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Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) is characterized by chronic pain in pelvic area and lower urinary tract symptoms (LUTS). Previous neuroimaging studies demonstrated that chronic pain was associated with the altered brain activity. However, the pathological mechanisms associated with altered brain control of CP/CPPS are not well‐understood. Therefore, we sought to investigate the topological properties of white matter brain networks in patients with CP/CPPS and whether the topological configuration of frontal‐parietal control network was disrupted. We collected 19 patients with CP/CPPS and 32 matched healthy controls (HCs). Diffusion tensor imaging data of all participates were used to map the white matter structural networks. Graph theoretical method was applied to investigate the alterations of topological properties of brain network in patients. Moreover, we also investigated whether the alerted brain regions might be correlated with any clinical features of patients by the method of Pearson correlation analysis. Both CP/CPPS patients and HCs exhibited a 'small‐world' behavior or economical small‐world architecture of the white matter brain networks. In addition, CP/CPPS had a lower global efficiency in the right middle frontal gyrus (orbital part) and a higher global efficiency in the left middle cingulate and paracingulate gyri. CP/CPPS also showed increased local efficiency in the left middle cingulate and paracingulate gyri and paracentral lobule. Moreover, the local efficiency of the left middle cingulate gyrus was positively correlated with the scores of the influence of symptoms on the quality of life. The local efficiency of the left precuneus and right supplementary motor area were positively correlated with the total scores of NIH‐CPSI and the scores of pain and discomfort symptoms, respectively. Together, we found that patients with CP/CPPS had alterations of connections within the frontal‐parietal control network, which suggested that the altered connectivity involved in the executive control processing procedures might contribute to the pathogenesis of the pelvic pain and LUTS in CP/CPPS. Thus these results provided new insights into the understanding of CP/CPPS. [ABSTRACT FROM AUTHOR]

Background: Neuroimaging studies have demonstrated disrupted brain functional networks in major depression disorder (MDD); however, alterations to whole‐brain networks specifically associated with adolescent MDD remain poorly understood. Purpose: To investigate the topological architecture of intrinsic brain functional networks in drug‐naïve, first‐episode adolescent MDD patients using graph theoretical analysis. Study type: Prospective. Subjects: In all, 109 adolescent MDD patients and 70 healthy control subjects. Field Strength/Sequences: 3.0T; gradient‐echo echo‐planar imaging sequence. Assessment: After the construction of whole‐brain functional networks by thresholding partial correlation matrices of 90 brain regions, we calculated the topological properties (eg, small‐world, efficiency, and nodal centrality) using graph theoretical analysis. Statistical Tests: A chi‐squared test was used to compare the gender‐ratio difference, and a two‐sample t‐test was used in the comparison of age. We compared network measures between the two groups using nonparametric permutation tests. Exploratory partial correlation analyses were used to determine the relationships between the topological metrics showing significant between‐group differences and the clinical variables for adolescent MDD patients. Results: Small‐world architecture in brain functional networks was identified for both the MDD and control groups. However, depressed adolescents exhibited lower characteristic path length, normalized characteristic path length and clustering coefficient, and higher global efficiency than controls (false discovery rate [FDR] q < 0.05). Compared with controls, depressed adolescents exhibited increased nodal centralities in the default mode regions, including the right anterior cingulate and paracingulate gyri, left posterior cingulate gyrus, right superior frontal gyrus (medial part), bilateral hippocampus, and bilateral parahippocampal gyrus, and decreased nodal centralities in the orbitofrontal, temporal, and occipital regions (FDR q < 0.05). Data Conclusion: This study indicated that drug‐naïve, first‐episode adolescent MDD patients exhibit disruptions in whole‐brain functional networks. Level of Evidence: 1 Technical Efficacy Stage: 2 J. MAGN. RESON. IMAGING 2020;52:1790–1798. [ABSTRACT FROM AUTHOR]

In this work we prove general bounds for the diameter of random graphs generated by a preferential attachment model whose parameter is a function f:N→[0,1] that drives the asymptotic proportion between the numbers of vertices and edges. These results are sharp when f is a regularly varying function at infinity with strictly negative index of regular variation −γ. For this particular class, we prove a characterization for the diameter that depends only on −γ. More specifically, we prove that the diameter of such graphs is of order 1/γ with high probability, although its vertex set order goes to infinity polynomially. Sharp results for the diameter for a wide class of slowly varying functions are also obtained. [ABSTRACT FROM AUTHOR]

Various neuroimaging studies had identified changes of cerebral activity and structures during the processes of sexual arousal. The identified brain regions were also believed to regulate emotional and cognitive processes. Yet, little was known about the neural mechanisms underlying psychogenic erectile dysfunction. Moreover, brain mechanisms mediating the processing of sexual arousal by these psychological processes were unclear. To investigate this issue, graph theory was applied to evaluate the topological properties of functional brain networks among 24 psychogenic erectile dysfunction patients and 26 healthy controls. Abnormal metrics and correlations with clinical characteristics were further analyzed. Our results showed that psychogenic erectile dysfunction had higher small-worldness and more modules. Furthermore, psychogenic erectile dysfunction showed altered path length and strength of the right superior frontal gyrus (dorsolateral), superior parietal gyrus, parahippocampal gyrus and left temporal pole (superior temporal gyrus), post-central gyrus, mainly located in the cognitive control and emotional regulation subnetworks. And the altered parameters of the small-worldness and right parahippocampal gyrus were related to the clinical characteristics of psychogenic erectile dysfunction. Together, our results suggested that psychogenic erectile dysfunction was associated with disruptions in the topological structure of functional brain subnetworks underlying the cognitive and emotional processes. [ABSTRACT FROM AUTHOR]

This article investigates the impact of network structure on innovation efficiency by establishing a simulation model of innovation process in the context of innovation networks. The results indicate that short path lengths between vertices are conductive to high efficiency of explorative innovations, dense clusters are conductive to high efficiency of exploitative innovations, and high small-worldness is conductive to high efficiency of the hybrid of these two innovations. Moreover, we discussed the reason of the results and give some suggestions to innovators and innovation policy makers. © 2014 Wiley Periodicals, Inc. Complexity 21: 111-122, 2015 [ABSTRACT FROM AUTHOR]

Recent neuroimaging studies have demonstrated that cigarette smoking is associated with changed brain structure and function. However, little is known about alterations of the topological organization of brain functional networks in heavy smokers. Thirty-one heavy smokers and 33 non-smokers underwent a resting-state functional magnetic resonance imaging scan. The whole-brain functional networks were constructed by thresholding the correlation matrices of 90 brain regions and their topological properties were analyzed using graph network analysis. Non-parametric permutation tests were performed to investigate group differences in network topological measures and multiple regression analysis was conducted to determine the relationships between the network metrics and smoking-related variables. Both heavy smokers and non-smokers exhibited small-world architecture in their brain functional networks. Compared with non-smokers, however, heavy smokers showed altered topological measurements characterized by lower global efficiency, higher local efficiency and clustering coefficients and greater path length. Furthermore, heavy smokers demonstrated decreased nodal global efficiency mainly in brain regions within the default mode network, whereas increased nodal local efficiency predominated in the visual-related regions. In addition, heavy smokers exhibited an association between the altered network metrics and the duration of cigarette use or the severity of nicotine dependence. Our results suggest that heavy smokers may have less efficient network architecture in the brain, and chronic cigarette smoking is associated with disruptions in the topological organization of brain networks. Our findings may further the understanding of the effects of chronic cigarette smoking on the brain and the pathophysiological mechanisms underlying nicotine dependence. [ABSTRACT FROM AUTHOR]

Recent resting-state functional magnetic resonance imaging (fMRI) studies using graph theory metrics have revealed that the functional network of the human brain possesses small-world characteristics and comprises several functional hub regions. However, it is unclear how the affective functional network is organized in the brain during the processing of affective information. In this study, the fMRI data were collected from 25 healthy college students as they viewed a total of 81 positive, neutral, and negative pictures. The results indicated that affective functional networks exhibit weaker small-worldness properties with higher local efficiency, implying that local connections increase during viewing affective pictures. Moreover, positive and negative emotional processing exhibit dissociable functional hubs, emerging mainly in task-positive regions. These functional hubs, which are the centers of information processing, have nodal betweenness centrality values that are at least 1.5 times larger than the average betweenness centrality of the network. Positive affect scores correlated with the betweenness values of the right orbital frontal cortex (OFC) and the right putamen in the positive emotional network; negative affect scores correlated with the betweenness values of the left OFC and the left amygdala in the negative emotional network. The local efficiencies in the left superior and inferior parietal lobe correlated with subsequent arousal ratings of positive and negative pictures, respectively. These observations provide important evidence for the organizational principles of the human brain functional connectome during the processing of affective information. Hum Brain Mapp, 36:415-426, 2015. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Consider the network of all secondary structures of a given RNA sequence, where nodes are connected when the corresponding structures have base pair distance one. The expected degree of the network is the average number of neighbors, where average may be computed with respect to the either the uniform or Boltzmann probability. Here, we describe the first algorithm, RNAexpNumNbors, that can compute the expected number of neighbors, or expected network degree, of an input sequence. For RNA sequences from the Rfam database, the expected degree is significantly less than the constrained minimum free energy structure, defined to have minimum free energy (MFE) over all structures consistent with the Rfam consensus structure. The expected degree of structural RNAs, such as purine riboswitches, paradoxically appears to be smaller than that of random RNA, yet the difference between the degree of the MFE structure and the expected degree is larger than that of random RNA. Expected degree does not seem to correlate with standard structural diversity measures of RNA, such as positional entropy and ensemble defect. The program RNAexpNumNbors is written in C, runs in cubic time and quadratic space, and is publicly available at . © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

A large number of studies have been conducted over a long period of time to better understand how people adopt new products and/or ideas. With recent advancement in social network technologies such as Facebook and Twitter, the interest in better understanding the role of social network effect or social influence on the adoption of an idea has again been brought to the forefront. In this study, we examine the propagation of ideas through a social network and we introduce two network propagation rules to explain the idea propagation under social influence. We then apply the rules to different network models, which include a large-world network, a small-world network, a random network, and local neighbor networks such as a path, a star, and their variations. In terms of social network in our study, we say that if a person has adopted an idea, then the person is infected by the idea. So the main interest of our study is to find the minimum number of initially infected individuals (i.e., early adopters of an idea) to effectively infect the rest of the social network. Furthermore, we give examples of real-world situations in management and the spread of violence, where the proposed propagation analysis can be applied. © 2013 Wiley Periodicals, Inc. NETWORKS, Vol. 63(2), 160-169 2014 [ABSTRACT FROM AUTHOR]

In a research environment dominated by reductionist approaches to brain disease mechanisms, gene network analysis provides a complementary framework in which to tackle the complex dysregulations that occur in neuropsychiatric and other neurological disorders. Gene-gene expression correlations are a common source of molecular networks because they can be extracted from high-dimensional disease data and encapsulate the activity of multiple regulatory systems. However, the analysis of gene coexpression patterns is often treated as a mechanistic black box, in which looming 'hub genes' direct cellular networks, and where other features are obscured. By examining the biophysical bases of coexpression and gene regulatory changes that occur in disease, recent studies suggest it is possible to use coexpression networks as a multi-omic screening procedure to generate novel hypotheses for disease mechanisms. Because technical processing steps can affect the outcome and interpretation of coexpression networks, we examine the assumptions and alternatives to common patterns of coexpression analysis and discuss additional topics such as acceptable datasets for coexpression analysis, the robust identification of modules, disease-related prioritization of genes and molecular systems and network meta-analysis. To accelerate coexpression research beyond modules and hubs, we highlight some emerging directions for coexpression network research that are especially relevant to complex brain disease, including the centrality-lethality relationship, integration with machine learning approaches and network pharmacology. [ABSTRACT FROM AUTHOR]

Researchers often model folded protein structures as graphs with amino acids as the vertices and edges representing contacts between amino acids. The vertices in these graphs are naturally ordered in the amino acid sequence order. There are many different graph construction methods and there is no consensus about what construction to use or what the major issues are with each construction in the literature. We investigate different constructions and examine their effect on various graph measures. We also consider the small-world network model for proteins, discuss its validity under the different constructions, and discuss random protein graph generation. We propose a new graph property for graphs with ordered vertices, the contact distribution, and propose a method of reciprocal attachment to merge neighborhoods for protein graphs. Statistical Analysis and Data Mining Statistical Analysis and Data Mining 2013 DOI: 10.1002/sam.11203 [ABSTRACT FROM AUTHOR]

The article presents a topological study of metabolism. It mentions that graph representations of pathways are used in the study as well as the paradigmatic examples that are used in the evaluation of the topological properties of metabolism that include the bipartite network approach. Moreover, it indicates that determining the topological organization of metabolism is a fascinating one that requires an appropriate measures and real systems should be mapped conveniently.

We investigated the relationships among landscape quality, gene flow, and population genetic structure of fishers ( Martes pennanti) in ON, Canada. We used graph theory as an analytical framework considering each landscape as a network node. The 34 nodes were connected by 93 edges. Network structure was characterized by a higher level of clustering than expected by chance, a short mean path length connecting all pairs of nodes, and a resiliency to the loss of highly connected nodes. This suggests that alleles can be efficiently spread through the system and that extirpations and conservative harvest are not likely to affect their spread. Two measures of node centrality were negatively related to both the proportion of immigrants in a node and node snow depth. This suggests that central nodes are producers of emigrants, contain high-quality habitat (i.e., deep snow can make locomotion energetically costly) and that fishers were migrating from high to low quality habitat. A method of community detection on networks delineated five genetic clusters of nodes suggesting cryptic population structure. Our analyses showed that network models can provide system-level insight into the process of gene flow with implications for understanding how landscape alterations might affect population fitness and evolutionary potential. [ABSTRACT FROM AUTHOR]

Abstract: The network paradigm is increasingly used to describe the topology and dynamics of complex systems. Here, we review the results of the topological analysis of protein structures as molecular networks describing their small-world character, and the role of hubs and central network elements in governing enzyme activity, allosteric regulation, protein motor function, signal transduction and protein stability. We summarize available data how central network elements are enriched in active centers and ligand binding sites directing the dynamics of the entire protein. We assess the feasibility of conformational and energy networks to simplify the vast complexity of rugged energy landscapes and to predict protein folding and dynamics. Finally, we suggest that modular analysis, novel centrality measures, hierarchical representation of networks and the analysis of network dynamics will soon lead to an expansion of this field. [Copyright &y& Elsevier]

Networks are ubiquitous in natural, technological and social systems. They are of increasing relevance for improved understanding and control of infectious diseases of plants, animals and humans, given the interconnectedness of today's world. Recent modelling work on disease development in complex networks shows: the relative rapidity of pathogen spread in scale-free compared with random networks, unless there is high local clustering; the theoretical absence of an epidemic threshold in scale-free networks of infinite size, which implies that diseases with low infection rates can spread in them, but the emergence of a threshold when realistic features are added to networks (e.g. finite size, household structure or deactivation of links); and the influence on epidemic dynamics of asymmetrical interactions. Models suggest that control of pathogens spreading in scale-free networks should focus on highly connected individuals rather than on mass random immunization. A growing number of empirical applications of network theory in human medicine and animal disease ecology confirm the potential of the approach, and suggest that network thinking could also benefit plant epidemiology and forest pathology, particularly in human-modified pathosystems linked by commercial transport of plant and disease propagules. Potential consequences for the study and management of plant and tree diseases are discussed. [ABSTRACT FROM AUTHOR]