=0.032). CONCLUSION The functional brain network of a subset of (relatively young) glioma patients is disturbed on a global level, suggesting that treatment thereof might benefit patients. Moreover, our exploratory analyses suggest that gliomas occur more often in normally highly clustered regions, but that tumors occurring in less clustered regions are associated with more extensive global network alterations. These findings may speculatively indicate that patients with and without such pathologically altered global clustering represent distinct phenotypes (both in terms of age and tumor localization) and may also need to be treated as such.

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Optimisation of Ecological Leisure Industrial Planning Based on Improved GIS-AHP: A Case Study in Shapingba District, Chongqing, China

Sustainability ◽

10.3390/su12010033 ◽

2019 ◽

Vol 12 (1) ◽

pp. 33 ◽

Cited By ~ 1

Author(s):

Tongyun Du ◽

Henrik Vejre ◽

Christian Fertner ◽

Pengcheng Xiang

Keyword(s):

River System ◽

Global Network ◽

Local Network ◽

Analytic Hierarchy ◽

Scenic Spot ◽

Suitability Index ◽

Artificial Environment ◽

Industrial Planning ◽

Leisure Industry ◽

Industry Area

This study seeks a scientific methodology for ecological leisure industry planners to contribute to a more ecologically friendly leisure industry. This study creates an environment suitability model (LIDES) for leisure industry development. This model sees the natural ecological environment as just as important as the artificial environment. This study identifies the following factors: suitable land, unsuitable land, park plaza, scenic spot, river system, global network reachability, local network reachability, business impact, industrial impact. The Spatial Syntax method is used to account for effects of the urban road network. This method is incorporated into a geographic information system-analytic hierarchy process (GIS-AHP) approach, thus developing this method further. The method is demonstrated in the leisure industry in Shapingba District of Chongqing, China. The final suitability index map for ecological leisure industry is divided into four types: highly suitable, moderately suitable, marginally suitable, and low suitability areas. As a result, 8.08% (42.55 km2) of the study area has low suitability, 82.61% (435.15 km2) has marginal suitability, 8.62% (45.42 km2) has moderate suitability and 0.69% (3.65 km2) has the best suitability for creating an ecological leisure industry area. Discussion and relevant suggestions are given for further research.

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Network Ecology and Adolescent Social Structure

American Sociological Review ◽

10.1177/0003122414554001 ◽

2014 ◽

Vol 79 (6) ◽

pp. 1088-1121 ◽

Cited By ~ 80

Author(s):

Daniel A. McFarland ◽

James Moody ◽

David Diehl ◽

Jeffrey A. Smith ◽

Reuben J. Thomas

Keyword(s):

Global Network ◽

Health Study ◽

Formation Mechanisms ◽

Network Clustering ◽

Network Structures ◽

Exponential Random Graph Models ◽

Graph Models ◽

Organizational Environments ◽

Random Graph Models ◽

Exponential Random Graph

Adolescent societies—whether arising from weak, short-term classroom friendships or from close, long-term friendships—exhibit various levels of network clustering, segregation, and hierarchy. Some are rank-ordered caste systems and others are flat, cliquish worlds. Explaining the source of such structural variation remains a challenge, however, because global network features are generally treated as the agglomeration of micro-level tie-formation mechanisms, namely balance, homophily, and dominance. How do the same micro-mechanisms generate significant variation in global network structures? To answer this question we propose and test a network ecological theory that specifies the ways features of organizational environments moderate the expression of tie-formation processes, thereby generating variability in global network structures across settings. We develop this argument using longitudinal friendship data on schools (Add Health study) and classrooms (Classroom Engagement study), and by extending exponential random graph models to the study of multiple societies over time.

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ЕARTHQUAKE NEAR CRILLON CAPE on NOVEMBER 25th, 2013 with Mw=5.2, I0=6–7 (Sakhalin Island)

Zemletriaseniia Severnoi Evrazii [Earthquakes in Northern Eurasia] ◽

10.35540/1818-6254.2019.22.41 ◽

2019 ◽

pp. 466-476 ◽

Cited By ~ 8

Author(s):

E. Semenova ◽

D. Safonov ◽

T. Fokina

Keyword(s):

Water Area ◽

Sakhalin Island ◽

Global Network ◽

Local Network ◽

Seismic Model ◽

Regional Network ◽

The South ◽

Earthquake Parameters ◽

Seismic Stations ◽

Field Stations

On November 25, 2013, at 03:23 UTC, an earthquake with a magnitude of МwGCMT=5.2 was recorded in the Laperuz Strait water area. This earthquake was the strongest in the south of Sakhalin Island in 2013. Parameters of the earthquake have been determined by data of seismic stations of the regional network of GS RAS Sa-khalin branch, seismic stations of global network IRIS (GSN) and Hokkaido University. Earthquake parameters on data of regional network are in accordance with data of the international seismological centers. The intensity of concussions in some areas of Sakhalin was 4–5 points on a scale of MSK-64 and 3 points on the JMA scale on the island of Hokkaido. The focus shift has happened under conditions of close horizontal compression, seismodislocation type – uplift. The earthquake of 2013 has arisen in the place of regional tec-tonic structures contact – breaks of West Sakhalin and Central Sakhalin. The seismic model was described by data of local network of field stations. Results of observations of an earthquake on November 25, 2013 are written in this article.

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Research on Distributed Data Sharing System based on Internet of Things and Blockchain

Journal of Systems Science and Information ◽

10.21078/jssi-2021-239-16 ◽

2021 ◽

Vol 9 (3) ◽

pp. 239-254

Author(s):

Enchang Sun ◽

Kang Meng ◽

Ruizhe Yang ◽

Yanhua Zhang ◽

Meng Li

Keyword(s):

Internet Of Things ◽

Data Sharing ◽

Data Storage ◽

Network Architecture ◽

Data Privacy ◽

Distributed Storage ◽

Global Network ◽

Local Network ◽

Network Data ◽

Distributed Data

Abstract Aiming at the problems of the traditional centralized data sharing platform, such as poor data privacy protection ability, insufficient scalability of the system and poor interaction ability, this paper proposes a distributed data sharing system architecture based on the Internet of Things and blockchain technology. In this system, the distributed consensus mechanism of blockchain and the distributed storage technology are employed to manage the access and storage of Internet of Things data in a secure manner. Up to the physical topology of the network, a hierarchical blockchain network architecture is proposed for local network data storage and global network data sharing, which reduces networking complexity and improves the scalability of the system. In addition, smart contract and distributed machine learning are adopted to design automatic processing functions for different types of data (public or private) and supervise the data sharing process, improving both the security and interactive ability of the system.

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The role of anatomical connection strength for interareal communication in macaque cortex

10.1101/2020.12.15.422902 ◽

2020 ◽

Author(s):

Julien Vezoli ◽

Martin Vinck ◽

Conrado A. Bosman ◽

Andre M. Bastos ◽

Christopher M Lewis ◽

...

Keyword(s):

Large Scale ◽

Functional Organization ◽

Brain Regions ◽

Connection Strength ◽

Non Invasive ◽

Large Scale Networks ◽

Invasive Techniques ◽

The Relationship ◽

Anatomical Connection

What is the relationship between anatomical connection strength and rhythmic synchronization? Simultaneous recordings of 15 cortical areas in two macaque monkeys show that interareal networks are functionally organized in spatially distinct modules with specific synchronization frequencies, i.e. frequency-specific functional connectomes. We relate the functional interactions between 91 area pairs to their anatomical connection strength defined in a separate cohort of twenty six subjects. This reveals that anatomical connection strength predicts rhythmic synchronization and vice-versa, in a manner that is specific for frequency bands and for the feedforward versus feedback direction, even if interareal distances are taken into account. These results further our understanding of structure-function relationships in large-scale networks covering different modality-specific brain regions and provide strong constraints on mechanistic models of brain function. Because this approach can be adapted to non-invasive techniques, it promises to open new perspectives on the functional organization of the human brain.

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