A Scalable Approach for Discovering Conserved Active Subnetworks across Species

PubMed Central

Verfaillie, Catherine M.; Hu, Wei-Shou; Myers, Chad L.

2010-01-01

Overlaying differential changes in gene expression on protein interaction networks has proven to be a useful approach to interpreting the cell's dynamic response to a changing environment. Despite successes in finding active subnetworks in the context of a single species, the idea of overlaying lists of differentially expressed genes on networks has not yet been extended to support the analysis of multiple species' interaction networks. To address this problem, we designed a scalable, cross-species network search algorithm, neXus (Network - cross(X)-species - Search), that discovers conserved, active subnetworks based on parallel differential expression studies in multiple species. Our approach leverages functional linkage networks, which provide more comprehensive coverage of functional relationships than physical interaction networks by combining heterogeneous types of genomic data. We applied our cross-species approach to identify conserved modules that are differentially active in stem cells relative to differentiated cells based on parallel gene expression studies and functional linkage networks from mouse and human. We find hundreds of conserved active subnetworks enriched for stem cell-associated functions such as cell cycle, DNA repair, and chromatin modification processes. Using a variation of this approach, we also find a number of species-specific networks, which likely reflect mechanisms of stem cell function that have diverged between mouse and human. We assess the statistical significance of the subnetworks by comparing them with subnetworks discovered on random permutations of the differential expression data. We also describe several case examples that illustrate the utility of comparative analysis of active subnetworks. PMID:21170309

Twitter=quitter? An analysis of Twitter quit smoking social networks.

PubMed

Prochaska, Judith J; Pechmann, Cornelia; Kim, Romina; Leonhardt, James M

2012-07-01

Widely popular, Twitter, a free social networking and micro-blogging service, offers potential for health promotion. This study examined the activity of Twitter quit smoking social network accounts. A cross-sectional analysis identified 153 activated Twitter quit smoking accounts dating back to 2007 and examined recent account activity for the month of August 2010. The accounts had a median of 155 followers and 82 total tweets per account; 49% of accounts had >100 tweets. Posted content was largely inconsistent with clinical guidelines; 48% linked to commercial sites for quitting smoking and 43% had tweets on e-cigarettes. In August 2010, 81 of the accounts (53%) were still active. Though popular for building quit smoking social networks, many of the Twitter accounts were no longer active, and tweet content was largely inconsistent with clinical guidelines. Future research is needed to examine the effectiveness of Twitter for supporting smoking cessation.

Complex network analysis of resting-state fMRI of the brain.

PubMed

Anwar, Abdul Rauf; Hashmy, Muhammad Yousaf; Imran, Bilal; Riaz, Muhammad Hussnain; Mehdi, Sabtain Muhammad Muntazir; Muthalib, Makii; Perrey, Stephane; Deuschl, Gunther; Groppa, Sergiu; Muthuraman, Muthuraman

2016-08-01

Due to the fact that the brain activity hardly ever diminishes in healthy individuals, analysis of resting state functionality of the brain seems pertinent. Various resting state networks are active inside the idle brain at any time. Based on various neuro-imaging studies, it is understood that various structurally distant regions of the brain could be functionally connected. Regions of the brain, that are functionally connected, during rest constitutes to the resting state network. In the present study, we employed the complex network measures to estimate the presence of community structures within a network. Such estimate is named as modularity. Instead of using a traditional correlation matrix, we used a coherence matrix taken from the causality measure between different nodes. Our results show that in prolonged resting state the modularity starts to decrease. This decrease was observed in all the resting state networks and on both sides of the brain. Our study highlights the usage of coherence matrix instead of correlation matrix for complex network analysis.

Analyzing heterogeneity in the effects of physical activity in children on social network structure and peer selection dynamics

PubMed Central

Henry, Teague; Gesell, Sabina B.; Ip, Edward H.

2016-01-01

Background Social networks influence children and adolescents’ physical activity. The focus of this paper is to examine the differences in the effects of physical activity on friendship selection, with eye to the implications on physical activity interventions for young children. Network interventions to increase physical activity are warranted but have not been conducted. Prior to implementing a network intervention in the field, it is important to understand potential heterogeneities in the effects that activity level have on network structure. In this study, the associations between activity level and cross sectional network structure, and activity level and change in network structure are assessed. Methods We studied a real-world friendship network among 81 children (average age 7.96 years) who lived in low SES neighborhoods, attended public schools, and attended one of two structured aftercare programs, of which one has existed and the other was new. We used the exponential random graph model (ERGMs) and its longitudinal extension to evaluate the association between activity level and various demographic factors in having, forming, and dissolving friendship. Due to heterogeneity between the friendship networks within the aftercare programs, separate analyses were conducted for each network. Results There was heterogeneity in the effect of physical activity on both cross sectional network structure and the formation and dissolution processes, both across time and between networks. Conclusions Network analysis could be used to assess the unique structure and dynamics of a social network before an intervention is implemented, so as to optimize the effects of the network intervention for increasing childhood physical activity. Additionally, if peer selection processes are changing within a network, a static network intervention strategy for childhood physical activity could become inefficient as the network evolves. PMID:27867518

Using Spatial Multiple Regression to Identify Intrinsic Connectivity Networks Involved in Working Memory Performance

PubMed Central

Gordon, Evan M.; Stollstorff, Melanie; Vaidya, Chandan J.

2012-01-01

Many researchers have noted that the functional architecture of the human brain is relatively invariant during task performance and the resting state. Indeed, intrinsic connectivity networks (ICNs) revealed by resting-state functional connectivity analyses are spatially similar to regions activated during cognitive tasks. This suggests that patterns of task-related activation in individual subjects may result from the engagement of one or more of these ICNs; however, this has not been tested. We used a novel analysis, spatial multiple regression, to test whether the patterns of activation during an N-back working memory task could be well described by a linear combination of ICNs delineated using Independent Components Analysis at rest. We found that across subjects, the cingulo-opercular Set Maintenance ICN, as well as right and left Frontoparietal Control ICNs, were reliably activated during working memory, while Default Mode and Visual ICNs were reliably deactivated. Further, involvement of Set Maintenance, Frontoparietal Control, and Dorsal Attention ICNs was sensitive to varying working memory load. Finally, the degree of left Frontoparietal Control network activation predicted response speed, while activation in both left Frontoparietal Control and Dorsal Attention networks predicted task accuracy. These results suggest that a close relationship between resting-state networks and task-evoked activation is functionally relevant for behavior, and that spatial multiple regression analysis is a suitable method for revealing that relationship. PMID:21761505

Network analysis of inter-organizational relationships and policy use among active living organizations in Alberta, Canada.

PubMed

Loitz, Christina C; Stearns, Jodie A; Fraser, Shawn N; Storey, Kate; Spence, John C

2017-08-09

Coordinated partnerships and collaborations can optimize the efficiency and effectiveness of service and program delivery in organizational networks. However, the extent to which organizations are working together to promote physical activity, and use physical activity policies in Canada, is unknown. This project sought to provide a snapshot of the funding, coordination and partnership relationships among provincial active living organizations (ALOs) in Alberta, Canada. Additionally, the awareness, and use of the provincial policy and national strategy by the organizations was examined. Provincial ALOs (N = 27) answered questions regarding their funding, coordination and partnership connections with other ALOs in the network. Social network analysis was employed to examine network structure and position of each ALO. Discriminant function analysis determined the extent to which degree centrality was associated with the use of the Active Alberta (AA) policy and Active Canada 20/20 (AC 20/20) strategy. The funding network had a low density level (density = .20) and was centralized around Alberta Tourism Parks and Recreation (ATPR; degree centralization = 48.77%, betweenness centralization = 32.43%). The coordination network had a moderate density level (density = .31), and was low-to-moderately centralized around a few organizations (degree centralization = 45.37%, betweenness centrality = 19.92%). The partnership network had a low density level (density = .15), and was moderate-to-highly centralized around ATPR. Most organizations were aware of AA (89%) and AC 20/20 (78%), however more were using AA (67%) compared to AC 20/20 (33%). Central ALOs in the funding network were more likely to use AA and AC 20/20. Central ALOs in the coordination network were more likely to use AC 20/20, but not AA. Increasing formal and informal relationships between organizations and integrating disconnected or peripheral organizations could increase the capacity of the network to promote active living across Alberta. Uptake of the AA policy within the network is high and appears to be facilitated by the most central ALO. Promoting policy use through a central organization appeared to be an effective strategy for disseminating the province-level physical activity policy and could be considered as a policy-uptake strategy by other regions.

Human Impacts and Climate Change Influence Nestedness and Modularity in Food-Web and Mutualistic Networks.

PubMed

Takemoto, Kazuhiro; Kajihara, Kosuke

2016-01-01

Theoretical studies have indicated that nestedness and modularity-non-random structural patterns of ecological networks-influence the stability of ecosystems against perturbations; as such, climate change and human activity, as well as other sources of environmental perturbations, affect the nestedness and modularity of ecological networks. However, the effects of climate change and human activities on ecological networks are poorly understood. Here, we used a spatial analysis approach to examine the effects of climate change and human activities on the structural patterns of food webs and mutualistic networks, and found that ecological network structure is globally affected by climate change and human impacts, in addition to current climate. In pollination networks, for instance, nestedness increased and modularity decreased in response to increased human impacts. Modularity in seed-dispersal networks decreased with temperature change (i.e., warming), whereas food web nestedness increased and modularity declined in response to global warming. Although our findings are preliminary owing to data-analysis limitations, they enhance our understanding of the effects of environmental change on ecological communities.

Correspondence of the brain's functional architecture during activation and rest.

PubMed

Smith, Stephen M; Fox, Peter T; Miller, Karla L; Glahn, David C; Fox, P Mickle; Mackay, Clare E; Filippini, Nicola; Watkins, Kate E; Toro, Roberto; Laird, Angela R; Beckmann, Christian F

2009-08-04

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is "at rest." In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically "active" even when at "rest."

Graph theoretical analysis of functional network for comprehension of sign language.

PubMed

Liu, Lanfang; Yan, Xin; Liu, Jin; Xia, Mingrui; Lu, Chunming; Emmorey, Karen; Chu, Mingyuan; Ding, Guosheng

2017-09-15

Signed languages are natural human languages using the visual-motor modality. Previous neuroimaging studies based on univariate activation analysis show that a widely overlapped cortical network is recruited regardless whether the sign language is comprehended (for signers) or not (for non-signers). Here we move beyond previous studies by examining whether the functional connectivity profiles and the underlying organizational structure of the overlapped neural network may differ between signers and non-signers when watching sign language. Using graph theoretical analysis (GTA) and fMRI, we compared the large-scale functional network organization in hearing signers with non-signers during the observation of sentences in Chinese Sign Language. We found that signed sentences elicited highly similar cortical activations in the two groups of participants, with slightly larger responses within the left frontal and left temporal gyrus in signers than in non-signers. Crucially, further GTA revealed substantial group differences in the topologies of this activation network. Globally, the network engaged by signers showed higher local efficiency (t (24) =2.379, p=0.026), small-worldness (t (24) =2.604, p=0.016) and modularity (t (24) =3.513, p=0.002), and exhibited different modular structures, compared to the network engaged by non-signers. Locally, the left ventral pars opercularis served as a network hub in the signer group but not in the non-signer group. These findings suggest that, despite overlap in cortical activation, the neural substrates underlying sign language comprehension are distinguishable at the network level from those for the processing of gestural action. Copyright © 2017 Elsevier B.V. All rights reserved.

Passive and Active Analysis in DSR-Based Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Dempsey, Tae; Sahin, Gokhan; Morton, Y. T. (Jade)

Security and vulnerabilities in wireless ad hoc networks have been considered at different layers, and many attack strategies have been proposed, including denial of service (DoS) through the intelligent jamming of the most critical packet types of flows in a network. This paper investigates the effectiveness of intelligent jamming in wireless ad hoc networks using the Dynamic Source Routing (DSR) and TCP protocols and introduces an intelligent classifier to facilitate the jamming of such networks. Assuming encrypted packet headers and contents, our classifier is based solely on the observable characteristics of size, inter-arrival timing, and direction and classifies packets with up to 99.4% accuracy in our experiments. Furthermore, we investigate active analysis, which is the combination of a classifier and intelligent jammer to invoke specific responses from a victim network.

Coupling effect of nodes popularity and similarity on social network persistence

PubMed Central

Jin, Xiaogang; Jin, Cheng; Huang, Jiaxuan; Min, Yong

2017-01-01

Network robustness represents the ability of networks to withstand failures and perturbations. In social networks, maintenance of individual activities, also called persistence, is significant towards understanding robustness. Previous works usually consider persistence on pre-generated network structures; while in social networks, the network structure is growing with the cascading inactivity of existed individuals. Here, we address this challenge through analysis for nodes under a coevolution model, which characterizes individual activity changes under three network growth modes: following the descending order of nodes’ popularity, similarity or uniform random. We show that when nodes possess high spontaneous activities, a popularity-first growth mode obtains highly persistent networks; otherwise, with low spontaneous activities, a similarity-first mode does better. Moreover, a compound growth mode, with the consecutive joining of similar nodes in a short period and mixing a few high popularity nodes, obtains the highest persistence. Therefore, nodes similarity is essential for persistent social networks, while properly coupling popularity with similarity further optimizes the persistence. This demonstrates the evolution of nodes activity not only depends on network topology, but also their connective typology. PMID:28220840

Coupling effect of nodes popularity and similarity on social network persistence

NASA Astrophysics Data System (ADS)

Jin, Xiaogang; Jin, Cheng; Huang, Jiaxuan; Min, Yong

2017-02-01

Network robustness represents the ability of networks to withstand failures and perturbations. In social networks, maintenance of individual activities, also called persistence, is significant towards understanding robustness. Previous works usually consider persistence on pre-generated network structures; while in social networks, the network structure is growing with the cascading inactivity of existed individuals. Here, we address this challenge through analysis for nodes under a coevolution model, which characterizes individual activity changes under three network growth modes: following the descending order of nodes’ popularity, similarity or uniform random. We show that when nodes possess high spontaneous activities, a popularity-first growth mode obtains highly persistent networks; otherwise, with low spontaneous activities, a similarity-first mode does better. Moreover, a compound growth mode, with the consecutive joining of similar nodes in a short period and mixing a few high popularity nodes, obtains the highest persistence. Therefore, nodes similarity is essential for persistent social networks, while properly coupling popularity with similarity further optimizes the persistence. This demonstrates the evolution of nodes activity not only depends on network topology, but also their connective typology.

Coupling effect of nodes popularity and similarity on social network persistence.

PubMed

Jin, Xiaogang; Jin, Cheng; Huang, Jiaxuan; Min, Yong

2017-02-21

Network robustness represents the ability of networks to withstand failures and perturbations. In social networks, maintenance of individual activities, also called persistence, is significant towards understanding robustness. Previous works usually consider persistence on pre-generated network structures; while in social networks, the network structure is growing with the cascading inactivity of existed individuals. Here, we address this challenge through analysis for nodes under a coevolution model, which characterizes individual activity changes under three network growth modes: following the descending order of nodes' popularity, similarity or uniform random. We show that when nodes possess high spontaneous activities, a popularity-first growth mode obtains highly persistent networks; otherwise, with low spontaneous activities, a similarity-first mode does better. Moreover, a compound growth mode, with the consecutive joining of similar nodes in a short period and mixing a few high popularity nodes, obtains the highest persistence. Therefore, nodes similarity is essential for persistent social networks, while properly coupling popularity with similarity further optimizes the persistence. This demonstrates the evolution of nodes activity not only depends on network topology, but also their connective typology.

Stochastic cycle selection in active flow networks.

PubMed

Woodhouse, Francis G; Forrow, Aden; Fawcett, Joanna B; Dunkel, Jörn

2016-07-19

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models.

Stochastic cycle selection in active flow networks

NASA Astrophysics Data System (ADS)

Woodhouse, Francis; Forrow, Aden; Fawcett, Joanna; Dunkel, Jorn

2016-11-01

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such non-equilibrium networks. By connecting concepts from lattice field theory, graph theory and transition rate theory, we show how topology controls dynamics in a generic model for actively driven flow on a network. Through theoretical and numerical analysis we identify symmetry-based rules to classify and predict the selection statistics of complex flow cycles from the network topology. Our conceptual framework is applicable to a broad class of biological and non-biological far-from-equilibrium networks, including actively controlled information flows, and establishes a new correspondence between active flow networks and generalized ice-type models.

Stochastic cycle selection in active flow networks

PubMed Central

Woodhouse, Francis G.; Forrow, Aden; Fawcett, Joanna B.; Dunkel, Jörn

2016-01-01

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models. PMID:27382186

DOE Office of Scientific and Technical Information (OSTI.GOV)

Law, B E

Research involves analysis and field direction of AmeriFlux operations, and the PI provides scientific leadership of the AmeriFlux network. Activities include the coordination and quality assurance of measurements across AmeriFlux network sites, synthesis of results across the network, organizing and supporting the annual Science Team Meeting, and communicating AmeriFlux results to the scientific community and other users. Objectives of measurement research include (i) coordination of flux and biometric measurement protocols (ii) timely data delivery to the Carbon Dioxide Information and Analysis Center (CDIAC); and (iii) assurance of data quality of flux and ecosystem measurements contributed by AmeriFlux sites. Objectives ofmore » integration and synthesis activities include (i) integration of site data into network-wide synthesis products; and (ii) participation in the analysis, modeling and interpretation of network data products. Communications objectives include (i) organizing an annual meeting of AmeriFlux investigators for reporting annual flux measurements and exchanging scientific information on ecosystem carbon budgets; (ii) developing focused topics for analysis and publication; and (iii) developing data reporting protocols in support of AmeriFlux network goals.« less

Combining Flux Balance and Energy Balance Analysis for Large-Scale Metabolic Network: Biochemical Circuit Theory for Analysis of Large-Scale Metabolic Networks

NASA Technical Reports Server (NTRS)

Beard, Daniel A.; Liang, Shou-Dan; Qian, Hong; Biegel, Bryan (Technical Monitor)

2001-01-01

Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.

Exploring sets of molecules from patents and relationships to other active compounds in chemical space networks

NASA Astrophysics Data System (ADS)

Kunimoto, Ryo; Bajorath, Jürgen

2017-09-01

Patents from medicinal chemistry represent a rich source of novel compounds and activity data that appear only infrequently in the scientific literature. Moreover, patent information provides a primary focal point for drug discovery. Accordingly, text mining and image extraction approaches have become hot topics in patent analysis and repositories of patent data are being established. In this work, we have generated network representations using alternative similarity measures to systematically compare molecules from patents with other bioactive compounds, visualize similarity relationships, explore the chemical neighbourhood of patent molecules, and identify closely related compounds with different activities. The design of network representations that combine patent molecules and other bioactive compounds and view patent information in the context of current bioactive chemical space aids in the analysis of patents and further extends the use of molecular networks to explore structure-activity relationships.

Research Activity in Computational Physics utilizing High Performance Computing: Co-authorship Network Analysis

NASA Astrophysics Data System (ADS)

Ahn, Sul-Ah; Jung, Youngim

2016-10-01

The research activities of the computational physicists utilizing high performance computing are analyzed by bibliometirc approaches. This study aims at providing the computational physicists utilizing high-performance computing and policy planners with useful bibliometric results for an assessment of research activities. In order to achieve this purpose, we carried out a co-authorship network analysis of journal articles to assess the research activities of researchers for high-performance computational physics as a case study. For this study, we used journal articles of the Scopus database from Elsevier covering the time period of 2004-2013. We extracted the author rank in the physics field utilizing high-performance computing by the number of papers published during ten years from 2004. Finally, we drew the co-authorship network for 45 top-authors and their coauthors, and described some features of the co-authorship network in relation to the author rank. Suggestions for further studies are discussed.

Multilayer motif analysis of brain networks

NASA Astrophysics Data System (ADS)

Battiston, Federico; Nicosia, Vincenzo; Chavez, Mario; Latora, Vito

2017-04-01

In the last decade, network science has shed new light both on the structural (anatomical) and on the functional (correlations in the activity) connectivity among the different areas of the human brain. The analysis of brain networks has made possible to detect the central areas of a neural system and to identify its building blocks by looking at overabundant small subgraphs, known as motifs. However, network analysis of the brain has so far mainly focused on anatomical and functional networks as separate entities. The recently developed mathematical framework of multi-layer networks allows us to perform an analysis of the human brain where the structural and functional layers are considered together. In this work, we describe how to classify the subgraphs of a multiplex network, and we extend the motif analysis to networks with an arbitrary number of layers. We then extract multi-layer motifs in brain networks of healthy subjects by considering networks with two layers, anatomical and functional, respectively, obtained from diffusion and functional magnetic resonance imaging. Results indicate that subgraphs in which the presence of a physical connection between brain areas (links at the structural layer) coexists with a non-trivial positive correlation in their activities are statistically overabundant. Finally, we investigate the existence of a reinforcement mechanism between the two layers by looking at how the probability to find a link in one layer depends on the intensity of the connection in the other one. Showing that functional connectivity is non-trivially constrained by the underlying anatomical network, our work contributes to a better understanding of the interplay between the structure and function in the human brain.

Analysing Health Professionals' Learning Interactions in an Online Social Network: A Longitudinal Study.

PubMed

Li, Xin; Verspoor, Karin; Gray, Kathleen; Barnett, Stephen

2016-01-01

This paper summarises a longitudinal analysis of learning interactions occurring over three years among health professionals in an online social network. The study employs the techniques of Social Network Analysis (SNA) and statistical modeling to identify the changes in patterns of interaction over time and test associated structural network effects. SNA results indicate overall low participation in the network, although some participants became active over time and even led discussions. In particular, the analysis has shown that a change of lead contributor results in a change in learning interaction and network structure. The analysis of structural network effects demonstrates that the interaction dynamics slow down over time, indicating that interactions in the network are more stable. The health professionals may be reluctant to share knowledge and collaborate in groups but were interested in building personal learning networks or simply seeking information.

System Analysis for the Huntsville Operation Support Center, Distributed Computer System

NASA Technical Reports Server (NTRS)

Ingels, F. M.; Massey, D.

1985-01-01

HOSC as a distributed computing system, is responsible for data acquisition and analysis during Space Shuttle operations. HOSC also provides computing services for Marshall Space Flight Center's nonmission activities. As mission and nonmission activities change, so do the support functions of HOSC change, demonstrating the need for some method of simulating activity at HOSC in various configurations. The simulation developed in this work primarily models the HYPERchannel network. The model simulates the activity of a steady state network, reporting statistics such as, transmitted bits, collision statistics, frame sequences transmitted, and average message delay. These statistics are used to evaluate such performance indicators as throughout, utilization, and delay. Thus the overall performance of the network is evaluated, as well as predicting possible overload conditions.

The Contribution of Extracurricular Activities to Adolescent Friendships: New Insights through Social Network Analysis

ERIC Educational Resources Information Center

Schaefer, David R.; Simpkins, Sandra D.; Vest, Andrea E.; Price, Chara D.

2011-01-01

Extracurricular activities are settings that are theorized to help adolescents maintain existing friendships and develop new friendships. The overarching goal of the current investigation was to examine whether coparticipating in school-based extracurricular activities supported adolescents' school-based friendships. We used social network methods…

Putting age-related task activation into large-scale brain networks: A meta-analysis of 114 fMRI studies on healthy aging.

PubMed

Li, Hui-Jie; Hou, Xiao-Hui; Liu, Han-Hui; Yue, Chun-Lin; Lu, Guang-Ming; Zuo, Xi-Nian

2015-10-01

Normal aging is associated with cognitive decline and underlying brain dysfunction. Previous studies concentrated less on brain network changes at a systems level. Our goal was to examine these age-related changes of fMRI-derived activation with a common network parcellation of the human brain function, offering a systems-neuroscience perspective of healthy aging. We conducted a series of meta-analyses on a total of 114 studies that included 2035 older adults and 1845 young adults. Voxels showing significant age-related changes in activation were then overlaid onto seven commonly referenced neuronal networks. Older adults present moderate cognitive decline in behavioral performance during fMRI scanning, and hypo-activate the visual network and hyper-activate both the frontoparietal control and default mode networks. The degree of increased activation in frontoparietal network was associated with behavioral performance in older adults. Age-related changes in activation present different network patterns across cognitive domains. The systems neuroscience approach used here may be useful for elucidating the underlying network mechanisms of various brain plasticity processes during healthy aging. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Latino Civic Group Participation, Social Networks, and Physical Activity.

PubMed

Marquez, Becky; Gonzalez, Patricia; Gallo, Linda; Ji, Ming

2016-07-01

We examined whether social networks and resource awareness for physical activity may mediate the relationship between civic group participation and physical activity. This is a cross-sectional study of a randomly selected sample of 335 Latinos (mean age 42.1 ± 16.4 years) participating in the San Diego Prevention Research Center's 2009 Household Community Survey. Serial multiple mediation analysis tested the hypothesis that civic group participation is associated with meeting physical activity recommendations through an indirect mechanism of larger social networks followed by greater knowledge of physical activity community resources. The indirect effects of level of civic group participation as well as religious, health, neighborhood, or arts group participation on meeting national physical activity recommendations were significant in models testing pathways through social network size and physical activity resource awareness. The direct effect was only significant for health group indicating that participating in a health group predicted physical activity independent of social network size and awareness of physical activity resources. Belonging to civic groups may promote physical activity engagement through social network diffusion of information on community physical activity resources which has implications for health.

Temporal Sequence of Hemispheric Network Activation during Semantic Processing: A Functional Network Connectivity Analysis

ERIC Educational Resources Information Center

Assaf, Michal; Jagannathan, Kanchana; Calhoun, Vince; Kraut, Michael; Hart, John, Jr.; Pearlson, Godfrey

2009-01-01

To explore the temporal sequence of, and the relationship between, the left and right hemispheres (LH and RH) during semantic memory (SM) processing we identified the neural networks involved in the performance of functional MRI semantic object retrieval task (SORT) using group independent component analysis (ICA) in 47 healthy individuals. SORT…

Network Interventions on Physical Activity in an Afterschool Program: An Agent-Based Social Network Study

PubMed Central

Zhang, Jun; Shoham, David A.; Tesdahl, Eric

2015-01-01

Objectives. We studied simulated interventions that leveraged social networks to increase physical activity in children. Methods. We studied a real-world social network of 81 children (average age = 7.96 years) who lived in low socioeconomic status neighborhoods, and attended public schools and 1 of 2 structured afterschool programs. The sample was ethnically diverse, and 44% were overweight or obese. We used social network analysis and agent-based modeling simulations to test whether implementing a network intervention would increase children’s physical activity. We tested 3 intervention strategies. Results. The intervention that targeted opinion leaders was effective in increasing the average level of physical activity across the entire network. However, the intervention that targeted the most sedentary children was the best at increasing their physical activity levels. Conclusions. Which network intervention to implement depends on whether the goal is to shift the entire distribution of physical activity or to influence those most adversely affected by low physical activity. Agent-based modeling could be an important complement to traditional project planning tools, analogous to sample size and power analyses, to help researchers design more effective interventions for increasing children’s physical activity. PMID:25689202

Toward systems neuroscience in mild cognitive impairment and Alzheimer's disease: a meta-analysis of 75 fMRI studies.

PubMed

Li, Hui-Jie; Hou, Xiao-Hui; Liu, Han-Hui; Yue, Chun-Lin; He, Yong; Zuo, Xi-Nian

2015-03-01

Most of the previous task functional magnetic resonance imaging (fMRI) studies found abnormalities in distributed brain regions in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and few studies investigated the brain network dysfunction from the system level. In this meta-analysis, we aimed to examine brain network dysfunction in MCI and AD. We systematically searched task-based fMRI studies in MCI and AD published between January 1990 and January 2014. Activation likelihood estimation meta-analyses were conducted to compare the significant group differences in brain activation, the significant voxels were overlaid onto seven referenced neuronal cortical networks derived from the resting-state fMRI data of 1,000 healthy participants. Thirty-nine task-based fMRI studies (697 MCI patients and 628 healthy controls) were included in MCI-related meta-analysis while 36 task-based fMRI studies (421 AD patients and 512 healthy controls) were included in AD-related meta-analysis. The meta-analytic results revealed that MCI and AD showed abnormal regional brain activation as well as large-scale brain networks. MCI patients showed hypoactivation in default, frontoparietal, and visual networks relative to healthy controls, whereas AD-related hypoactivation mainly located in visual, default, and ventral attention networks relative to healthy controls. Both MCI-related and AD-related hyperactivation fell in frontoparietal, ventral attention, default, and somatomotor networks relative to healthy controls. MCI and AD presented different pathological while shared similar compensatory large-scale networks in fulfilling the cognitive tasks. These system-level findings are helpful to link the fundamental declines of cognitive tasks to brain networks in MCI and AD. © 2014 Wiley Periodicals, Inc.

Network analysis of Bogotá's Ciclovía Recreativa, a self-organized multisectorial community program to promote physical activity in a middle-income country.

PubMed

Meisel, Jose D; Sarmiento, Olga L; Montes, Felipe; Martinez, Edwin O; Lemoine, Pablo D; Valdivia, Juan A; Brownson, Ross C; Zarama, Roberto

2014-01-01

Conduct a social network analysis of the health and non-health related organizations that participate in Bogotá's Ciclovía Recreativa (Ciclovía). Cross-sectional study. Ciclovía is a multisectoral community-based mass program in which streets are temporarily closed to motorized transport, allowing exclusive access to individuals for leisure activities and physical activity. Twenty-five organizations that participate in the Ciclovía. Seven variables were examined by using network analytic methods: relationship, link attributes (integration, contact, and importance), and node attributes (leadership, years in the program, and the sector of the organization). The network analytic methods were based on a visual descriptive analysis and an exponential random graph model. Analysis shows that the most central organizations in the network were outside of the Health sector and include Sports and Recreation, Government, and Security sectors. The organizations work in clusters formed by organizations of different sectors. Organization importance and structural predictors were positively related to integration, while the number of years working with Ciclovía was negatively associated with integration. Ciclovía is a network whose structure emerged as a self-organized complex system. Ciclovía of Bogotá is an example of a program with public health potential formed by organizations of multiple sectors with Sports and Recreation as the most central.

Model Of Neural Network With Creative Dynamics

NASA Technical Reports Server (NTRS)

Zak, Michail; Barhen, Jacob

1993-01-01

Paper presents analysis of mathematical model of one-neuron/one-synapse neural network featuring coupled activation and learning dynamics and parametrical periodic excitation. Demonstrates self-programming, partly random behavior of suitable designed neural network; believed to be related to spontaneity and creativity of biological neural networks.

Default-Mode Network Functional Connectivity in Aphasia: Therapy-Induced Neuroplasticity

ERIC Educational Resources Information Center

Marcotte, Karine; Perlbarg, Vincent; Marrelec, Guillaume; Benali, Habib; Ansaldo, Ana Ines

2013-01-01

Previous research on participants with aphasia has mainly been based on standard functional neuroimaging analysis. Recent studies have shown that functional connectivity analysis can detect compensatory activity, not revealed by standard analysis. Little is known, however, about the default-mode network in aphasia. In the current study, we studied…

Social Network Analysis as a Methodological Approach to Explore Health Systems: A Case Study Exploring Support among Senior Managers/Executives in a Hospital Network.

PubMed

De Brún, Aoife; McAuliffe, Eilish

2018-03-13

Health systems research recognizes the complexity of healthcare, and the interacting and interdependent nature of components of a health system. To better understand such systems, innovative methods are required to depict and analyze their structures. This paper describes social network analysis as a methodology to depict, diagnose, and evaluate health systems and networks therein. Social network analysis is a set of techniques to map, measure, and analyze social relationships between people, teams, and organizations. Through use of a case study exploring support relationships among senior managers in a newly established hospital group, this paper illustrates some of the commonly used network- and node-level metrics in social network analysis, and demonstrates the value of these maps and metrics to understand systems. Network analysis offers a valuable approach to health systems and services researchers as it offers a means to depict activity relevant to network questions of interest, to identify opinion leaders, influencers, clusters in the network, and those individuals serving as bridgers across clusters. The strengths and limitations inherent in the method are discussed, and the applications of social network analysis in health services research are explored.

Neural Systems Underlying Individual Differences in Intertemporal Decision-making.

PubMed

Elton, Amanda; Smith, Christopher T; Parrish, Michael H; Boettiger, Charlotte A

2017-03-01

Excessively choosing immediate over larger future rewards, or delay discounting (DD), associates with multiple clinical conditions. Individual differences in DD likely depend on variations in the activation of and functional interactions between networks, representing possible endophenotypes for associated disorders, including alcohol use disorders (AUDs). Numerous fMRI studies have probed the neural bases of DD, but investigations of large-scale networks remain scant. We addressed this gap by testing whether activation within large-scale networks during Now/Later decision-making predicts individual differences in DD. To do so, we scanned 95 social drinkers (18-40 years old; 50 women) using fMRI during hypothetical choices between small monetary amounts available "today" or larger amounts available later. We identified neural networks engaged during Now/Later choice using independent component analysis and tested the relationship between component activation and degree of DD. The activity of two components during Now/Later choice correlated with individual DD rates: A temporal lobe network positively correlated with DD, whereas a frontoparietal-striatal network negatively correlated with DD. Activation differences between these networks predicted individual differences in DD, and their negative correlation during Now/Later choice suggests functional competition. A generalized psychophysiological interactions analysis confirmed a decrease in their functional connectivity during decision-making. The functional connectivity of these two networks negatively correlates with alcohol-related harm, potentially implicating these networks in AUDs. These findings provide novel insight into the neural underpinnings of individual differences in impulsive decision-making with potential implications for addiction and related disorders in which impulsivity is a defining feature.

Measuring, Understanding, and Responding to Covert Social Networks: Passive and Active Tomography

DTIC Science & Technology

2017-11-29

Methods for generating a random sample of networks with desired properties are important tools for the analysis of social , biological, and information...on Theoretical Foundations for Statistical Network Analysis at the Isaac Newton Institute for Mathematical Sciences at Cambridge U. (organized by...Approach SOCIAL SCIENCES STATISTICS EECS Problems span three disciplines Scientific focus is needed at the interfaces

Time-Varying Networks of Inter-Ictal Discharging Reveal Epileptogenic Zone.

PubMed

Zhang, Luyan; Liang, Yi; Li, Fali; Sun, Hongbin; Peng, Wenjing; Du, Peishan; Si, Yajing; Song, Limeng; Yu, Liang; Xu, Peng

2017-01-01

The neuronal synchronous discharging may cause an epileptic seizure. Currently, most of the studies conducted to investigate the mechanism of epilepsy are based on EEGs or functional magnetic resonance imaging (fMRI) recorded during the ictal discharging or the resting-state, and few studies have probed into the dynamic patterns during the inter-ictal discharging that are much easier to record in clinical applications. Here, we propose a time-varying network analysis based on adaptive directed transfer function to uncover the dynamic brain network patterns during the inter-ictal discharging. In addition, an algorithm based on the time-varying outflow of information derived from the network analysis is developed to detect the epileptogenic zone. The analysis performed revealed the time-varying network patterns during different stages of inter-ictal discharging; the epileptogenic zone was activated prior to the discharge onset then worked as the source to propagate the activity to other brain regions. Consistence between the epileptogenic zones detected by our proposed approach and the actual epileptogenic zones proved that time-varying network analysis could not only reveal the underlying neural mechanism of epilepsy, but also function as a useful tool in detecting the epileptogenic zone based on the EEGs in the inter-ictal discharging.

Correspondence of the brain's functional architecture during activation and rest

PubMed Central

Smith, Stephen M.; Fox, Peter T.; Miller, Karla L.; Glahn, David C.; Fox, P. Mickle; Mackay, Clare E.; Filippini, Nicola; Watkins, Kate E.; Toro, Roberto; Laird, Angela R.; Beckmann, Christian F.

2009-01-01

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is “at rest.” In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically “active” even when at “rest.” PMID:19620724

Gender differences in working memory networks: A BrainMap meta-analysis

PubMed Central

Hill, Ashley C.; Laird, Angela R.; Robinson, Jennifer L.

2014-01-01

Gender differences in psychological processes have been of great interest in a variety of fields. While the majority of research in this area has focused on specific differences in relation to test performance, this study sought to determine the underlying neurofunctional differences observed during working memory, a pivotal cognitive process shown to be predictive of academic achievement and intelligence. Using the BrainMap database, we performed a meta-analysis and applied activation likelihood estimation to our search set. Our results demonstrate consistent working memory networks across genders, but also provide evidence for gender-specific networks whereby females consistently activate more limbic (e.g., amygdala and hippocampus) and prefrontal structures (e.g., right inferior frontal gyrus), and males activate a distributed network inclusive of more parietal regions. These data provide a framework for future investigation using functional or effective connectivity methods to elucidate the underpinnings of gender differences in neural network recruitment during working memory tasks. PMID:25042764

Gender differences in working memory networks: a BrainMap meta-analysis.

PubMed

Hill, Ashley C; Laird, Angela R; Robinson, Jennifer L

2014-10-01

Gender differences in psychological processes have been of great interest in a variety of fields. While the majority of research in this area has focused on specific differences in relation to test performance, this study sought to determine the underlying neurofunctional differences observed during working memory, a pivotal cognitive process shown to be predictive of academic achievement and intelligence. Using the BrainMap database, we performed a meta-analysis and applied activation likelihood estimation to our search set. Our results demonstrate consistent working memory networks across genders, but also provide evidence for gender-specific networks whereby females consistently activate more limbic (e.g., amygdala and hippocampus) and prefrontal structures (e.g., right inferior frontal gyrus), and males activate a distributed network inclusive of more parietal regions. These data provide a framework for future investigations using functional or effective connectivity methods to elucidate the underpinnings of gender differences in neural network recruitment during working memory tasks. Copyright © 2014 Elsevier B.V. All rights reserved.

NeuroCa: integrated framework for systematic analysis of spatiotemporal neuronal activity patterns from large-scale optical recording data

PubMed Central

Jang, Min Jee; Nam, Yoonkey

2015-01-01

Abstract. Optical recording facilitates monitoring the activity of a large neural network at the cellular scale, but the analysis and interpretation of the collected data remain challenging. Here, we present a MATLAB-based toolbox, named NeuroCa, for the automated processing and quantitative analysis of large-scale calcium imaging data. Our tool includes several computational algorithms to extract the calcium spike trains of individual neurons from the calcium imaging data in an automatic fashion. Two algorithms were developed to decompose the imaging data into the activity of individual cells and subsequently detect calcium spikes from each neuronal signal. Applying our method to dense networks in dissociated cultures, we were able to obtain the calcium spike trains of ∼1000 neurons in a few minutes. Further analyses using these data permitted the quantification of neuronal responses to chemical stimuli as well as functional mapping of spatiotemporal patterns in neuronal firing within the spontaneous, synchronous activity of a large network. These results demonstrate that our method not only automates time-consuming, labor-intensive tasks in the analysis of neural data obtained using optical recording techniques but also provides a systematic way to visualize and quantify the collective dynamics of a network in terms of its cellular elements. PMID:26229973

Different propagation speeds of recalled sequences in plastic spiking neural networks

NASA Astrophysics Data System (ADS)

Huang, Xuhui; Zheng, Zhigang; Hu, Gang; Wu, Si; Rasch, Malte J.

2015-03-01

Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in experiments.

Effect of Field Spread on Resting-State Magneto Encephalography Functional Network Analysis: A Computational Modeling Study.

PubMed

Silva Pereira, Silvana; Hindriks, Rikkert; Mühlberg, Stefanie; Maris, Eric; van Ede, Freek; Griffa, Alessandra; Hagmann, Patric; Deco, Gustavo

2017-11-01

A popular way to analyze resting-state electroencephalography (EEG) and magneto encephalography (MEG) data is to treat them as a functional network in which sensors are identified with nodes and the interaction between channel time series and the network connections. Although conceptually appealing, the network-theoretical approach to sensor-level EEG and MEG data is challenged by the fact that EEG and MEG time series are mixtures of source activity. It is, therefore, of interest to assess the relationship between functional networks of source activity and the ensuing sensor-level networks. Since these topological features are of high interest in experimental studies, we address the question of to what extent the network topology can be reconstructed from sensor-level functional connectivity (FC) measures in case of MEG data. Simple simulations that consider only a small number of regions do not allow to assess network properties; therefore, we use a diffusion magnetic resonance imaging-constrained whole-brain computational model of resting-state activity. Our motivation lies behind the fact that still many contributions found in the literature perform network analysis at sensor level, and we aim at showing the discrepancies between source- and sensor-level network topologies by using realistic simulations of resting-state cortical activity. Our main findings are that the effect of field spread on network topology depends on the type of interaction (instantaneous or lagged) and leads to an underestimation of lagged FC at sensor level due to instantaneous mixing of cortical signals, instantaneous interaction is more sensitive to field spread than lagged interaction, and discrepancies are reduced when using planar gradiometers rather than axial gradiometers. We, therefore, recommend using lagged interaction measures on planar gradiometer data when investigating network properties of resting-state sensor-level MEG data.

Network analysis of a regional fishery: Implications for management of natural resources, and recruitment and retention of anglers

USGS Publications Warehouse

Martin, Dustin R.; Shizuka, Daizaburo; Chizinski, Christopher J.; Pope, Kevin L.

2017-01-01

Angler groups and water-body types interact to create a complex social-ecological system. Network analysis could inform detailed mechanistic models on, and provide managers better information about, basic patterns of fishing activity. Differences in behavior and reservoir selection among angler groups in a regional fishery, the Salt Valley fishery in southeastern Nebraska, USA, were assessed using a combination of cluster and network analyses. The four angler groups assessed ranged from less active, unskilled anglers (group One) to highly active, very skilled anglers (group Four). Reservoir use patterns and the resulting network communities of these four angler groups differed; the number of reservoir communities for these groups ranged from two to three and appeared to be driven by reservoir location (group One), reservoir size and its associated attributes (groups Two and Four), or an interaction between reservoir size and location (group Three). Network analysis is a useful tool to describe differences in participation among angler groups within a regional fishery, and provides new insights about possible recruitment of anglers. For example, group One anglers fished reservoirs closer to home and had a greater probability of dropping out if local reservoir access were restricted.

A computer program for the generation of logic networks from task chart data

NASA Technical Reports Server (NTRS)

Herbert, H. E.

1980-01-01

The Network Generation Program (NETGEN), which creates logic networks from task chart data is presented. NETGEN is written in CDC FORTRAN IV (Extended) and runs in a batch mode on the CDC 6000 and CYBER 170 series computers. Data is input via a two-card format and contains information regarding the specific tasks in a project. From this data, NETGEN constructs a logic network of related activities with each activity having unique predecessor and successor nodes, activity duration, descriptions, etc. NETGEN then prepares this data on two files that can be used in the Project Planning Analysis and Reporting System Batch Network Scheduling program and the EZPERT graphics program.

Functional network connectivity analysis based on partial correlation in Alzheimer's disease

NASA Astrophysics Data System (ADS)

Zhang, Nan; Guan, Xiaoting; Zhang, Yumei; Li, Jingjing; Chen, Hongyan; Chen, Kewei; Fleisher, Adam; Yao, Li; Wu, Xia

2009-02-01

Functional network connectivity (FNC) measures the temporal dependency among the time courses of functional networks. However, the marginal correlation between two networks used in the classic FNC analysis approach doesn't separate the FNC from the direct/indirect effects of other networks. In this study, we proposed an alternative approach based on partial correlation to evaluate the FNC, since partial correlation based FNC can reveal the direct interaction between a pair of networks, removing dependencies or influences from others. Previous studies have demonstrated less task-specific activation and less rest-state activity in Alzheimer's disease (AD). We applied present approach to contrast FNC differences of resting state network (RSN) between AD and normal controls (NC). The fMRI data under resting condition were collected from 15 AD and 16 NC. FNC was calculated for each pair of six RSNs identified using Group ICA, thus resulting in 15 (2 out of 6) pairs for each subject. Partial correlation based FNC analysis indicated 6 pairs significant differences between groups, while marginal correlation only revealed 2 pairs (involved in the partial correlation results). Additionally, patients showed lower correlation than controls among most of the FNC differences. Our results provide new evidences for the disconnection hypothesis in AD.

Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging

PubMed Central

Patel, Tapan P.; Man, Karen; Firestein, Bonnie L.; Meaney, David F.

2017-01-01

Background Recent advances in genetically engineered calcium and membrane potential indicators provide the potential to estimate the activation dynamics of individual neurons within larger, mesoscale networks (100s–1000 +neurons). However, a fully integrated automated workflow for the analysis and visualization of neural microcircuits from high speed fluorescence imaging data is lacking. New method Here we introduce FluoroSNNAP, Fluorescence Single Neuron and Network Analysis Package. FluoroSNNAP is an open-source, interactive software developed in MATLAB for automated quantification of numerous biologically relevant features of both the calcium dynamics of single-cells and network activity patterns. FluoroSNNAP integrates and improves upon existing tools for spike detection, synchronization analysis, and inference of functional connectivity, making it most useful to experimentalists with little or no programming knowledge. Results We apply FluoroSNNAP to characterize the activity patterns of neuronal microcircuits undergoing developmental maturation in vitro. Separately, we highlight the utility of single-cell analysis for phenotyping a mixed population of neurons expressing a human mutant variant of the microtubule associated protein tau and wild-type tau. Comparison with existing method(s) We show the performance of semi-automated cell segmentation using spatiotemporal independent component analysis and significant improvement in detecting calcium transients using a template-based algorithm in comparison to peak-based or wavelet-based detection methods. Our software further enables automated analysis of microcircuits, which is an improvement over existing methods. Conclusions We expect the dissemination of this software will facilitate a comprehensive analysis of neuronal networks, promoting the rapid interrogation of circuits in health and disease. PMID:25629800

Analysis and Synthesis of Adaptive Neural Elements and Assemblies

DTIC Science & Technology

1992-12-14

network, a learning rule (activity-dependent neuromodulation ), which has been proposed as a cellular mechanism for classical conditioning , was...activity-dependent neuromodulation ), which has been proposed as a cellular mechanism for classical conditioning, was demonstrated to support many...network, a learning rule (activity-dependent neuromodulation ), which has been proposed as a cellular mechanism for classical conditioning, was

Imaging the where and when of tic generation and resting state networks in adult Tourette patients

PubMed Central

Neuner, Irene; Werner, Cornelius J.; Arrubla, Jorge; Stöcker, Tony; Ehlen, Corinna; Wegener, Hans P.; Schneider, Frank; Shah, N. Jon

2014-01-01

Introduction: Tourette syndrome (TS) is a neuropsychiatric disorder with the core phenomenon of tics, whose origin and temporal pattern are unclear. We investigated the When and Where of tic generation and resting state networks (RSNs) via functional magnetic resonance imaging (fMRI). Methods: Tic-related activity and the underlying RSNs in adult TS were studied within one fMRI session. Participants were instructed to lie in the scanner and to let tics occur freely. Tic onset times, as determined by video-observance were used as regressors and added to preceding time-bins of 1 s duration each to detect prior activation. RSN were identified by independent component analysis (ICA) and correlated to disease severity by the means of dual regression. Results: Two seconds before a tic, the supplementary motor area (SMA), ventral primary motor cortex, primary sensorimotor cortex and parietal operculum exhibited activation; 1 s before a tic, the anterior cingulate, putamen, insula, amygdala, cerebellum and the extrastriatal-visual cortex exhibited activation; with tic-onset, the thalamus, central operculum, primary motor and somatosensory cortices exhibited activation. Analysis of resting state data resulted in 21 components including the so-called default-mode network. Network strength in those regions in SMA of two premotor ICA maps that were also active prior to tic occurrence, correlated significantly with disease severity according to the Yale Global Tic Severity Scale (YGTTS) scores. Discussion: We demonstrate that the temporal pattern of tic generation follows the cortico-striato-thalamo-cortical circuit, and that cortical structures precede subcortical activation. The analysis of spontaneous fluctuations highlights the role of cortical premotor structures. Our study corroborates the notion of TS as a network disorder in which abnormal RSN activity might contribute to the generation of tics in SMA. PMID:24904391

Active learning of cortical connectivity from two-photon imaging data.

PubMed

Bertrán, Martín A; Martínez, Natalia L; Wang, Ye; Dunson, David; Sapiro, Guillermo; Ringach, Dario

2018-01-01

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this "active learning" method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model.

Active learning of cortical connectivity from two-photon imaging data

PubMed Central

Wang, Ye; Dunson, David; Sapiro, Guillermo; Ringach, Dario

2018-01-01

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this “active learning” method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model. PMID:29718955

Analysis of streamflow-gaging network for monitoring stormwater in small streams in the Puget Sound Basin, Washington

USGS Publications Warehouse

Konrad, Christopher P.; Voss, Frank D.

2012-01-01

The streamflow-gaging network in the Puget Sound basin was analyzed for its capacity to monitor stormwater in small streams. The analysis consisted of an inventory of active and inactive gages and an evaluation of the coverage and resolution of the gaging network with an emphasis on lowland areas. The active gaging network covers much of the Puget Lowland largely by gages located at sites on larger streams and rivers. Assessments of stormwater impacts and management will likely require streamflow information with higher spatial resolution than provided by the current gaging network. Monitoring that emphasizes small streams in combination with approaches for estimating streamflow at ungaged sites provides an alternative to expanding the current gaging network that can improve the spatial resolution of streamflow information in the region. The highest priority gaps in the gaging network are low elevation basins close to the Puget Sound shoreline and sites that share less than 10 percent of the drainage area of an active gage. Although small, lowland sites with long records of streamflow are particularly valuable to maintain in the region, other criteria for prioritizing sites in the gaging network should be based on the specific questions that stormwater managers need to answer.

Mapping the Cortical Network Arising From Up-Regulated Amygdaloidal Activation Using -Louvain Algorithm.

PubMed

Liu, Ning; Yu, Xueli; Yao, Li; Zhao, Xiaojie

2018-06-01

The amygdala plays an important role in emotion processing. Several studies have proved that its activation can be regulated by real-time functional magnetic resonance imaging (rtfMRI)-based neurofeedback training. However, although studies have found brain regions that are functionally closely connected to the amygdala in the cortex, it is not clear whether these brain regions and the amygdala are structurally closely connected, and if they show the same training effect as the amygdala in the process of emotional regulation. In this paper, we instructed subjects to up-regulate the activation of the left amygdala (LA) through rtfMRI-based neurofeedback training. In order to fuse multimodal imaging data, we introduced a network analysis method called the -Louvain clustering algorithm. This method was used to integrate multimodal data from the training experiment and construct an LA-cortical network. Correlation analysis and main-effect analysis were conducted to determine the signal covariance associated with the activation of the target area; ultimately, we identified the left temporal pole superior as the amygdaloidal-cortical network region. As a deep nucleus in the brain, the treatment and stimulation of the amygdala remains challenging. Our results provide new insights for the regulation of activation in a deep nucleus using more neurofeedback techniques.

Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays.

PubMed

Sanchez, Karla R; Mersha, Mahlet D; Dhillon, Harbinder S; Temburni, Murali K

2018-04-26

Bis-phenols, such as bis-phenol A (BPA) and bis-phenol-S (BPS), are polymerizing agents widely used in the production of plastics and numerous everyday products. They are classified as endocrine disrupting compounds (EDC) with estradiol-like properties. Long-term exposure to EDCs, even at low doses, has been linked with various health defects including cancer, behavioral disorders, and infertility, with greater vulnerability during early developmental periods. To study the effects of BPA on the development of neuronal function, we used an in vitro neuronal network derived from the early chick embryonic brain as a model. We found that exposure to BPA affected the development of network activity, specifically spiking activity and synchronization. A change in network activity is the crucial link between the molecular target of a drug or compound and its effect on behavioral outcome. Multi-electrode arrays are increasingly becoming useful tools to study the effects of drugs on network activity in vitro. There are several systems available in the market and, although there are variations in the number of electrodes, the type and quality of the electrode array and the analysis software, the basic underlying principles, and the data obtained is the same across the different systems. Although currently limited to analysis of two-dimensional in vitro cultures, these MEA systems are being improved to enable in vivo network activity in brain slices. Here, we provide a detailed protocol for embryonic exposure and recording neuronal network activity and synchrony, along with representative results.

Neural networks supporting switching, hypothesis testing, and rule application

PubMed Central

Liu, Zhiya; Braunlich, Kurt; Wehe, Hillary S.; Seger, Carol A.

2015-01-01

We identified dynamic changes in recruitment of neural connectivity networks across three phases of a flexible rule learning and set-shifting task similar to the Wisconsin Card Sort Task: switching, rule learning via hypothesis testing, and rule application. During fMRI scanning, subjects viewed pairs of stimuli that differed across four dimensions (letter, color, size, screen location), chose one stimulus, and received feedback. Subjects were informed that the correct choice was determined by a simple unidimensional rule, for example “choose the blue letter.” Once each rule had been learned and correctly applied for 4-7 trials, subjects were cued via either negative feedback or visual cues to switch to learning a new rule. Task performance was divided into three phases: Switching (first trial after receiving the switch cue), hypothesis testing (subsequent trials through the last error trial), and rule application (correct responding after the rule was learned). We used both univariate analysis to characterize activity occurring within specific regions of the brain, and a multivariate method, constrained principal component analysis for fMRI (fMRI-CPCA), to investigate how distributed regions coordinate to subserve different processes. As hypothesized, switching was subserved by a limbic network including the ventral striatum, thalamus, and parahippocampal gyrus, in conjunction with cortical salience network regions including the anterior cingulate and frontoinsular cortex. Activity in the ventral striatum was associated with switching regardless of how switching was cued; visually cued shifts were associated with additional visual cortical activity. After switching, as subjects moved into the hypothesis testing phase, a broad fronto-parietal-striatal network (associated with the cognitive control, dorsal attention, and salience networks) increased in activity. This network was sensitive to rule learning speed, with greater extended activity for the slowest learning speed late in the time course of learning. As subjects shifted from hypothesis testing to rule application, activity in this network decreased and activity in the somatomotor and default mode networks increased. PMID:26197092

Neural networks supporting switching, hypothesis testing, and rule application.

PubMed

Liu, Zhiya; Braunlich, Kurt; Wehe, Hillary S; Seger, Carol A

2015-10-01

We identified dynamic changes in recruitment of neural connectivity networks across three phases of a flexible rule learning and set-shifting task similar to the Wisconsin Card Sort Task: switching, rule learning via hypothesis testing, and rule application. During fMRI scanning, subjects viewed pairs of stimuli that differed across four dimensions (letter, color, size, screen location), chose one stimulus, and received feedback. Subjects were informed that the correct choice was determined by a simple unidimensional rule, for example "choose the blue letter". Once each rule had been learned and correctly applied for 4-7 trials, subjects were cued via either negative feedback or visual cues to switch to learning a new rule. Task performance was divided into three phases: Switching (first trial after receiving the switch cue), hypothesis testing (subsequent trials through the last error trial), and rule application (correct responding after the rule was learned). We used both univariate analysis to characterize activity occurring within specific regions of the brain, and a multivariate method, constrained principal component analysis for fMRI (fMRI-CPCA), to investigate how distributed regions coordinate to subserve different processes. As hypothesized, switching was subserved by a limbic network including the ventral striatum, thalamus, and parahippocampal gyrus, in conjunction with cortical salience network regions including the anterior cingulate and frontoinsular cortex. Activity in the ventral striatum was associated with switching regardless of how switching was cued; visually cued shifts were associated with additional visual cortical activity. After switching, as subjects moved into the hypothesis testing phase, a broad fronto-parietal-striatal network (associated with the cognitive control, dorsal attention, and salience networks) increased in activity. This network was sensitive to rule learning speed, with greater extended activity for the slowest learning speed late in the time course of learning. As subjects shifted from hypothesis testing to rule application, activity in this network decreased and activity in the somatomotor and default mode networks increased. Copyright © 2015 Elsevier Ltd. All rights reserved.

A quantitative meta-analysis and review of motor learning in the human brain

PubMed Central

Hardwick, Robert M.; Rottschy, Claudia; Miall, R. Chris; Eickhoff, Simon B.

2013-01-01

Neuroimaging studies have improved our understanding of which brain structures are involved in motor learning. Despite this, questions remain regarding the areas that contribute consistently across paradigms with different task demands. For instance, sensorimotor tasks focus on learning novel movement kinematics and dynamics, while serial response time task (SRTT) variants focus on sequence learning. These differing task demands are likely to elicit quantifiably different patterns of neural activity on top of a potentially consistent core network. The current study identified consistent activations across 70 motor learning experiments using activation likelihood estimation (ALE) meta-analysis. A global analysis of all tasks revealed a bilateral cortical–subcortical network consistently underlying motor learning across tasks. Converging activations were revealed in the dorsal premotor cortex, supplementary motor cortex, primary motor cortex, primary somatosensory cortex, superior parietal lobule, thalamus, putamen and cerebellum. These activations were broadly consistent across task specific analyses that separated sensorimotor tasks and SRTT variants. Contrast analysis indicated that activity in the basal ganglia and cerebellum was significantly stronger for sensorimotor tasks, while activity in cortical structures and the thalamus was significantly stronger for SRTT variants. Additional conjunction analyses then indicated that the left dorsal premotor cortex was activated across all analyses considered, even when controlling for potential motor confounds. The highly consistent activation of the left dorsal premotor cortex suggests it is a critical node in the motor learning network. PMID:23194819

YANA – a software tool for analyzing flux modes, gene-expression and enzyme activities

PubMed Central

Schwarz, Roland; Musch, Patrick; von Kamp, Axel; Engels, Bernd; Schirmer, Heiner; Schuster, Stefan; Dandekar, Thomas

2005-01-01

Background A number of algorithms for steady state analysis of metabolic networks have been developed over the years. Of these, Elementary Mode Analysis (EMA) has proven especially useful. Despite its low user-friendliness, METATOOL as a reliable high-performance implementation of the algorithm has been the instrument of choice up to now. As reported here, the analysis of metabolic networks has been improved by an editor and analyzer of metabolic flux modes. Analysis routines for expression levels and the most central, well connected metabolites and their metabolic connections are of particular interest. Results YANA features a platform-independent, dedicated toolbox for metabolic networks with a graphical user interface to calculate (integrating METATOOL), edit (including support for the SBML format), visualize, centralize, and compare elementary flux modes. Further, YANA calculates expected flux distributions for a given Elementary Mode (EM) activity pattern and vice versa. Moreover, a dissection algorithm, a centralization algorithm, and an average diameter routine can be used to simplify and analyze complex networks. Proteomics or gene expression data give a rough indication of some individual enzyme activities, whereas the complete flux distribution in the network is often not known. As such data are noisy, YANA features a fast evolutionary algorithm (EA) for the prediction of EM activities with minimum error, including alerts for inconsistent experimental data. We offer the possibility to include further known constraints (e.g. growth constraints) in the EA calculation process. The redox metabolism around glutathione reductase serves as an illustration example. All software and documentation are available for download at . Conclusion A graphical toolbox and an editor for METATOOL as well as a series of additional routines for metabolic network analyses constitute a new user-friendly software for such efforts. PMID:15929789

Altered Synchronizations among Neural Networks in Geriatric Depression

PubMed Central

Wang, Lihong; Chou, Ying-Hui; Potter, Guy G.; Steffens, David C.

2015-01-01

Although major depression has been considered as a manifestation of discoordinated activity between affective and cognitive neural networks, only a few studies have examined the relationships among neural networks directly. Because of the known disconnection theory, geriatric depression could be a useful model in studying the interactions among different networks. In the present study, using independent component analysis to identify intrinsically connected neural networks, we investigated the alterations in synchronizations among neural networks in geriatric depression to better understand the underlying neural mechanisms. Resting-state fMRI data was collected from thirty-two patients with geriatric depression and thirty-two age-matched never-depressed controls. We compared the resting-state activities between the two groups in the default-mode, central executive, attention, salience, and affective networks as well as correlations among these networks. The depression group showed stronger activity than the controls in an affective network, specifically within the orbitofrontal region. However, unlike the never-depressed controls, geriatric depression group lacked synchronized/antisynchronized activity between the affective network and the other networks. Those depressed patients with lower executive function has greater synchronization between the salience network with the executive and affective networks. Our results demonstrate the effectiveness of the between-network analyses in examining neural models for geriatric depression. PMID:26180795

Altered Synchronizations among Neural Networks in Geriatric Depression.

PubMed

Wang, Lihong; Chou, Ying-Hui; Potter, Guy G; Steffens, David C

2015-01-01

Although major depression has been considered as a manifestation of discoordinated activity between affective and cognitive neural networks, only a few studies have examined the relationships among neural networks directly. Because of the known disconnection theory, geriatric depression could be a useful model in studying the interactions among different networks. In the present study, using independent component analysis to identify intrinsically connected neural networks, we investigated the alterations in synchronizations among neural networks in geriatric depression to better understand the underlying neural mechanisms. Resting-state fMRI data was collected from thirty-two patients with geriatric depression and thirty-two age-matched never-depressed controls. We compared the resting-state activities between the two groups in the default-mode, central executive, attention, salience, and affective networks as well as correlations among these networks. The depression group showed stronger activity than the controls in an affective network, specifically within the orbitofrontal region. However, unlike the never-depressed controls, geriatric depression group lacked synchronized/antisynchronized activity between the affective network and the other networks. Those depressed patients with lower executive function has greater synchronization between the salience network with the executive and affective networks. Our results demonstrate the effectiveness of the between-network analyses in examining neural models for geriatric depression.

Social Media and Networking Technologies: An Analysis of Collaborative Work and Team Communication

ERIC Educational Resources Information Center

Okoro, Ephraim A.; Hausman, Angela; Washington, Melvin C.

2012-01-01

Digital communication increases students' learning outcomes in higher education. Web 2.0 technologies encourages students' active engagement, collaboration, and participation in class activities, facilitates group work, and encourages information sharing among students. Familiarity with organizational use and sharing in social networks aids…

Identification of interactive gene networks: a novel approach in gene array profiling of myometrial events during guinea pig pregnancy.

PubMed

Mason, Clifford W; Swaan, Peter W; Weiner, Carl P

2006-06-01

The transition from myometrial quiescence to activation is poorly understood, and the analysis of array data is limited by the available data mining tools. We applied functional analysis and logical operations along regulatory gene networks to identify molecular processes and pathways underlying quiescence and activation. We analyzed some 18,400 transcripts and variants in guinea pig myometrium at stages corresponding to quiescence and activation, and compared them to the nonpregnant (control) counterpart using a functional mapping tool, MetaCore (GeneGo, St Joseph, MI) to identify novel gene networks composed of biological pathways during mid (MP) and late (LP) pregnancy. Genes altered during quiescence and or activation were identified following gene specific comparisons with myometrium from nonpregnant animals, and then linked to curated pathways and formulated networks. The MP and LP networks were subtracted from each other to identify unique genomic events during those periods. For example, changes 2-fold or greater in genes mediating protein biosynthesis, programmed cell death, microtubule polymerization, and microtubule based movement were noted during the transition to LP. We describe a novel approach combining microarrays and genetic data to identify networks associated with normal myometrial events. The resulting insights help identify potential biomarkers and permit future targeted investigations of these pathways or networks to confirm or refute their importance.

Affective mentalizing and brain activity at rest in the behavioral variant of frontotemporal dementia.

PubMed

Caminiti, Silvia P; Canessa, Nicola; Cerami, Chiara; Dodich, Alessandra; Crespi, Chiara; Iannaccone, Sandro; Marcone, Alessandra; Falini, Andrea; Cappa, Stefano F

2015-01-01

bvFTD patients display an impairment in the attribution of cognitive and affective states to others, reflecting GM atrophy in brain regions associated with social cognition, such as amygdala, superior temporal cortex and posterior insula. Distinctive patterns of abnormal brain functioning at rest have been reported in bvFTD, but their relationship with defective attribution of affective states has not been investigated. To investigate the relationship among resting-state brain activity, gray matter (GM) atrophy and the attribution of mental states in the behavioral variant of fronto-temporal degeneration (bvFTD). We compared 12 bvFTD patients with 30 age- and education-matched healthy controls on a) performance in a task requiring the attribution of affective vs. cognitive mental states; b) metrics of resting-state activity in known functional networks; and c) the relationship between task-performances and resting-state metrics. In addition, we assessed a connection between abnormal resting-state metrics and GM atrophy. Compared with controls, bvFTD patients showed a reduction of intra-network coherent activity in several components, as well as decreased strength of activation in networks related to attentional processing. Anomalous resting-state activity involved networks which also displayed a significant reduction of GM density. In patients, compared with controls, higher affective mentalizing performance correlated with stronger functional connectivity between medial prefrontal sectors of the default-mode and attentional/performance monitoring networks, as well as with increased coherent activity in components of the executive, sensorimotor and fronto-limbic networks. Some of the observed effects may reflect specific compensatory mechanisms for the atrophic changes involving regions in charge of affective mentalizing. The analysis of specific resting-state networks thus highlights an intermediate level of analysis between abnormal brain structure and impaired behavioral performance in bvFTD, reflecting both dysfunction and compensation mechanisms.

Modeling shifts in the rate and pattern of subthalamopallidal network activity during deep brain stimulation.

PubMed

Hahn, Philip J; McIntyre, Cameron C

2010-06-01

Deep brain stimulation (DBS) of the subthlamic nucleus (STN) represents an effective treatment for medically refractory Parkinson's disease; however, understanding of its effects on basal ganglia network activity remains limited. We constructed a computational model of the subthalamopallidal network, trained it to fit in vivo recordings from parkinsonian monkeys, and evaluated its response to STN DBS. The network model was created with synaptically connected single compartment biophysical models of STN and pallidal neurons, and stochastically defined inputs driven by cortical beta rhythms. A least mean square error training algorithm was developed to parameterize network connections and minimize error when compared to experimental spike and burst rates in the parkinsonian condition. The output of the trained network was then compared to experimental data not used in the training process. We found that reducing the influence of the cortical beta input on the model generated activity that agreed well with recordings from normal monkeys. Further, during STN DBS in the parkinsonian condition the simulations reproduced the reduction in GPi bursting found in existing experimental data. The model also provided the opportunity to greatly expand analysis of GPi bursting activity, generating three major predictions. First, its reduction was proportional to the volume of STN activated by DBS. Second, GPi bursting decreased in a stimulation frequency dependent manner, saturating at values consistent with clinically therapeutic DBS. And third, ablating STN neurons, reported to generate similar therapeutic outcomes as STN DBS, also reduced GPi bursting. Our theoretical analysis of stimulation induced network activity suggests that regularization of GPi firing is dependent on the volume of STN tissue activated and a threshold level of burst reduction may be necessary for therapeutic effect.

Neighborhoods and Adolescent Health-Risk Behavior: An Ecological Network Approach1

PubMed Central

Browning, Christopher R.; Soller, Brian; Jackson, Aubrey L.

2014-01-01

This study integrates insights from social network analysis, activity space perspectives, and theories of urban and spatial processes to present an innovative approach to neighborhood effects on health-risk behavior among youth. We suggest spatial patterns of neighborhood residents’ non-home routine activities may be conceptualized as ecological, or “eco”-networks, which are two-mode networks that indirectly link residents through socio-spatial overlap in routine activities. We further argue structural configurations of eco-networks are consequential for youth’s behavioral health. In this study we focus on a key structural feature of eco-networks—the neighborhood-level extent to which households share two or more activity locations, or eco-network reinforcement—and its association with two dimensions of health-risk behavior, substance use and delinquency/sexual activity. Using geographic data on non-home routine activity locations among respondents from the Los Angeles Family and Neighborhood Survey (L.A.FANS), we constructed neighborhood-specific eco-networks by connecting sampled households to “activity clusters,” which are sets of spatially-proximate activity locations. We then measured eco-network reinforcement and examined its association with adolescent dimensions of health risk behavior employing a sample of 830 youth ages 12-17 nested in 65 census tracts. We also examined whether neighborhood-level social processes (collective efficacy and intergenerational closure) mediate the association between eco-network reinforcement and the outcomes considered. Results indicated eco-network reinforcement exhibits robust negative associations with both substance use and delinquency/sexual activity scales. Eco-network reinforcement effects were not explained by potential mediating variables. In addition to introducing a novel theoretical and empirical approach to neighborhood effects on youth, our findings highlight the importance of eco-network reinforcement for adolescent behavioral health. PMID:25011958

A statistical method for measuring activation of gene regulatory networks.

PubMed

Esteves, Gustavo H; Reis, Luiz F L

2018-06-13

Gene expression data analysis is of great importance for modern molecular biology, given our ability to measure the expression profiles of thousands of genes and enabling studies rooted in systems biology. In this work, we propose a simple statistical model for the activation measuring of gene regulatory networks, instead of the traditional gene co-expression networks. We present the mathematical construction of a statistical procedure for testing hypothesis regarding gene regulatory network activation. The real probability distribution for the test statistic is evaluated by a permutation based study. To illustrate the functionality of the proposed methodology, we also present a simple example based on a small hypothetical network and the activation measuring of two KEGG networks, both based on gene expression data collected from gastric and esophageal samples. The two KEGG networks were also analyzed for a public database, available through NCBI-GEO, presented as Supplementary Material. This method was implemented in an R package that is available at the BioConductor project website under the name maigesPack.

Changes in the interaction of resting-state neural networks from adolescence to adulthood.

PubMed

Stevens, Michael C; Pearlson, Godfrey D; Calhoun, Vince D

2009-08-01

This study examined how the mutual interactions of functionally integrated neural networks during resting-state fMRI differed between adolescence and adulthood. Independent component analysis (ICA) was used to identify functionally connected neural networks in 100 healthy participants aged 12-30 years. Hemodynamic timecourses that represented integrated neural network activity were analyzed with tools that quantified system "causal density" estimates, which indexed the proportion of significant Granger causality relationships among system nodes. Mutual influences among networks decreased with age, likely reflecting stronger within-network connectivity and more efficient between-network influences with greater development. Supplemental tests showed that this normative age-related reduction in causal density was accompanied by fewer significant connections to and from each network, regional increases in the strength of functional integration within networks, and age-related reductions in the strength of numerous specific system interactions. The latter included paths between lateral prefrontal-parietal circuits and "default mode" networks. These results contribute to an emerging understanding that activity in widely distributed networks thought to underlie complex cognition influences activity in other networks. (c) 2009 Wiley-Liss, Inc.

The optimization of force inputs for active structural acoustic control using a neural network

NASA Technical Reports Server (NTRS)

Cabell, R. H.; Lester, H. C.; Silcox, R. J.

1992-01-01

This paper investigates the use of a neural network to determine which force actuators, of a multi-actuator array, are best activated in order to achieve structural-acoustic control. The concept is demonstrated using a cylinder/cavity model on which the control forces, produced by piezoelectric actuators, are applied with the objective of reducing the interior noise. A two-layer neural network is employed and the back propagation solution is compared with the results calculated by a conventional, least-squares optimization analysis. The ability of the neural network to accurately and efficiently control actuator activation for interior noise reduction is demonstrated.

International Assistance for Low-Emission Development Planning: Coordinated Low Emissions Assistance Network (CLEAN) Inventory of Activities and Tools--Preliminary Trends

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cox, S.; Benioff, R.

2011-05-01

The Coordinated Low Emissions Assistance Network (CLEAN) is a voluntary network of international practitioners supporting low-emission planning in developing countries. The network seeks to improve quality of support through sharing project information, tools, best practices and lessons, and by fostering harmonized assistance. CLEAN has developed an inventory to track and analyze international technical support and tools for low-carbon planning activities in developing countries. This paper presents a preliminary analysis of the inventory to help identify trends in assistance activities and tools available to support developing countries with low-emission planning.

Bimanual Motor Coordination in Older Adults Is Associated with Increased Functional Brain Connectivity – A Graph-Theoretical Analysis

PubMed Central

Heitger, Marcus H.; Goble, Daniel J.; Dhollander, Thijs; Dupont, Patrick; Caeyenberghs, Karen; Leemans, Alexander; Sunaert, Stefan; Swinnen, Stephan P.

2013-01-01

In bimanual coordination, older and younger adults activate a common cerebral network but the elderly also have additional activation in a secondary network of brain areas to master task performance. It remains unclear whether the functional connectivity within these primary and secondary motor networks differs between the old and the young and whether task difficulty modulates connectivity. We applied graph-theoretical network analysis (GTNA) to task-driven fMRI data in 16 elderly and 16 young participants using a bimanual coordination task including in-phase and anti-phase flexion/extension wrist movements. Network nodes for the GTNA comprised task-relevant brain areas as defined by fMRI activation foci. The elderly matched the motor performance of the young but showed an increased functional connectivity in both networks across a wide range of connectivity metrics, i.e., higher mean connectivity degree, connection strength, network density and efficiency, together with shorter mean communication path length between the network nodes and also a lower betweenness centrality. More difficult movements showed an increased connectivity in both groups. The network connectivity of both groups had “small world” character. The present findings indicate (a) that bimanual coordination in the aging brain is associated with a higher functional connectivity even between areas also activated in young adults, independently from task difficulty, and (b) that adequate motor coordination in the context of task-driven bimanual control in older adults may not be solely due to additional neural recruitment but also to aging-related changes of functional relationships between brain regions. PMID:23637982

Advanced Fault Diagnosis Methods in Molecular Networks

PubMed Central

Habibi, Iman; Emamian, Effat S.; Abdi, Ali

2014-01-01

Analysis of the failure of cell signaling networks is an important topic in systems biology and has applications in target discovery and drug development. In this paper, some advanced methods for fault diagnosis in signaling networks are developed and then applied to a caspase network and an SHP2 network. The goal is to understand how, and to what extent, the dysfunction of molecules in a network contributes to the failure of the entire network. Network dysfunction (failure) is defined as failure to produce the expected outputs in response to the input signals. Vulnerability level of a molecule is defined as the probability of the network failure, when the molecule is dysfunctional. In this study, a method to calculate the vulnerability level of single molecules for different combinations of input signals is developed. Furthermore, a more complex yet biologically meaningful method for calculating the multi-fault vulnerability levels is suggested, in which two or more molecules are simultaneously dysfunctional. Finally, a method is developed for fault diagnosis of networks based on a ternary logic model, which considers three activity levels for a molecule instead of the previously published binary logic model, and provides equations for the vulnerabilities of molecules in a ternary framework. Multi-fault analysis shows that the pairs of molecules with high vulnerability typically include a highly vulnerable molecule identified by the single fault analysis. The ternary fault analysis for the caspase network shows that predictions obtained using the more complex ternary model are about the same as the predictions of the simpler binary approach. This study suggests that by increasing the number of activity levels the complexity of the model grows; however, the predictive power of the ternary model does not appear to be increased proportionally. PMID:25290670

Complex Networks, Fractals and Topology Trends for Oxidative Activity of DNA in Cells for Populations of Fluorescing Neutrophils in Medical Diagnostics

NASA Astrophysics Data System (ADS)

Galich, N. E.

A novel nonlinear statistical method of immunofluorescence data analysis is presented. The data of DNA fluorescence due to oxidative activity in neutrophils nuclei of peripheral blood is analyzed. Histograms of photon counts statistics are generated using flow cytometry method. The histograms represent the distributions of fluorescence flash frequency as functions of intensity for large populations∼104-105 of fluorescing cells. We have shown that these experiments present 3D-correlations of oxidative activity of DNA for full chromosomes set in cells with spatial resolution of measurements is about few nanometers in the flow direction the jet of blood. Detailed analysis showed that large-scale correlations in oxidative activity of DNA in cells are described as networks of small- worlds (complex systems with logarithmic scaling) with self own small-world networks for given donor at given time for all states of health. We observed changes in fractal networks of oxidative activity of DNA in neutrophils in vivo and during medical treatments for classification and diagnostics of pathologies for wide spectra of diseases. Our approach based on analysis of changes topology of networks (fractal dimension) at variation the scales of networks. We produce the general estimation of health status of a given donor in a form of yes/no of answers (healthy/sick) in the dependence on the sign of plus/minus in the trends change of fractal dimensions due to decreasing the scale of nets. We had noted the increasing biodiversity of neutrophils and stochastic (Brownian) character of intercellular correlations of different neutrophils in the blood of healthy donor. In the blood of sick people we observed the deterministic cell-cell correlations of neutrophils and decreasing their biodiversity.

PhenomeExpress: a refined network analysis of expression datasets by inclusion of known disease phenotypes.

PubMed

Soul, Jamie; Hardingham, Timothy E; Boot-Handford, Raymond P; Schwartz, Jean-Marc

2015-01-29

We describe a new method, PhenomeExpress, for the analysis of transcriptomic datasets to identify pathogenic disease mechanisms. Our analysis method includes input from both protein-protein interaction and phenotype similarity networks. This introduces valuable information from disease relevant phenotypes, which aids the identification of sub-networks that are significantly enriched in differentially expressed genes and are related to the disease relevant phenotypes. This contrasts with many active sub-network detection methods, which rely solely on protein-protein interaction networks derived from compounded data of many unrelated biological conditions and which are therefore not specific to the context of the experiment. PhenomeExpress thus exploits readily available animal model and human disease phenotype information. It combines this prior evidence of disease phenotypes with the experimentally derived disease data sets to provide a more targeted analysis. Two case studies, in subchondral bone in osteoarthritis and in Pax5 in acute lymphoblastic leukaemia, demonstrate that PhenomeExpress identifies core disease pathways in both mouse and human disease expression datasets derived from different technologies. We also validate the approach by comparison to state-of-the-art active sub-network detection methods, which reveals how it may enhance the detection of molecular phenotypes and provide a more detailed context to those previously identified as possible candidates.

Functional connectivity mapping of regions associated with self- and other-processing.

PubMed

Murray, Ryan J; Debbané, Martin; Fox, Peter T; Bzdok, Danilo; Eickhoff, Simon B

2015-04-01

Neuroscience literature increasingly suggests a conceptual self composed of interacting neural regions, rather than independent local activations, yet such claims have yet to be investigated. We, thus, combined task-dependent meta-analytic connectivity modeling (MACM) with task-independent resting-state (RS) connectivity analysis to delineate the neural network of the self, across both states. Given psychological evidence implicating the self's interdependence on social information, we also delineated the neural network underlying conceptual other-processing. To elucidate the relation between the self-/other-networks and their function, we mined the MACM metadata to generate a cognitive-behavioral profile for an empirically identified region specific to conceptual self, the pregenual anterior cingulate (pACC), and conceptual other, posterior cingulate/precuneus (PCC/PC). Mining of 7,200 published, task-dependent, neuroimaging studies, using healthy human subjects, yielded 193 studies activating the self-related seed and were conjoined with RS connectivity analysis to delineate a differentiated self-network composed of the pACC (seed) and anterior insula, relative to other functional connectivity. Additionally, 106 studies activating the other-related seed were conjoined with RS connectivity analysis to delineate a differentiated other-network of PCC/PC (seed) and angular gyrus/temporoparietal junction, relative to self-functional connectivity. The self-network seed related to emotional conflict resolution and motivational processing, whereas the other-network seed related to socially oriented processing and contextual information integration. Notably, our findings revealed shared RS connectivity between ensuing self-/other-networks within the ventromedial prefrontal cortex and medial orbitofrontal cortex, suggesting self-updating via integration of self-relevant social information. We, therefore, present initial neurobiological evidence corroborating the increasing claims of an intricate self-network, the architecture of which may promote social value processing. © 2014 Wiley Periodicals, Inc.

Dynamic Neural Networks Supporting Memory Retrieval

PubMed Central

St. Jacques, Peggy L.; Kragel, Philip A.; Rubin, David C.

2011-01-01

How do separate neural networks interact to support complex cognitive processes such as remembrance of the personal past? Autobiographical memory (AM) retrieval recruits a consistent pattern of activation that potentially comprises multiple neural networks. However, it is unclear how such large-scale neural networks interact and are modulated by properties of the memory retrieval process. In the present functional MRI (fMRI) study, we combined independent component analysis (ICA) and dynamic causal modeling (DCM) to understand the neural networks supporting AM retrieval. ICA revealed four task-related components consistent with the previous literature: 1) Medial Prefrontal Cortex (PFC) Network, associated with self-referential processes, 2) Medial Temporal Lobe (MTL) Network, associated with memory, 3) Frontoparietal Network, associated with strategic search, and 4) Cingulooperculum Network, associated with goal maintenance. DCM analysis revealed that the medial PFC network drove activation within the system, consistent with the importance of this network to AM retrieval. Additionally, memory accessibility and recollection uniquely altered connectivity between these neural networks. Recollection modulated the influence of the medial PFC on the MTL network during elaboration, suggesting that greater connectivity among subsystems of the default network supports greater re-experience. In contrast, memory accessibility modulated the influence of frontoparietal and MTL networks on the medial PFC network, suggesting that ease of retrieval involves greater fluency among the multiple networks contributing to AM. These results show the integration between neural networks supporting AM retrieval and the modulation of network connectivity by behavior. PMID:21550407

System data communication structures for active-control transport aircraft, volume 1

NASA Technical Reports Server (NTRS)

Hopkins, A. L.; Martin, J. H.; Brock, L. D.; Jansson, D. G.; Serben, S.; Smith, T. B.; Hanley, L. D.

1981-01-01

Candidate data communication techniques are identified, including dedicated links, local buses, broadcast buses, multiplex buses, and mesh networks. The design methodology for mesh networks is then discussed, including network topology and node architecture. Several concepts of power distribution are reviewed, including current limiting and mesh networks for power. The technology issues of packaging, transmission media, and lightning are addressed, and, finally, the analysis tools developed to aid in the communication design process are described. There are special tools to analyze the reliability and connectivity of networks and more general reliability analysis tools for all types of systems.

An fMRI investigation of the relationship between future imagination and cognitive flexibility

PubMed Central

Roberts, R.P.; Wiebels, K.; Sumner, R.L.; van Mulukom, V.; Grady, C.L.; Schacter, D.L.; Addis, D.R.

2016-01-01

While future imagination is largely considered to be a cognitive process grounded in default mode network activity, studies have shown that future imagination recruits regions in both default mode and frontoparietal control networks. In addition, it has recently been shown that the ability to imagine the future is associated with cognitive flexibility, and that tasks requiring cognitive flexibility result in increased coupling of the default mode network with frontoparietal control and salience networks. In the current study, we investigated the neural correlates underlying the association between cognitive flexibility and future imagination in two ways. First, we experimentally varied the degree of cognitive flexibility required during future imagination by manipulating the disparateness of episodic details contributing to imagined events. To this end, participants generated episodic details (persons, locations, objects) within three social spheres; during fMRI scanning they were presented with sets of three episodic details all taken from the same social sphere (Congruent condition) or different social spheres (Incongruent condition) and required to imagine a future event involving the three details. We predicted that, relative to the Congruent condition, future simulation in the Incongruent condition would be associated with increased activity in regions of the default mode, frontoparietal and salience networks. Second, we hypothesized that individual differences in cognitive flexibility, as measured by performance on the Alternate Uses Task, would correspond to individual differences in the brain regions recruited during future imagination. A task partial least squares (PLS) analysis showed that the Incongruent condition resulted in an increase in activity in regions in salience networks (e.g. the insula) but, contrary to our prediction, reduced activity in many regions of the default mode network (including the hippocampus). A subsequent functional connectivity (within-subject seed PLS) analysis showed that the insula exhibited increased coupling with default mode regions during the Incongruent condition. Finally, a behavioral PLS analysis showed that individual differences in cognitive flexibility were associated with differences in activity in a number of regions from frontoparietal, salience and default-mode networks during both future imagination conditions, further highlighting that the cognitive flexibility underlying future imagination is grounded in the complex interaction of regions in these networks. PMID:27908591

How to Identify Success Among Networks That Promote Active Living.

PubMed

Litt, Jill; Varda, Danielle; Reed, Hannah; Retrum, Jessica; Tabak, Rachel; Gustat, Jeanette; O'Hara Tompkins, Nancy

2015-11-01

We evaluated organization- and network-level factors that influence organizations' perceived success. This is important for managing interorganizational networks, which can mobilize communities to address complex health issues such as physical activity, and for achieving change. In 2011, we used structured interview and network survey data from 22 states in the United States to estimate multilevel random-intercept models to understand organization- and network-level factors that explain perceived network success. A total of 53 of 59 "whole networks" met the criteria for inclusion in the analysis (89.8%). Coordinators identified 559 organizations, with 3 to 12 organizations from each network taking the online survey (response rate = 69.7%; range = 33%-100%). Occupying a leadership position (P < .01), the amount of time with the network (P < .05), and support from community leaders (P < .05) emerged as correlates of perceived success. Organizations' perceptions of success can influence decisions about continuing involvement and investment in networks designed to promote environment and policy change for active living. Understanding these factors can help leaders manage complex networks that involve diverse memberships, varied interests, and competing community-level priorities.

3D Filament Network Segmentation with Multiple Active Contours

NASA Astrophysics Data System (ADS)

Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei

2014-03-01

Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and microtubules. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we developed a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D TIRF Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy.

Identifying and tracking dynamic processes in social networks

NASA Astrophysics Data System (ADS)

Chung, Wayne; Savell, Robert; Schütt, Jan-Peter; Cybenko, George

2006-05-01

The detection and tracking of embedded malicious subnets in an active social network can be computationally daunting due to the quantity of transactional data generated in the natural interaction of large numbers of actors comprising a network. In addition, detection of illicit behavior may be further complicated by evasive strategies designed to camouflage the activities of the covert subnet. In this work, we move beyond traditional static methods of social network analysis to develop a set of dynamic process models which encode various modes of behavior in active social networks. These models will serve as the basis for a new application of the Process Query System (PQS) to the identification and tracking of covert dynamic processes in social networks. We present a preliminary result from application of our technique in a real-world data stream-- the Enron email corpus.

Mapping one strong 'Ohana: using network analysis and GIS to enhance the effectiveness of a statewide coalition to prevent child abuse and neglect.

PubMed

Cardazone, Gina; U Sy, Angela; Chik, Ivan; Corlew, Laura Kate

2014-06-01

Network analysis and GIS enable the presentation of meaningful data about organizational relationships and community characteristics, respectively. Together, these tools can provide a concrete representation of the ecological context in which coalitions operate, and may help coalitions identify opportunities for growth and enhanced effectiveness. This study uses network analysis and GIS mapping as part of an evaluation of the One Strong 'Ohana (OSO) campaign. The OSO campaign was launched in 2012 via a partnership between the Hawai'i Children's Trust Fund (HCTF) and the Joyful Heart Foundation. The OSO campaign uses a collaborative approach aimed at increasing public awareness of child maltreatment and protective factors that can prevent maltreatment, as well as enhancing the effectiveness of the HCTF Coalition. This study focuses on three elements of the OSO campaign evaluation: (1) Network analysis exploring the relationships between 24 active Coalition member organizations, (2) GIS mapping of responses to a randomized statewide phone survey (n = 1,450) assessing awareness of factors contributing to child maltreatment, and (3) Combined GIS maps and network data, illustrating opportunities for geographically-targeted coalition building and public awareness activities.

Information transmission and signal permutation in active flow networks

NASA Astrophysics Data System (ADS)

Woodhouse, Francis G.; Fawcett, Joanna B.; Dunkel, Jörn

2018-03-01

Recent experiments show that both natural and artificial microswimmers in narrow channel-like geometries will self-organise to form steady, directed flows. This suggests that networks of flowing active matter could function as novel autonomous microfluidic devices. However, little is known about how information propagates through these far-from-equilibrium systems. Through a mathematical analogy with spin-ice vertex models, we investigate here the input–output characteristics of generic incompressible active flow networks (AFNs). Our analysis shows that information transport through an AFN is inherently different from conventional pressure or voltage driven networks. Active flows on hexagonal arrays preserve input information over longer distances than their passive counterparts and are highly sensitive to bulk topological defects, whose presence can be inferred from marginal input–output distributions alone. This sensitivity further allows controlled permutations on parallel inputs, revealing an unexpected link between active matter and group theory that can guide new microfluidic mixing strategies facilitated by active matter and aid the design of generic autonomous information transport networks.

The neural basis of hand gesture comprehension: A meta-analysis of functional magnetic resonance imaging studies.

PubMed

Yang, Jie; Andric, Michael; Mathew, Mili M

2015-10-01

Gestures play an important role in face-to-face communication and have been increasingly studied via functional magnetic resonance imaging. Although a large amount of data has been provided to describe the neural substrates of gesture comprehension, these findings have never been quantitatively summarized and the conclusion is still unclear. This activation likelihood estimation meta-analysis investigated the brain networks underpinning gesture comprehension while considering the impact of gesture type (co-speech gestures vs. speech-independent gestures) and task demand (implicit vs. explicit) on the brain activation of gesture comprehension. The meta-analysis of 31 papers showed that as hand actions, gestures involve a perceptual-motor network important for action recognition. As meaningful symbols, gestures involve a semantic network for conceptual processing. Finally, during face-to-face interactions, gestures involve a network for social emotive processes. Our finding also indicated that gesture type and task demand influence the involvement of the brain networks during gesture comprehension. The results highlight the complexity of gesture comprehension, and suggest that future research is necessary to clarify the dynamic interactions among these networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using Social Network Analysis to Better Understand Compulsive Exercise Behavior Among a Sample of Sorority Members.

PubMed

Patterson, Megan S; Goodson, Patricia

2017-05-01

Compulsive exercise, a form of unhealthy exercise often associated with prioritizing exercise and feeling guilty when exercise is missed, is a common precursor to and symptom of eating disorders. College-aged women are at high risk of exercising compulsively compared with other groups. Social network analysis (SNA) is a theoretical perspective and methodology allowing researchers to observe the effects of relational dynamics on the behaviors of people. SNA was used to assess the relationship between compulsive exercise and body dissatisfaction, physical activity, and network variables. Descriptive statistics were conducted using SPSS, and quadratic assignment procedure (QAP) analyses were conducted using UCINET. QAP regression analysis revealed a statistically significant model (R 2 = .375, P < .0001) predicting compulsive exercise behavior. Physical activity, body dissatisfaction, and network variables were statistically significant predictor variables in the QAP regression model. In our sample, women who are connected to "important" or "powerful" people in their network are likely to have higher compulsive exercise scores. This result provides healthcare practitioners key target points for intervention within similar groups of women. For scholars researching eating disorders and associated behaviors, this study supports looking into group dynamics and network structure in conjunction with body dissatisfaction and exercise frequency.

Structural analysis of behavioral networks from the Internet

NASA Astrophysics Data System (ADS)

Meiss, M. R.; Menczer, F.; Vespignani, A.

2008-06-01

In spite of the Internet's phenomenal growth and social impact, many aspects of the collective communication behavior of its users are largely unknown. Understanding the structure and dynamics of the behavioral networks that connect users with each other and with services across the Internet is key to modeling the network and designing future applications. We present a characterization of the properties of the behavioral networks generated by several million users of the Abilene (Internet2) network. Structural features of these networks offer new insights into scaling properties of network activity and ways of distinguishing particular patterns of traffic. For example, we find that the structure of the behavioral network associated with Web activity is characterized by such extreme heterogeneity as to challenge any simple attempt to model Web server traffic.

Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

PubMed Central

Liu, Zhongming; de Zwart, Jacco A.; Chang, Catie; Duan, Qi; van Gelderen, Peter; Duyn, Jeff H.

2014-01-01

Spontaneous activity in the human brain occurs in complex spatiotemporal patterns that may reflect functionally specialized neural networks. Here, we propose a subspace analysis method to elucidate large-scale networks by the joint analysis of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data. The new approach is based on the notion that the neuroelectrical activity underlying the fMRI signal may have EEG spectral features that report on regional neuronal dynamics and interregional interactions. Applying this approach to resting healthy adults, we indeed found characteristic spectral signatures in the EEG correlates of spontaneous fMRI signals at individual brain regions as well as the temporal synchronization among widely distributed regions. These spectral signatures not only allowed us to parcel the brain into clusters that resembled the brain's established functional subdivision, but also offered important clues for disentangling the involvement of individual regions in fMRI network activity. PMID:23796947

Geo-Spatial Social Network Analysis of Social Media to Mitigate Disasters

NASA Astrophysics Data System (ADS)

Carley, K. M.

2017-12-01

Understanding the spatial layout of human activity can afford a better understanding many phenomena - such as local cultural, the spread of ideas, and the scope of a disaster. Today, social media is one of the key sensors for acquiring information on socio-cultural activity, some with cues as to the geo-location. We ask, What can be learned by putting such data on maps? For example, are people who chat on line more likely to be near each other? Can Twitter data support disaster planning or early warning? In this talk, such issues are examined using data collected via Twitter and analyzed using ORA. ORA is a network analysis and visualization system. It supports not just social networks (who is interacting with whom), but also high dimensional networks with many types of nodes (e.g. people, organizations, resources, activities …) and relations, geo-spatial network analysis, dynamic network analysis, & geo-temporal analysis. Using ORA lessons learned from five case studies are considered: Arab Spring, Tsunami warning in Padang Indonesia, Twitter around Fukushima in Japan, Typhoon Haiyan (Yolanda), & regional conflict. Using Padang Indonesia data, we characterize the strengths and limitations of social media data to support disaster planning & early warning, identify at risk areas & issues of concern, and estimate where people are and which areas are impacted. Using Fukushima Japanese data, social media is used to estimate geo-spatial regularities in movement and communication that can inform disaster response and risk estimation. Using Arab Spring data, we find that the spread of bots & extremists varies by country and time, to the extent that using twitter to understand who is important or what ideas are critical can be compromised. Bots and extremists can exploit disaster messaging to create havoc and facilitate criminal activity e.g. human trafficking. Event discovery mechanisms support isolating geo-epi-centers for key events become crucial. Spatial inference enables improved country, and city identification. Geo-network analytics with and without these inferences reveal that explicitly geo-tagged data may not be representative and that improved location estimation provides better insight into the social condition. These results demonstrate the value of these technique to mitigate the social impact of disasters.

Quantitative Analysis of Signaling Networks across Differentially Embedded Tumors Highlights Interpatient Heterogeneity in Human Glioblastoma

PubMed Central

2015-01-01

Glioblastoma multiforme (GBM) is the most aggressive malignant primary brain tumor, with a dismal mean survival even with the current standard of care. Although in vitro cell systems can provide mechanistic insight into the regulatory networks governing GBM cell proliferation and migration, clinical samples provide a more physiologically relevant view of oncogenic signaling networks. However, clinical samples are not widely available and may be embedded for histopathologic analysis. With the goal of accurately identifying activated signaling networks in GBM tumor samples, we investigated the impact of embedding in optimal cutting temperature (OCT) compound followed by flash freezing in LN2 vs immediate flash freezing (iFF) in LN2 on protein expression and phosphorylation-mediated signaling networks. Quantitative proteomic and phosphoproteomic analysis of 8 pairs of tumor specimens revealed minimal impact of the different sample processing strategies and highlighted the large interpatient heterogeneity present in these tumors. Correlation analyses of the differentially processed tumor sections identified activated signaling networks present in selected tumors and revealed the differential expression of transcription, translation, and degradation associated proteins. This study demonstrates the capability of quantitative mass spectrometry for identification of in vivo oncogenic signaling networks from human tumor specimens that were either OCT-embedded or immediately flash-frozen. PMID:24927040

Analysis and Visualization of Relations in eLearning

NASA Astrophysics Data System (ADS)

Dráždilová, Pavla; Obadi, Gamila; Slaninová, Kateřina; Martinovič, Jan; Snášel, Václav

The popularity of eLearning systems is growing rapidly; this growth is enabled by the consecutive development in Internet and multimedia technologies. Web-based education became wide spread in the past few years. Various types of learning management systems facilitate development of Web-based courses. Users of these courses form social networks through the different activities performed by them. This chapter focuses on searching the latent social networks in eLearning systems data. These data consist of students activity records wherein latent ties among actors are embedded. The social network studied in this chapter is represented by groups of students who have similar contacts and interact in similar social circles. Different methods of data clustering analysis can be applied to these groups, and the findings show the existence of latent ties among the group members. The second part of this chapter focuses on social network visualization. Graphical representation of social network can describe its structure very efficiently. It can enable social network analysts to determine the network degree of connectivity. Analysts can easily determine individuals with a small or large amount of relationships as well as the amount of independent groups in a given network. When applied to the field of eLearning, data visualization simplifies the process of monitoring the study activities of individuals or groups, as well as the planning of educational curriculum, the evaluation of study processes, etc.

Social Networks and Health: Understanding the Nuances of Healthcare Access between Urban and Rural Populations.

PubMed

Amoah, Padmore Adusei; Edusei, Joseph; Amuzu, David

2018-05-13

Communities and individuals in many sub-Saharan African countries often face limited access to healthcare. Hence, many rely on social networks to enhance their chances for adequate health care. While this knowledge is well-established, little is known about the nuances of how different population groups activate these networks to improve access to healthcare. This paper examines how rural and urban dwellers in the Ashanti Region in Ghana distinctively and systematically activate their social networks to enhance access to healthcare. It uses a qualitative cross-sectional design, with in-depth interviews of 79 primary participants (28 urban and 51 rural residents) in addition to the views of eight community leaders and eight health personnel. It was discovered that both intimate and distanced social networks for healthcare are activated at different periods by rural and urban residents. Four main stages of social networks activation, comprising different individuals and groups were observed among rural and urban dwellers. Among both groups, physical proximity, privacy, trust and sense of fairness, socio-cultural meaning attached to health problems, and perceived knowledge and other resources (mainly money) held in specific networks inherently influenced social network activation. The paper posits that a critical analysis of social networks may help to tailor policy contents to individuals and groups with limited access to healthcare.

Network meta-analysis, electrical networks and graph theory.

PubMed

Rücker, Gerta

2012-12-01

Network meta-analysis is an active field of research in clinical biostatistics. It aims to combine information from all randomized comparisons among a set of treatments for a given medical condition. We show how graph-theoretical methods can be applied to network meta-analysis. A meta-analytic graph consists of vertices (treatments) and edges (randomized comparisons). We illustrate the correspondence between meta-analytic networks and electrical networks, where variance corresponds to resistance, treatment effects to voltage, and weighted treatment effects to current flows. Based thereon, we then show that graph-theoretical methods that have been routinely applied to electrical networks also work well in network meta-analysis. In more detail, the resulting consistent treatment effects induced in the edges can be estimated via the Moore-Penrose pseudoinverse of the Laplacian matrix. Moreover, the variances of the treatment effects are estimated in analogy to electrical effective resistances. It is shown that this method, being computationally simple, leads to the usual fixed effect model estimate when applied to pairwise meta-analysis and is consistent with published results when applied to network meta-analysis examples from the literature. Moreover, problems of heterogeneity and inconsistency, random effects modeling and including multi-armed trials are addressed. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.

California Health Services/Educational Activities. Consortium Network.

ERIC Educational Resources Information Center

White, Charles H.

Profiles are presented of each of the 10 consortia that make up the California Health Services/Education Activities (HS/EA) network (new relationships between educational facilities where health care manpower is trained in the community settings where they practice). The first part of the booklet is a comparative analysis of (1) Area Health…

Evaluating the effect of aging on interference resolution with time-varying complex networks analysis

PubMed Central

Ariza, Pedro; Solesio-Jofre, Elena; Martínez, Johann H.; Pineda-Pardo, José A.; Niso, Guiomar; Maestú, Fernando; Buldú, Javier M.

2015-01-01

In this study we used graph theory analysis to investigate age-related reorganization of functional networks during the active maintenance of information that is interrupted by external interference. Additionally, we sought to investigate network differences before and after averaging network parameters between both maintenance and interference windows. We compared young and older adults by measuring their magnetoencephalographic recordings during an interference-based working memory task restricted to successful recognitions. Data analysis focused on the topology/temporal evolution of functional networks during both the maintenance and interference windows. We observed that: (a) Older adults require higher synchronization between cortical brain sites in order to achieve a successful recognition, (b) The main differences between age groups arise during the interference window, (c) Older adults show reduced ability to reorganize network topology when interference is introduced, and (d) Averaging network parameters leads to a loss of sensitivity to detect age differences. PMID:26029079

Time-resolved microrheology of actively remodeling actomyosin networks

NASA Astrophysics Data System (ADS)

Silva, Marina Soares e.; Stuhrmann, Björn; Betz, Timo; Koenderink, Gijsje H.

2014-07-01

Living cells constitute an extraordinary state of matter since they are inherently out of thermal equilibrium due to internal metabolic processes. Indeed, measurements of particle motion in the cytoplasm of animal cells have revealed clear signatures of nonthermal fluctuations superposed on passive thermal motion. However, it has been difficult to pinpoint the exact molecular origin of this activity. Here, we employ time-resolved microrheology based on particle tracking to measure nonequilibrium fluctuations produced by myosin motor proteins in a minimal model system composed of purified actin filaments and myosin motors. We show that the motors generate spatially heterogeneous contractile fluctuations, which become less frequent with time as a consequence of motor-driven network remodeling. We analyze the particle tracking data on different length scales, combining particle image velocimetry, an ensemble analysis of the particle trajectories, and finally a kymograph analysis of individual particle trajectories to quantify the length and time scales associated with active particle displacements. All analyses show clear signatures of nonequilibrium activity: the particles exhibit random motion with an enhanced amplitude compared to passive samples, and they exhibit sporadic contractile fluctuations with ballistic motion over large (up to 30 μm) distances. This nonequilibrium activity diminishes with sample age, even though the adenosine triphosphate level is held constant. We propose that network coarsening concentrates motors in large clusters and depletes them from the network, thus reducing the occurrence of contractile fluctuations. Our data provide valuable insight into the physical processes underlying stress generation within motor-driven actin networks and the analysis framework may prove useful for future microrheology studies in cells and model organisms.

Altered brain activation and functional connectivity in working memory related networks in patients with type 2 diabetes: An ICA-based analysis

PubMed Central

Zhang, Yang; Lu, Shan; Liu, Chunlei; Zhang, Huimei; Zhou, Xuanhe; Ni, Changlin; Qin, Wen; Zhang, Quan

2016-01-01

Type 2 diabetes mellitus (T2DM) can cause multidimensional cognitive deficits, among which working memory (WM) is usually involved at an early stage. However, the neural substrates underlying impaired WM in T2DM patients are still unclear. To clarify this issue, we utilized functional magnetic resonance imaging (fMRI) and independent component analysis to evaluate T2DM patients for alterations in brain activation and functional connectivity (FC) in WM networks and to determine their associations with cognitive and clinical variables. Twenty complication-free T2DM patients and 19 matched healthy controls (HCs) were enrolled, and fMRI data were acquired during a block-designed 1-back WM task. The WM metrics of the T2DM patients showed no differences compared with those of the HCs, except for a slightly lower accuracy rate in the T2DM patients. Compared with the HCs, the T2DM patients demonstrated increased activation within their WM fronto-parietal networks, and activation strength was significantly correlated with WM performance. The T2DM patients also showed decreased FC within and between their WM networks. Our results indicate that the functional integration of WM sub-networks was disrupted in the complication-free T2DM patients and that strengthened regional activity in fronto-parietal networks may compensate for the WM impairment caused by T2DM. PMID:27021340

Neural network analysis of electrodynamic activity of yeast cells around 1 kHz

NASA Astrophysics Data System (ADS)

Janca, R.

2011-12-01

This paper deals with data analysis of electrodynamic activity of two mutants of yeast cells, cell cycle of which is synchronized and non-synchronized, respectively. We used data already published by Jelinek et al. and treat them with data mining method based on the multilayer neural network. Intersection of data mining and statistical distribution of the noise shows significant difference between synchronized and non-synchronized yeasts not only in total power, but also discrete frequencies.

Periodicity and global exponential stability of generalized Cohen-Grossberg neural networks with discontinuous activations and mixed delays.

PubMed

Wang, Dongshu; Huang, Lihong

2014-03-01

In this paper, we investigate the periodic dynamical behaviors for a class of general Cohen-Grossberg neural networks with discontinuous right-hand sides, time-varying and distributed delays. By means of retarded differential inclusions theory and the fixed point theorem of multi-valued maps, the existence of periodic solutions for the neural networks is obtained. After that, we derive some sufficient conditions for the global exponential stability and convergence of the neural networks, in terms of nonsmooth analysis theory with generalized Lyapunov approach. Without assuming the boundedness (or the growth condition) and monotonicity of the discontinuous neuron activation functions, our results will also be valid. Moreover, our results extend previous works not only on discrete time-varying and distributed delayed neural networks with continuous or even Lipschitz continuous activations, but also on discrete time-varying and distributed delayed neural networks with discontinuous activations. We give some numerical examples to show the applicability and effectiveness of our main results. Copyright © 2013 Elsevier Ltd. All rights reserved.

A multilevel path analysis of contact frequency between social network members

NASA Astrophysics Data System (ADS)

van den Berg, Pauline; Arentze, Theo; Timmermans, Harry

2012-04-01

Recently, there has been an increasing interest in the role of social networks in spatial-choice and travel behavior. It has been acknowledged that social activities and the travel for these activities can emerge from individuals' social networks and that social activities are responsible for an important portion of travel demand. The influence of information and communication technologies (ICT's) is also important in this respect. The purpose of the paper is to examine the effects of characteristics of egos and ego-alter relationships on the frequency of social interaction by different communication modes, using multilevel path analysis. The analyses are based on social network data collected in 2008 in the Eindhoven region in the Netherlands among 116 respondents. The results indicate a complementary relationship between contact frequencies by different modes. The contact frequencies of the different modes, especially face-to-face and telephone, can also be largely explained by the ego's personal characteristics and the type of relationship and the distance between ego and alter.

The role of interpersonal communication in the process of knowledge mobilization within a community-based organization: a network analysis.

PubMed

Gainforth, Heather L; Latimer-Cheung, Amy E; Athanasopoulos, Peter; Moore, Spencer; Ginis, Kathleen A Martin

2014-05-22

Diffusion of innovations theory has been widely used to explain knowledge mobilization of research findings. This theory posits that individuals who are more interpersonally connected within an organization may be more likely to adopt an innovation (e.g., research evidence) than individuals who are less interconnected. Research examining this tenet of diffusion of innovations theory in the knowledge mobilization literature is limited. The purpose of the present study was to use network analysis to examine the role of interpersonal communication in the adoption and mobilization of the physical activity guidelines for people with spinal cord injury (SCI) among staff in a community-based organization (CBO). The study used a cross-sectional, whole-network design. In total, 56 staff completed the network survey. Adoption of the guidelines was assessed using Rogers' innovation-decision process and interpersonal communication was assessed using an online network instrument. The patterns of densities observed within the network were indicative of a core-periphery structure revealing that interpersonal communication was greater within the core than between the core and periphery and within the periphery. Membership in the core, as opposed to membership in the periphery, was associated with greater knowledge of the evidence-based physical activity resources available and engagement in physical activity promotion behaviours (ps < 0.05). Greater in-degree centrality was associated with adoption of evidence-based behaviours (p < 0.05). Findings suggest that interpersonal communication is associated with knowledge mobilization and highlight how the network structure could be improved for further dissemination efforts. diffusion of innovations; network analysis; community-based organization; knowledge mobilization; knowledge translation, interpersonal communication.

PTP-ε HAS A CRITICAL ROLE IN SIGNALING TRANSDUCTION PATHWAYS AND PHOSPHOPROTEIN NETWORK TOPOLOGY IN RED CELLS

PubMed Central

De Franceschi, Lucia; Biondani, Andrea; Carta, Franco; Turrini, Franco; Laudanna, Carlo; Deana, Renzo; Brunati, Anna Maria; Turretta, Loris; Iolascon, Achille; Perrotta, Silverio; Elson, Ari; Bulato, Cristina; Brugnara, Carlo

2010-01-01

Protein tyrosine phosphatases (PTPs) are crucial components of cellular signal transduction pathways. We report here that red blood cells (RBCs) from mice lacking PTPε (Ptpre−/−) exhibit abnormal morphology and increased Ca2+-activated-K+ channel activity, which was partially blocked by the Src-Family-Kinases (SFKs) inhibitor PP1. In Ptpre−/− mouse RBCs, the activity of Fyn and Yes, two SFKs, were increased, suggesting a functional relationship between SFKs, PTPε and Ca2+-activated-K+-channel. The absence of PTPε markedly affected the RBC membrane tyrosine (Tyr-) phosphoproteome, indicating a perturbation of RBCs signal transduction pathways. Using signaling network computational analysis of the Tyr-phosphoproteomic data, we identified 7 topological clusters. We studied cluster 1, containing Syk-Tyr-kinase: Syk-kinase activity was higher in wild-type than in Ptpre−/− RBCs, validating the network computational analysis and indicating a novel signaling pathway, which involves Fyn and Syk in regulation of red cell morphology. PMID:18924107

Support surfaces for pressure ulcer prevention: A network meta-analysis

PubMed Central

Dumville, Jo C.; Cullum, Nicky

2018-01-01

Background Pressure ulcers are a prevalent and global issue and support surfaces are widely used for preventing ulceration. However, the diversity of available support surfaces and the lack of direct comparisons in RCTs make decision-making difficult. Objectives To determine, using network meta-analysis, the relative effects of different support surfaces in reducing pressure ulcer incidence and comfort and to rank these support surfaces in order of their effectiveness. Methods We conducted a systematic review, using a literature search up to November 2016, to identify randomised trials comparing support surfaces for pressure ulcer prevention. Two reviewers independently performed study selection, risk of bias assessment and data extraction. We grouped the support surfaces according to their characteristics and formed evidence networks using these groups. We used network meta-analysis to estimate the relative effects and effectiveness ranking of the groups for the outcomes of pressure ulcer incidence and participant comfort. GRADE was used to assess the certainty of evidence. Main results We included 65 studies in the review. The network for assessing pressure ulcer incidence comprised evidence of low or very low certainty for most network contrasts. There was moderate-certainty evidence that powered active air surfaces and powered hybrid air surfaces probably reduce pressure ulcer incidence compared with standard hospital surfaces (risk ratios (RR) 0.42, 95% confidence intervals (CI) 0.29 to 0.63; 0.22, 0.07 to 0.66, respectively). The network for comfort suggested that powered active air-surfaces are probably slightly less comfortable than standard hospital mattresses (RR 0.80, 95% CI 0.69 to 0.94; moderate-certainty evidence). Conclusions This is the first network meta-analysis of the effects of support surfaces for pressure ulcer prevention. Powered active air-surfaces probably reduce pressure ulcer incidence, but are probably less comfortable than standard hospital surfaces. Most prevention evidence was of low or very low certainty, and more research is required to reduce these uncertainties. PMID:29474359

Support surfaces for pressure ulcer prevention: A network meta-analysis.

PubMed

Shi, Chunhu; Dumville, Jo C; Cullum, Nicky

2018-01-01

Pressure ulcers are a prevalent and global issue and support surfaces are widely used for preventing ulceration. However, the diversity of available support surfaces and the lack of direct comparisons in RCTs make decision-making difficult. To determine, using network meta-analysis, the relative effects of different support surfaces in reducing pressure ulcer incidence and comfort and to rank these support surfaces in order of their effectiveness. We conducted a systematic review, using a literature search up to November 2016, to identify randomised trials comparing support surfaces for pressure ulcer prevention. Two reviewers independently performed study selection, risk of bias assessment and data extraction. We grouped the support surfaces according to their characteristics and formed evidence networks using these groups. We used network meta-analysis to estimate the relative effects and effectiveness ranking of the groups for the outcomes of pressure ulcer incidence and participant comfort. GRADE was used to assess the certainty of evidence. We included 65 studies in the review. The network for assessing pressure ulcer incidence comprised evidence of low or very low certainty for most network contrasts. There was moderate-certainty evidence that powered active air surfaces and powered hybrid air surfaces probably reduce pressure ulcer incidence compared with standard hospital surfaces (risk ratios (RR) 0.42, 95% confidence intervals (CI) 0.29 to 0.63; 0.22, 0.07 to 0.66, respectively). The network for comfort suggested that powered active air-surfaces are probably slightly less comfortable than standard hospital mattresses (RR 0.80, 95% CI 0.69 to 0.94; moderate-certainty evidence). This is the first network meta-analysis of the effects of support surfaces for pressure ulcer prevention. Powered active air-surfaces probably reduce pressure ulcer incidence, but are probably less comfortable than standard hospital surfaces. Most prevention evidence was of low or very low certainty, and more research is required to reduce these uncertainties.

Network discovery with DCM

PubMed Central

Friston, Karl J.; Li, Baojuan; Daunizeau, Jean; Stephan, Klaas E.

2011-01-01

This paper is about inferring or discovering the functional architecture of distributed systems using Dynamic Causal Modelling (DCM). We describe a scheme that recovers the (dynamic) Bayesian dependency graph (connections in a network) using observed network activity. This network discovery uses Bayesian model selection to identify the sparsity structure (absence of edges or connections) in a graph that best explains observed time-series. The implicit adjacency matrix specifies the form of the network (e.g., cyclic or acyclic) and its graph-theoretical attributes (e.g., degree distribution). The scheme is illustrated using functional magnetic resonance imaging (fMRI) time series to discover functional brain networks. Crucially, it can be applied to experimentally evoked responses (activation studies) or endogenous activity in task-free (resting state) fMRI studies. Unlike conventional approaches to network discovery, DCM permits the analysis of directed and cyclic graphs. Furthermore, it eschews (implausible) Markovian assumptions about the serial independence of random fluctuations. The scheme furnishes a network description of distributed activity in the brain that is optimal in the sense of having the greatest conditional probability, relative to other networks. The networks are characterised in terms of their connectivity or adjacency matrices and conditional distributions over the directed (and reciprocal) effective connectivity between connected nodes or regions. We envisage that this approach will provide a useful complement to current analyses of functional connectivity for both activation and resting-state studies. PMID:21182971

Efficacy and safety of TNF-α inhibitors for active ankylosing spondylitis patients: Multiple treatment comparisons in a network meta-analysis

NASA Astrophysics Data System (ADS)

Liu, Wei; Wu, Yuan-Hao; Zhang, Lei; Liu, Xiao-Ya; Bin Xue; Bin Liu; Wang, Yi; Ji, Yang

2016-09-01

Ankylosing spondylitis (AS) is an inflammatory rheumatic disease with impact on axial skeleton, peripheral joints and enthuses, and it may result in severe disabilities of those parts. Tumor necrosis factor-α (TNF-α) inhibitors are considered as an effective treatment for patients with active AS. In this study, we conducted a network meta-analysis to compare the clinical outcomes of active AS patients treated with TNF-α inhibitors. Randomized controlled trials (RCTs) evaluating the efficacy and safety of TNF-α inhibitors were retrieved in literature search and selected for meta-analysis. Changes in ASAS20 response, ASAS40 response and BASDAI 50% response were regarded as efficacy outcomes; serious adverse events (SAE) and all cause withdrawals were regarded as safety outcomes. Both traditional pairwise meta-analysis and network meta-analysis were performed. The results showed that adalimumab and infliximab had better clinical outcomes. Infliximab consistently appeared to be the most effective TNF-α inhibitors with a high risk of adverse events for patients with active AS; meanwhile, adalimumab ranked highest with respect to adverse effects with efficacy secondary to infliximab. As a result, we were unable to conclude the optimal TNF-α inhibitor and this issue should be solved by future researchers.

Online social networks that connect users to physical activity partners: a review and descriptive analysis.

PubMed

Nakhasi, Atul; Shen, Album Xiaotian; Passarella, Ralph Joseph; Appel, Lawrence J; Anderson, Cheryl Am

2014-06-16

The US Centers for Disease Control and Prevention have identified a lack of encouragement, support, or companionship from family and friends as a major barrier to physical activity. To overcome this barrier, online social networks are now actively leveraging principles of companion social support in novel ways. The aim was to evaluate the functionality, features, and usability of existing online social networks which seek to increase physical activity and fitness among users by connecting them to physical activity partners, not just online, but also face-to-face. In September 2012, we used 3 major databases to identify the website addresses for relevant online social networks. We conducted a Google search using 8 unique keyword combinations: the common keyword "find" coupled with 1 of 4 prefix terms "health," "fitness," "workout," or "physical" coupled with 1 of 2 stem terms "activity partners" or "activity buddies." We also searched 2 prominent technology start-up news sites, TechCrunch and Y Combinator, using 2 unique keyword combinations: the common keyword "find" coupled with 1 of 2 stem terms "activity partners" and "activity buddies." Sites were defined as online social health activity networks if they had the ability to (1) actively find physical activity partners or activities for the user, (2) offer dynamic, real-time tracking or sharing of social activities, and (3) provide virtual profiles to users. We excluded from our analysis sites that were not Web-based, publicly available, in English, or free. Of the 360 initial search results, we identified 13 websites that met our complete criteria of an online social health activity network. Features such as physical activity creation (13/13, 100%) and private messaging (12/13, 92%) appeared almost universally among these websites. However, integration with Web 2.0 technologies such as Facebook and Twitter (9/13, 69%) and the option of direct event joining (8/13, 62%) were not as universally present. Largely absent were more sophisticated features that would enable greater usability, such as interactive engagement prompts (3/13, 23%) and system-created best fit activities (3/13, 23%). Several major online social networks that connect users to physical activity partners currently exist and use standardized features to achieve their goals. Future research is needed to better understand how users utilize these features and how helpful they truly are.

A BEFORE AND AFTER TRIAL OF THE EFFECTIVENESS OF NETWORK ANALYSIS IN HEALTH OPERATIONS MANAGEMENT.

PubMed

Bhalwar, R; Srivastava, M; Verma, S S; Vaze, M; Tilak, V W

1996-10-01

An intervention trial using "before-and-after" approach was undertaken to address the question whether network analysis as a health managerial tool of control can favourably affect the delays that occur in planning and executing the antimalaria operations of a Station Health Organization in a large military station. Exposure variable of interest was intervention with a network diagram, by which the potential causes of delay along the various activities were assessed and remedial measures were introduced during the second year. Sample size was calculated using conventional alpha and beta error levels. The study indicated that there was a definite beneficial outcome in that the operations could be started as well as completed in time during the intervention year. There was reduction in time requirement in 5 out of the 9 activities, the exact 'p' value being 0.08, by both parametric and non-parametric tests. The use of network analysis in health care management has been recommended.

Population activity structure of excitatory and inhibitory neurons

PubMed Central

Doiron, Brent

2017-01-01

Many studies use population analysis approaches, such as dimensionality reduction, to characterize the activity of large groups of neurons. To date, these methods have treated each neuron equally, without taking into account whether neurons are excitatory or inhibitory. We studied population activity structure as a function of neuron type by applying factor analysis to spontaneous activity from spiking networks with balanced excitation and inhibition. Throughout the study, we characterized population activity structure by measuring its dimensionality and the percentage of overall activity variance that is shared among neurons. First, by sampling only excitatory or only inhibitory neurons, we found that the activity structures of these two populations in balanced networks are measurably different. We also found that the population activity structure is dependent on the ratio of excitatory to inhibitory neurons sampled. Finally we classified neurons from extracellular recordings in the primary visual cortex of anesthetized macaques as putative excitatory or inhibitory using waveform classification, and found similarities with the neuron type-specific population activity structure of a balanced network with excitatory clustering. These results imply that knowledge of neuron type is important, and allows for stronger statistical tests, when interpreting population activity structure. PMID:28817581

Multistability of memristive Cohen-Grossberg neural networks with non-monotonic piecewise linear activation functions and time-varying delays.

PubMed

Nie, Xiaobing; Zheng, Wei Xing; Cao, Jinde

2015-11-01

The problem of coexistence and dynamical behaviors of multiple equilibrium points is addressed for a class of memristive Cohen-Grossberg neural networks with non-monotonic piecewise linear activation functions and time-varying delays. By virtue of the fixed point theorem, nonsmooth analysis theory and other analytical tools, some sufficient conditions are established to guarantee that such n-dimensional memristive Cohen-Grossberg neural networks can have 5(n) equilibrium points, among which 3(n) equilibrium points are locally exponentially stable. It is shown that greater storage capacity can be achieved by neural networks with the non-monotonic activation functions introduced herein than the ones with Mexican-hat-type activation function. In addition, unlike most existing multistability results of neural networks with monotonic activation functions, those obtained 3(n) locally stable equilibrium points are located both in saturated regions and unsaturated regions. The theoretical findings are verified by an illustrative example with computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Network analysis of Bogotá’s Ciclovía Recreativa, a self-organized multisectoral community program to promote physical activity in a middle-income country

PubMed Central

Meisel, Jose D; Sarmiento, Olga; Montes, Felipe; Martinez, Edwin O.; Lemoine, Pablo D; Valdivia, Juan A; Brownson, RC; Zarama, Robert

2016-01-01

Purpose Conduct a social network analysis of the health and non-health related organizations that participate in the Bogotá’s Ciclovía Recreativa (Ciclovía). Design Cross sectional study. Setting Ciclovía is a multisectoral community-based mass program in which streets are temporarily closed to motorized transport, allowing exclusive access to individuals for leisure activities and PA. Subjects 25 organizations that participate in the Ciclovía. Measures Seven variables were examined using network analytic methods: relationship, link attributes (integration, contact, and importance), and node attributes (leadership, years in the program, and the sector of the organization). Analysis The network analytic methods were based on a visual descriptive analysis and an exponential random graph model. Results Analysis shows that the most central organizations in the network were outside of the health sector and includes Sports and Recreation, Government, and Security sectors. The organizations work in clusters formed by organizations of different sectors. Organization importance and structural predictors were positively related to integration, while the number of years working with Ciclovía was negatively associated with integration. Conclusion Ciclovía is a network whose structure emerged as a self-organized complex system. Ciclovía of Bogotá is an example of a program with public health potential formed by organizations of multiple sectors with Sports and Recreation as the most central. PMID:23971523

Emerging and encouraging trends in e-prescribing adoption among providers and pharmacies.

PubMed

Gabriel, Meghan E; Furukawa, Michael F; Vaidya, Varun

2013-09-01

The objective of this study is to describe the growth in provider (physician, nurse practitioner, and physician assistant) adoption of e-prescribing and the growth in pharmacies actively accepting e-prescriptions using nationally representative data from December 2008 to December 2012. Additionally, this study explored e-prescribing adoption variation by urban and rural counties. Descriptive analysis of nationally representative, transactional e-prescribing data. Data for this analysis were from Surescripts. Surescripts is a leading e-prescription network utilized by a majority of all chain, franchise, or independently owned pharmacies in the United States routing prescriptions for more than 240 million patients through their network. The total number of prescribers, including physicians, nurse practitioners, and physician assistants e-prescribing via an electronic health record (EHR) on the Surescripts network has increased from 7% to 54%. Additionally, the number of pharmacies actively accepting e-prescriptions is 94%. These increases in pharmacies actively accepting e-prescriptions and the provider's eprescribing mirror the increase in the volume of e-prescriptions sent on the Surescripts network. This analysis shows that the vast majority of pharmacies in the United States are able to accept e-prescriptions and over half of providers are e-prescribing via an EHR.

Centrality in earthquake multiplex networks

NASA Astrophysics Data System (ADS)

Lotfi, Nastaran; Darooneh, Amir Hossein; Rodrigues, Francisco A.

2018-06-01

Seismic time series has been mapped as a complex network, where a geographical region is divided into square cells that represent the nodes and connections are defined according to the sequence of earthquakes. In this paper, we map a seismic time series to a temporal network, described by a multiplex network, and characterize the evolution of the network structure in terms of the eigenvector centrality measure. We generalize previous works that considered the single layer representation of earthquake networks. Our results suggest that the multiplex representation captures better earthquake activity than methods based on single layer networks. We also verify that the regions with highest seismological activities in Iran and California can be identified from the network centrality analysis. The temporal modeling of seismic data provided here may open new possibilities for a better comprehension of the physics of earthquakes.

Motor modules in robot-aided walking

PubMed Central

2012-01-01

Background It is hypothesized that locomotion is achieved by means of rhythm generating networks (central pattern generators) and muscle activation generating networks. This modular organization can be partly identified from the analysis of the muscular activity by means of factorization algorithms. The activity of rhythm generating networks is described by activation signals whilst the muscle intervention generating network is represented by motor modules (muscle synergies). In this study, we extend the analysis of modular organization of walking to the case of robot-aided locomotion, at varying speed and body weight support level. Methods Non Negative Matrix Factorization was applied on surface electromyographic signals of 8 lower limb muscles of healthy subjects walking in gait robotic trainer at different walking velocities (1 to 3km/h) and levels of body weight support (0 to 30%). Results The muscular activity of volunteers could be described by low dimensionality (4 modules), as for overground walking. Moreover, the activation signals during robot-aided walking were bursts of activation timed at specific phases of the gait cycle, underlying an impulsive controller, as also observed in overground walking. This modular organization was consistent across the investigated speeds, body weight support level, and subjects. Conclusions These results indicate that walking in a Lokomat robotic trainer is achieved by similar motor modules and activation signals as overground walking and thus supports the use of robotic training for re-establishing natural walking patterns. PMID:23043818

Network analysis to detect common strategies in Italian foreign direct investment

NASA Astrophysics Data System (ADS)

De Masi, G.; Giovannetti, G.; Ricchiuti, G.

2013-03-01

In this paper we reconstruct and discuss the network of Italian firms investing abroad, exploiting information from complex network analysis. This method, detecting the key nodes of the system (both in terms of firms and countries of destination), allows us to single out the linkages among firms without ex-ante priors. Moreover, through the examination of affiliates’ economic activity, it allows us to highlight different internationalization strategies of “leaders” in different manufacturing sectors.

The Role of Sex of Peers and Gender-Typed Activities in Young Children's Peer Affiliative Networks: A Longitudinal Analysis of Selection and Influence

ERIC Educational Resources Information Center

Martin, Carol Lynn; Kornienko, Olga; Schaefer, David R.; Hanish, Laura D.; Fabes, Richard A.; Goble, Priscilla

2013-01-01

A stochastic actor-based model was used to investigate the origins of sex segregation by examining how similarity in sex of peers and time spent in gender-typed activities affected affiliation network selection and how peers influenced children's ("N" = 292; "M"[subscript age] = 4.3 years) activity involvement. Gender had…

Default Network Modulation and Large-Scale Network Interactivity in Healthy Young and Old Adults

PubMed Central

Schacter, Daniel L.

2012-01-01

We investigated age-related changes in default, attention, and control network activity and their interactions in young and old adults. Brain activity during autobiographical and visuospatial planning was assessed using multivariate analysis and with intrinsic connectivity networks as regions of interest. In both groups, autobiographical planning engaged the default network while visuospatial planning engaged the attention network, consistent with a competition between the domains of internalized and externalized cognition. The control network was engaged for both planning tasks. In young subjects, the control network coupled with the default network during autobiographical planning and with the attention network during visuospatial planning. In old subjects, default-to-control network coupling was observed during both planning tasks, and old adults failed to deactivate the default network during visuospatial planning. This failure is not indicative of default network dysfunction per se, evidenced by default network engagement during autobiographical planning. Rather, a failure to modulate the default network in old adults is indicative of a lower degree of flexible network interactivity and reduced dynamic range of network modulation to changing task demands. PMID:22128194

Social Networks and Participation with Others for Youth with Learning, Attention and Autism Spectrum Disorders

PubMed Central

Kreider, Consuelo M.; Bendixen, Roxanna M.; Young, Mary Ellen; Prudencio, Stephanie M.; McCarty, Christopher; Mann, William C.

2015-01-01

Background Social participation involves activities and roles providing interactions with others, including those within their social networks. Purpose Characterize social networks and participation with others for 36 adolescents, ages 11-16 years, with (n = 19) and without (n = 17) learning disability, attention disorder or high-functioning autism. Methods Social networks were measured using methods of personal network analysis. The Children's Assessment of Participation and Enjoyment With Whom dimension scores was used to measure participation with others. Youth from the clinical group were interviewed regarding their experiences within their social networks. Findings Group differences were observed for six social network variables and in the proportion of overall, physical, recreational, social and informal activities engaged with family and/or friends. Qualitative findings explicated strategies used in building, shaping and maintaining their social networks. Implications Social network factors should be considered when seeking to understand social participation. PMID:26755040

Critical path method applied to research project planning: Fire Economics Evaluation System (FEES)

Treesearch

Earl B. Anderson; R. Stanton Hales

1986-01-01

The critical path method (CPM) of network analysis (a) depicts precedence among the many activities in a project by a network diagram; (b) identifies critical activities by calculating their starting, finishing, and float times; and (c) displays possible schedules by constructing time charts. CPM was applied to the development of the Forest Service's Fire...

Characterizing and modeling the dynamics of activity and popularity.

PubMed

Zhang, Peng; Li, Menghui; Gao, Liang; Fan, Ying; Di, Zengru

2014-01-01

Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks.

Characterizing and Modeling the Dynamics of Activity and Popularity

PubMed Central

Zhang, Peng; Li, Menghui; Gao, Liang; Fan, Ying; Di, Zengru

2014-01-01

Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks. PMID:24586586

A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

PubMed

Li, Qian; Li, Xudong; Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-03-22

Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking.

A Network-Based Multi-Target Computational Estimation Scheme for Anticoagulant Activities of Compounds

PubMed Central

Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-01-01

Background Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. Methodology We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. Conclusions This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking. PMID:21445339

Network robustness assessed within a dual connectivity framework: joint dynamics of the Active and Idle Networks.

PubMed

Tejedor, Alejandro; Longjas, Anthony; Zaliapin, Ilya; Ambroj, Samuel; Foufoula-Georgiou, Efi

2017-08-17

Network robustness against attacks has been widely studied in fields as diverse as the Internet, power grids and human societies. But current definition of robustness is only accounting for half of the story: the connectivity of the nodes unaffected by the attack. Here we propose a new framework to assess network robustness, wherein the connectivity of the affected nodes is also taken into consideration, acknowledging that it plays a crucial role in properly evaluating the overall network robustness in terms of its future recovery from the attack. Specifically, we propose a dual perspective approach wherein at any instant in the network evolution under attack, two distinct networks are defined: (i) the Active Network (AN) composed of the unaffected nodes and (ii) the Idle Network (IN) composed of the affected nodes. The proposed robustness metric considers both the efficiency of destroying the AN and that of building-up the IN. We show, via analysis of well-known prototype networks and real world data, that trade-offs between the efficiency of Active and Idle Network dynamics give rise to surprising robustness crossovers and re-rankings, which can have significant implications for decision making.

Reward-based training of recurrent neural networks for cognitive and value-based tasks

PubMed Central

Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

2017-01-01

Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal’s internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task. DOI: http://dx.doi.org/10.7554/eLife.21492.001 PMID:28084991

Automatic network coupling analysis for dynamical systems based on detailed kinetic models.

PubMed

Lebiedz, Dirk; Kammerer, Julia; Brandt-Pollmann, Ulrich

2005-10-01

We introduce a numerical complexity reduction method for the automatic identification and analysis of dynamic network decompositions in (bio)chemical kinetics based on error-controlled computation of a minimal model dimension represented by the number of (locally) active dynamical modes. Our algorithm exploits a generalized sensitivity analysis along state trajectories and subsequent singular value decomposition of sensitivity matrices for the identification of these dominant dynamical modes. It allows for a dynamic coupling analysis of (bio)chemical species in kinetic models that can be exploited for the piecewise computation of a minimal model on small time intervals and offers valuable functional insight into highly nonlinear reaction mechanisms and network dynamics. We present results for the identification of network decompositions in a simple oscillatory chemical reaction, time scale separation based model reduction in a Michaelis-Menten enzyme system and network decomposition of a detailed model for the oscillatory peroxidase-oxidase enzyme system.

Sparse representation of whole-brain fMRI signals for identification of functional networks.

PubMed

Lv, Jinglei; Jiang, Xi; Li, Xiang; Zhu, Dajiang; Chen, Hanbo; Zhang, Tuo; Zhang, Shu; Hu, Xintao; Han, Junwei; Huang, Heng; Zhang, Jing; Guo, Lei; Liu, Tianming

2015-02-01

There have been several recent studies that used sparse representation for fMRI signal analysis and activation detection based on the assumption that each voxel's fMRI signal is linearly composed of sparse components. Previous studies have employed sparse coding to model functional networks in various modalities and scales. These prior contributions inspired the exploration of whether/how sparse representation can be used to identify functional networks in a voxel-wise way and on the whole brain scale. This paper presents a novel, alternative methodology of identifying multiple functional networks via sparse representation of whole-brain task-based fMRI signals. Our basic idea is that all fMRI signals within the whole brain of one subject are aggregated into a big data matrix, which is then factorized into an over-complete dictionary basis matrix and a reference weight matrix via an effective online dictionary learning algorithm. Our extensive experimental results have shown that this novel methodology can uncover multiple functional networks that can be well characterized and interpreted in spatial, temporal and frequency domains based on current brain science knowledge. Importantly, these well-characterized functional network components are quite reproducible in different brains. In general, our methods offer a novel, effective and unified solution to multiple fMRI data analysis tasks including activation detection, de-activation detection, and functional network identification. Copyright © 2014 Elsevier B.V. All rights reserved.

Identification of the anti-tumor activity and mechanisms of nuciferine through a network pharmacology approach

PubMed Central

Qi, Quan; Li, Rui; Li, Hui-ying; Cao, Yu-bing; Bai, Ming; Fan, Xiao-jing; Wang, Shu-yan; Zhang, Bo; Li, Shao

2016-01-01

Aim: Nuciferine is an aporphine alkaloid extracted from lotus leaves, which is a raw material in Chinese medicinal herb for weight loss. In this study we used a network pharmacology approach to identify the anti-tumor activity of nuciferine and the underlying mechanisms. Methods: The pharmacological activities and mechanisms of nuciferine were identified through target profile prediction, clustering analysis and functional enrichment analysis using our traditional Chinese medicine (TCM) network pharmacology platform. The anti-tumor activity of nuciferine was validated by in vitro and in vivo experiments. The anti-tumor mechanisms of nuciferine were predicted through network target analysis and verified by in vitro experiments. Results: The nuciferine target profile was enriched with signaling pathways and biological functions, including “regulation of lipase activity”, “response to nicotine” and “regulation of cell proliferation”. Target profile clustering results suggested that nuciferine to exert anti-tumor effect. In experimental validation, nuciferine (0.8 mg/mL) markedly inhibited the viability of human neuroblastoma SY5Y cells and mouse colorectal cancer CT26 cells in vitro, and nuciferine (0.05 mg/mL) significantly suppressed the invasion of 6 cancer cell lines in vitro. Intraperitoneal injection of nuciferine (9.5 mg/mL, ip, 3 times a week for 3 weeks) significantly decreased the weight of SY5Y and CT26 tumor xenografts in nude mice. Network target analysis and experimental validation in SY5Y and CT26 cells showed that the anti-tumor effect of nuciferine was mediated through inhibiting the PI3K-AKT signaling pathway and IL-1 levels in SY5Y and CT26 cells. Conclusion: By using a TCM network pharmacology method, nuciferine is identified as an anti-tumor agent against human neuroblastoma and mouse colorectal cancer in vitro and in vivo, through inhibiting the PI3K-AKT signaling pathways and IL-1 levels. PMID:27180984

Affective mentalizing and brain activity at rest in the behavioral variant of frontotemporal dementia

PubMed Central

Caminiti, Silvia P.; Canessa, Nicola; Cerami, Chiara; Dodich, Alessandra; Crespi, Chiara; Iannaccone, Sandro; Marcone, Alessandra; Falini, Andrea; Cappa, Stefano F.

2015-01-01

Background bvFTD patients display an impairment in the attribution of cognitive and affective states to others, reflecting GM atrophy in brain regions associated with social cognition, such as amygdala, superior temporal cortex and posterior insula. Distinctive patterns of abnormal brain functioning at rest have been reported in bvFTD, but their relationship with defective attribution of affective states has not been investigated. Objective To investigate the relationship among resting-state brain activity, gray matter (GM) atrophy and the attribution of mental states in the behavioral variant of fronto-temporal degeneration (bvFTD). Methods We compared 12 bvFTD patients with 30 age- and education-matched healthy controls on a) performance in a task requiring the attribution of affective vs. cognitive mental states; b) metrics of resting-state activity in known functional networks; and c) the relationship between task-performances and resting-state metrics. In addition, we assessed a connection between abnormal resting-state metrics and GM atrophy. Results Compared with controls, bvFTD patients showed a reduction of intra-network coherent activity in several components, as well as decreased strength of activation in networks related to attentional processing. Anomalous resting-state activity involved networks which also displayed a significant reduction of GM density. In patients, compared with controls, higher affective mentalizing performance correlated with stronger functional connectivity between medial prefrontal sectors of the default-mode and attentional/performance monitoring networks, as well as with increased coherent activity in components of the executive, sensorimotor and fronto-limbic networks. Conclusions Some of the observed effects may reflect specific compensatory mechanisms for the atrophic changes involving regions in charge of affective mentalizing. The analysis of specific resting-state networks thus highlights an intermediate level of analysis between abnormal brain structure and impaired behavioral performance in bvFTD, reflecting both dysfunction and compensation mechanisms. PMID:26594631

Implementing multiple intervention strategies in Dutch public health-related policy networks.

PubMed

Harting, Janneke; Peters, Dorothee; Grêaux, Kimberly; van Assema, Patricia; Verweij, Stefan; Stronks, Karien; Klijn, Erik-Hans

2017-10-13

Improving public health requires multiple intervention strategies. Implementing such an intervention mix is supposed to require a multisectoral policy network. As evidence to support this assumption is scarce, we examined under which conditions public health-related policy networks were able to implement an intervention mix. Data were collected (2009-14) from 29 Dutch public health policy networks. Surveys were used to identify the number of policy sectors, participation of actors, level of trust, networking by the project leader, and intervention strategies implemented. Conditions sufficient for an intervention mix (≥3 of 4 non-educational strategies present) were determined in a fuzzy-set qualitative comparative analysis. A multisectoral policy network (≥7 of 14 sectors present) was neither a necessary nor a sufficient condition. In multisectoral networks, additionally required was either the active participation of network actors (≥50% actively involved) or active networking by the project leader (≥monthly contacts with network actors). In policy networks that included few sectors, a high level of trust (positive perceptions of each other's intentions) was needed-in the absence though of any of the other conditions. If the network actors were also actively involved, an extra requirement was active networking by the project leader. We conclude that the multisectoral composition of policy networks can contribute to the implementation of a variety of intervention strategies, but not without additional efforts. However, policy networks that include only few sectors are also able to implement an intervention mix. Here, trust seems to be the most important condition. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Monitoring of Students' Interaction in Online Learning Settings by Structural Network Analysis and Indicators.

PubMed

Ammenwerth, Elske; Hackl, Werner O

2017-01-01

Learning as a constructive process works best in interaction with other learners. Support of social interaction processes is a particular challenge within online learning settings due to the spatial and temporal distribution of participants. It should thus be carefully monitored. We present structural network analysis and related indicators to analyse and visualize interaction patterns of participants in online learning settings. We validate this approach in two online courses and show how the visualization helps to monitor interaction and to identify activity profiles of learners. Structural network analysis is a feasible approach for an analysis of the intensity and direction of interaction in online learning settings.

Riometer based Neural Network Prediction of Kp

NASA Astrophysics Data System (ADS)

Arnason, K. M.; Spanswick, E.; Chaddock, D.; Tabrizi, A. F.; Behjat, L.

2017-12-01

The Canadian Geospace Observatory Riometer Array is a network of 11 wide-beam riometers deployed across Central and Northern Canada. The geographic coverage of the network affords a near continent scale view of high energy (>30keV) electron precipitation at a very course spatial resolution. In this paper we present the first results from a neural network based analysis of riometer data. Trained on decades of riometer data, the neural network is tuned to predict a simple index of global geomagnetic activity (Kp) based solely on the information provided by the high energy electron precipitation over Canada. We present results from various configurations of training and discuss the applicability of this technique for short term prediction of geomagnetic activity.

Driver-centred vehicle automation: using network analysis for agent-based modelling of the driver in highly automated driving systems.

PubMed

Banks, Victoria A; Stanton, Neville A

2016-11-01

To the average driver, the concept of automation in driving infers that they can become completely 'hands and feet free'. This is a common misconception, however, one that has been shown through the application of Network Analysis to new Cruise Assist technologies that may feature on our roads by 2020. Through the adoption of a Systems Theoretic approach, this paper introduces the concept of driver-initiated automation which reflects the role of the driver in highly automated driving systems. Using a combination of traditional task analysis and the application of quantitative network metrics, this agent-based modelling paper shows how the role of the driver remains an integral part of the driving system implicating the need for designers to ensure they are provided with the tools necessary to remain actively in-the-loop despite giving increasing opportunities to delegate their control to the automated subsystems. Practitioner Summary: This paper describes and analyses a driver-initiated command and control system of automation using representations afforded by task and social networks to understand how drivers remain actively involved in the task. A network analysis of different driver commands suggests that such a strategy does maintain the driver in the control loop.

Neural electrical activity and neural network growth.

PubMed

Gafarov, F M

2018-05-01

The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

Network analysis of physics discussion forums and links to course success

NASA Astrophysics Data System (ADS)

Traxler, Adrienne; Gavrin, Andrew; Lindell, Rebecca

2017-01-01

Large introductory science courses tend to isolate students, with negative consequences for long-term retention in college. Many active learning courses build collaboration and community among students as an explicit goal, and social network analysis has been used to track the development and beneficial effects of these collaborations. Here we supplement such work by conducting network analysis of online course discussion forums in two semesters of an introductory physics class. Online forums provide a tool for engaging students with each other outside of class, and offer new opportunities to commuter or non-traditional students with limited on-campus time. We look for correlations between position in the forum network (centrality) and final course grades. Preliminary investigation has shown weak correlations in the very dense full-semester network, so we will consider reduced ''backbone'' networks that highlight the most consistent links between students. Future work and implications for instruction will also be discussed.

Anti AIDS drug design with the help of neural networks

NASA Astrophysics Data System (ADS)

Tetko, I. V.; Tanchuk, V. Yu.; Luik, A. I.

1995-04-01

Artificial neural networks were used to analyze and predict the human immunodefiency virus type 1 reverse transcriptase inhibitors. Training and control set included 44 molecules (most of them are well-known substances such as AZT, TIBO, dde, etc.) The biological activities of molecules were taken from literature and rated for two classes: active and inactive compounds according to their values. We used topological indices as molecular parameters. Four most informative parameters (out of 46) were chosen using cluster analysis and original input parameters' estimation procedure and were used to predict activities of both control and new (synthesized in our institute) molecules. We applied pruning network algorithm and network ensembles to obtain the final classifier and avoid chance correlation. The increasing of neural network generalization of the data from the control set was observed, when using the aforementioned methods. The prognosis of new molecules revealed one molecule as possibly active. It was confirmed by further biological tests. The compound was as active as AZT and in order less toxic. The active compound is currently being evaluated in pre clinical trials as possible drug for anti-AIDS therapy.

PhotoMEA: an opto-electronic biosensor for monitoring in vitro neuronal network activity.

PubMed

Ghezzi, Diego; Pedrocchi, Alessandra; Menegon, Andrea; Mantero, Sara; Valtorta, Flavia; Ferrigno, Giancarlo

2007-02-01

PhotoMEA is a biosensor useful for the analysis of an in vitro neuronal network, fully based on optical methods. Its function is based on the stimulation of neurons with caged glutamate and the recording of neuronal activity by Voltage-Sensitive fluorescent Dyes (VSD). The main advantage is that it will be possible to stimulate even at sub-single neuron level and to record with high resolution the activity of the entire network in the culture. A large-scale view of neuronal intercommunications offers a unique opportunity for testing the ability of drugs to affect neuronal properties as well as alterations in the behaviour of the entire network. The concept and a prototype for validation is described here in detail.

Structure-Based Network Analysis of Activation Mechanisms in the ErbB Family of Receptor Tyrosine Kinases: The Regulatory Spine Residues Are Global Mediators of Structural Stability and Allosteric Interactions

PubMed Central

James, Kevin A.; Verkhivker, Gennady M.

2014-01-01

The ErbB protein tyrosine kinases are among the most important cell signaling families and mutation-induced modulation of their activity is associated with diverse functions in biological networks and human disease. We have combined molecular dynamics simulations of the ErbB kinases with the protein structure network modeling to characterize the reorganization of the residue interaction networks during conformational equilibrium changes in the normal and oncogenic forms. Structural stability and network analyses have identified local communities integrated around high centrality sites that correspond to the regulatory spine residues. This analysis has provided a quantitative insight to the mechanism of mutation-induced “superacceptor” activity in oncogenic EGFR dimers. We have found that kinase activation may be determined by allosteric interactions between modules of structurally stable residues that synchronize the dynamics in the nucleotide binding site and the αC-helix with the collective motions of the integrating αF-helix and the substrate binding site. The results of this study have pointed to a central role of the conserved His-Arg-Asp (HRD) motif in the catalytic loop and the Asp-Phe-Gly (DFG) motif as key mediators of structural stability and allosteric communications in the ErbB kinases. We have determined that residues that are indispensable for kinase regulation and catalysis often corresponded to the high centrality nodes within the protein structure network and could be distinguished by their unique network signatures. The optimal communication pathways are also controlled by these nodes and may ensure efficient allosteric signaling in the functional kinase state. Structure-based network analysis has quantified subtle effects of ATP binding on conformational dynamics and stability of the EGFR structures. Consistent with the NMR studies, we have found that nucleotide-induced modulation of the residue interaction networks is not limited to the ATP site, and may enhance allosteric cooperativity with the substrate binding region by increasing communication capabilities of mediating residues. PMID:25427151

Soil ionomic and enzymatic responses and correlations to fertilizations amended with and without organic fertilizer in long-term experiments.

PubMed

Feng, Xumeng; Ling, Ning; Chen, Huan; Zhu, Chen; Duan, Yinghua; Peng, Chang; Yu, Guanghui; Ran, Wei; Shen, Qirong; Guo, Shiwei

2016-04-15

To investigate potential interactions between the soil ionome and enzyme activities affected by fertilization with or without organic fertilizer, soil samples were collected from four long-term experiments over China. Irrespective of variable interactions, fertilization type was the major factor impacting soil ionomic behavior and accounted for 15.14% of the overall impact. Sampling site was the major factor affecting soil enzymatic profile and accounted for 34.25% of the overall impact. The availabilities of Pb, La, Ni, Co, Fe and Al were significantly higher in soil with only chemical fertilizer than the soil with organic amendment. Most of the soil enzyme activities, including α-glucosidase activity, were significantly activated by organic amendment. Network analysis between the soil ionome and the soil enzyme activities was more complex in the organic-amended soils than in the chemical fertilized soils, whereas the network analysis among the soil ions was less complex with organic amendment. Moreover, α-glucosidase was revealed to generally harbor more corrections with the soil ionic availabilities in network. We concluded that some of the soil enzymes activated by organic input can make the soil more vigorous and stable and that the α-glucosidase revealed by this analysis might help stabilize the soil ion availability.

Soil ionomic and enzymatic responses and correlations to fertilizations amended with and without organic fertilizer in long-term experiments

PubMed Central

Feng, Xumeng; Ling, Ning; Chen, Huan; Zhu, Chen; Duan, Yinghua; Peng, Chang; Yu, Guanghui; Ran, Wei; Shen, Qirong; Guo, Shiwei

2016-01-01

To investigate potential interactions between the soil ionome and enzyme activities affected by fertilization with or without organic fertilizer, soil samples were collected from four long-term experiments over China. Irrespective of variable interactions, fertilization type was the major factor impacting soil ionomic behavior and accounted for 15.14% of the overall impact. Sampling site was the major factor affecting soil enzymatic profile and accounted for 34.25% of the overall impact. The availabilities of Pb, La, Ni, Co, Fe and Al were significantly higher in soil with only chemical fertilizer than the soil with organic amendment. Most of the soil enzyme activities, including α-glucosidase activity, were significantly activated by organic amendment. Network analysis between the soil ionome and the soil enzyme activities was more complex in the organic-amended soils than in the chemical fertilized soils, whereas the network analysis among the soil ions was less complex with organic amendment. Moreover, α-glucosidase was revealed to generally harbor more corrections with the soil ionic availabilities in network. We concluded that some of the soil enzymes activated by organic input can make the soil more vigorous and stable and that the α-glucosidase revealed by this analysis might help stabilize the soil ion availability. PMID:27079657

Using Social Network Analysis to Assess Mentorship and Collaboration in a Public Health Network.

PubMed

Petrescu-Prahova, Miruna; Belza, Basia; Leith, Katherine; Allen, Peg; Coe, Norma B; Anderson, Lynda A

2015-08-20

Addressing chronic disease burden requires the creation of collaborative networks to promote systemic changes and engage stakeholders. Although many such networks exist, they are rarely assessed with tools that account for their complexity. This study examined the structure of mentorship and collaboration relationships among members of the Healthy Aging Research Network (HAN) using social network analysis (SNA). We invited 97 HAN members and partners to complete an online social network survey that included closed-ended questions about HAN-specific mentorship and collaboration during the previous 12 months. Collaboration was measured by examining the activity of the network on 6 types of products: published articles, in-progress manuscripts, grant applications, tools, research projects, and presentations. We computed network-level measures such as density, number of components, and centralization to assess the cohesiveness of the network. Sixty-three respondents completed the survey (response rate, 65%). Responses, which included information about collaboration with nonrespondents, suggested that 74% of HAN members were connected through mentorship ties and that all 97 members were connected through at least one form of collaboration. Mentorship and collaboration ties were present both within and across boundaries of HAN member organizations. SNA of public health collaborative networks provides understanding about the structure of relationships that are formed as a result of participation in network activities. This approach may offer members and funders a way to assess the impact of such networks that goes beyond simply measuring products and participation at the individual level.

Brain Network Activity During Face Perception: The Impact of Perceptual Familiarity and Individual Differences in Childhood Experience.

PubMed

Cloutier, Jasmin; Li, Tianyi; Mišic, Bratislav; Correll, Joshua; Berman, Marc G

2017-09-01

An extended distributed network of brain regions supports face perception. Face familiarity influences activity in brain regions involved in this network, but the impact of perceptual familiarity on this network has never been directly assessed with the use of partial least squares analysis. In the present work, we use this multivariate statistical analysis to examine how face-processing systems are differentially recruited by characteristics of the targets (i.e. perceptual familiarity and race) and of the perceivers (i.e. childhood interracial contact). Novel faces were found to preferentially recruit a large distributed face-processing network compared with perceptually familiar faces. Additionally, increased interracial contact during childhood led to decreased recruitment of distributed brain networks previously implicated in face perception, salience detection, and social cognition. Current results provide a novel perspective on the impact of cross-race exposure, suggesting that interracial contact early in life may dramatically shape the neural substrates of face perception generally. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Encoding Time in Feedforward Trajectories of a Recurrent Neural Network Model.

PubMed

Hardy, N F; Buonomano, Dean V

2018-02-01

Brain activity evolves through time, creating trajectories of activity that underlie sensorimotor processing, behavior, and learning and memory. Therefore, understanding the temporal nature of neural dynamics is essential to understanding brain function and behavior. In vivo studies have demonstrated that sequential transient activation of neurons can encode time. However, it remains unclear whether these patterns emerge from feedforward network architectures or from recurrent networks and, furthermore, what role network structure plays in timing. We address these issues using a recurrent neural network (RNN) model with distinct populations of excitatory and inhibitory units. Consistent with experimental data, a single RNN could autonomously produce multiple functionally feedforward trajectories, thus potentially encoding multiple timed motor patterns lasting up to several seconds. Importantly, the model accounted for Weber's law, a hallmark of timing behavior. Analysis of network connectivity revealed that efficiency-a measure of network interconnectedness-decreased as the number of stored trajectories increased. Additionally, the balance of excitation (E) and inhibition (I) shifted toward excitation during each unit's activation time, generating the prediction that observed sequential activity relies on dynamic control of the E/I balance. Our results establish for the first time that the same RNN can generate multiple functionally feedforward patterns of activity as a result of dynamic shifts in the E/I balance imposed by the connectome of the RNN. We conclude that recurrent network architectures account for sequential neural activity, as well as for a fundamental signature of timing behavior: Weber's law.

Social network types among older Korean adults: Associations with subjective health.

PubMed

Sohn, Sung Yun; Joo, Won-Tak; Kim, Woo Jung; Kim, Se Joo; Youm, Yoosik; Kim, Hyeon Chang; Park, Yeong-Ran; Lee, Eun

2017-01-01

With population aging now a global phenomenon, the health of older adults is becoming an increasingly important issue. Because the Korean population is aging at an unprecedented rate, preparing for public health problems associated with old age is particularly salient in this country. As the physical and mental health of older adults is related to their social relationships, investigating the social networks of older adults and their relationship to health status is important for establishing public health policies. The aims of this study were to identify social network types among older adults in South Korea and to examine the relationship of these social network types with self-rated health and depression. Data from the Korean Social Life, Health, and Aging Project were analyzed. Model-based clustering using finite normal mixture modeling was conducted to identify the social network types based on ten criterion variables of social relationships and activities: marital status, number of children, number of close relatives, number of friends, frequency of attendance at religious services, attendance at organized group meetings, in-degree centrality, out-degree centrality, closeness centrality, and betweenness centrality. Multivariate regression analysis was conducted to examine associations between the identified social network types and self-rated health and depression. The model-based clustering analysis revealed that social networks clustered into five types: diverse, family, congregant, congregant-restricted, and restricted. Diverse or family social network types were significantly associated with more favorable subjective mental health, whereas the restricted network type was significantly associated with poorer ratings of mental and physical health. In addition, our analysis identified unique social network types related to religious activities. In summary, we developed a comprehensive social network typology for older Korean adults. Copyright © 2016 Elsevier Ltd. All rights reserved.

How to Identify Success Among Networks That Promote Active Living

PubMed Central

Varda, Danielle; Reed, Hannah; Retrum, Jessica; Tabak, Rachel; Gustat, Jeanette; O'Hara Tompkins, Nancy

2015-01-01

Objectives. We evaluated organization- and network-level factors that influence organizations’ perceived success. This is important for managing interorganizational networks, which can mobilize communities to address complex health issues such as physical activity, and for achieving change. Methods. In 2011, we used structured interview and network survey data from 22 states in the United States to estimate multilevel random-intercept models to understand organization- and network-level factors that explain perceived network success. Results. A total of 53 of 59 “whole networks” met the criteria for inclusion in the analysis (89.8%). Coordinators identified 559 organizations, with 3 to 12 organizations from each network taking the online survey (response rate = 69.7%; range = 33%–100%). Occupying a leadership position (P < .01), the amount of time with the network (P < .05), and support from community leaders (P < .05) emerged as correlates of perceived success. Conclusions. Organizations’ perceptions of success can influence decisions about continuing involvement and investment in networks designed to promote environment and policy change for active living. Understanding these factors can help leaders manage complex networks that involve diverse memberships, varied interests, and competing community-level priorities. PMID:26378863

Architectural Design for the Global Legal Information Network

NASA Technical Reports Server (NTRS)

Kalpakis, Konstantinos

1999-01-01

In this report, we provide a summary of our activities regarding the goals, requirements analysis, design, and prototype implementation for the Global Legal Information Network, a joint effort between the Law Library of Congress and NASA.

Predictive Control of Networked Multiagent Systems via Cloud Computing.

PubMed

Liu, Guo-Ping

2017-01-18

This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.

Graph analysis of functional brain networks: practical issues in translational neuroscience

PubMed Central

De Vico Fallani, Fabrizio; Richiardi, Jonas; Chavez, Mario; Achard, Sophie

2014-01-01

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes. PMID:25180301

Exploring Molecular Mechanisms of Paradoxical Activation in the BRAF Kinase Dimers: Atomistic Simulations of Conformational Dynamics and Modeling of Allosteric Communication Networks and Signaling Pathways

PubMed Central

Tse, Amanda; Verkhivker, Gennady M.

2016-01-01

The recent studies have revealed that most BRAF inhibitors can paradoxically induce kinase activation by promoting dimerization and enzyme transactivation. Despite rapidly growing number of structural and functional studies about the BRAF dimer complexes, the molecular basis of paradoxical activation phenomenon is poorly understood and remains largely hypothetical. In this work, we have explored the relationships between inhibitor binding, protein dynamics and allosteric signaling in the BRAF dimers using a network-centric approach. Using this theoretical framework, we have combined molecular dynamics simulations with coevolutionary analysis and modeling of the residue interaction networks to determine molecular determinants of paradoxical activation. We have investigated functional effects produced by paradox inducer inhibitors PLX4720, Dabrafenib, Vemurafenib and a paradox breaker inhibitor PLX7904. Functional dynamics and binding free energy analyses of the BRAF dimer complexes have suggested that negative cooperativity effect and dimer-promoting potential of the inhibitors could be important drivers of paradoxical activation. We have introduced a protein structure network model in which coevolutionary residue dependencies and dynamic maps of residue correlations are integrated in the construction and analysis of the residue interaction networks. The results have shown that coevolutionary residues in the BRAF structures could assemble into independent structural modules and form a global interaction network that may promote dimerization. We have also found that BRAF inhibitors could modulate centrality and communication propensities of global mediating centers in the residue interaction networks. By simulating allosteric communication pathways in the BRAF structures, we have determined that paradox inducer and breaker inhibitors may activate specific signaling routes that correlate with the extent of paradoxical activation. While paradox inducer inhibitors may facilitate a rapid and efficient communication via an optimal single pathway, the paradox breaker may induce a broader ensemble of suboptimal and less efficient communication routes. The central finding of our study is that paradox breaker PLX7904 could mimic structural, dynamic and network features of the inactive BRAF-WT monomer that may be required for evading paradoxical activation. The results of this study rationalize the existing structure-functional experiments by offering a network-centric rationale of the paradoxical activation phenomenon. We argue that BRAF inhibitors that amplify dynamic features of the inactive BRAF-WT monomer and intervene with the allosteric interaction networks may serve as effective paradox breakers in cellular environment. PMID:27861609

Activation of an IL-6:STAT3-dependent Transcriptome in Pediatric-onset Inflammatory Bowel Disease

PubMed Central

Carey, Rebecca; Jurickova, Ingrid; Ballard, Edgar; Bonkowski, Erin; Han, Xiaonan; Xu, Huan; Denson, Lee A.

2008-01-01

Background: While activation of the IL-6-dependent transcription factor signal transducer and activator of transcription 3 (STAT3) has been implicated in the pathogenesis of inflammatory bowel disease (IBD), a direct effect on mucosal gene expression and inflammation has not been shown. We hypothesized that a proinflammatory IL-6:STAT3-dependent biological network would be up regulated in pediatric-onset IBD patients, and would be associated with the severity of mucosal inflammation. Methods: Patients with pediatric-onset IBD were enrolled at diagnosis and during therapy. Serum cytokine analysis was performed using Bioplex. STAT3 phosphorylation (pSTAT3) in peripheral blood leukocytes (PBLs) was assessed by flow cytometry. Immunohistochemistry of colonic mucosa was used to localize pSTAT3 and STAT3 target genes. Microarray analysis was used to determine RNA expression profiles from colon biopsies. Results: Circulating IL-6 was upregulated in active IBD patients at diagnosis and during therapy. STAT3 activation was increased in PB granulocytes, IL-6-stimulated CD3+/CD4+ lymphocytes, and affected colon biopsies of IBD patients. The frequency of pSTAT3+PB granulocytes and colon epithelial and lamina propria cells was highly correlated with the degree of mucosal inflammation. Microarray and Ingenuity Systems bioinformatics analysis identified IL-6:STAT3-dependent biological networks upregulated in IBD patients which control leukocyte recruitment, HLA expression, angiogenesis, and tissue remodeling. Conclusions: A proinflammatory IL6:STAT3 biologic network is upregulated in active pediatric IBD patients at diagnosis and during therapy. Specific targeting of this network may be effective in reducing mucosal inflammation. PMID:18069684

Analysis and Synthesis of Adaptive Neural Elements and Assembles

DTIC Science & Technology

1992-02-17

effects of neuromodulators on electrically activity. Based on the simulations it appears that there are potentially novel mechanisms with which modulatory...and Byrne, J.H. A learning rule based on empirically-derived activity-dependent neuromodulation supports operant conditioning in a small network...dependent neuromodulation can support operant conditioning in a small oscillatory network". 2. Society for Neuroscience Short Course on Neural

Coexistence and local μ-stability of multiple equilibrium points for memristive neural networks with nonmonotonic piecewise linear activation functions and unbounded time-varying delays.

PubMed

Nie, Xiaobing; Zheng, Wei Xing; Cao, Jinde

2016-12-01

In this paper, the coexistence and dynamical behaviors of multiple equilibrium points are discussed for a class of memristive neural networks (MNNs) with unbounded time-varying delays and nonmonotonic piecewise linear activation functions. By means of the fixed point theorem, nonsmooth analysis theory and rigorous mathematical analysis, it is proven that under some conditions, such n-neuron MNNs can have 5 n equilibrium points located in ℜ n , and 3 n of them are locally μ-stable. As a direct application, some criteria are also obtained on the multiple exponential stability, multiple power stability, multiple log-stability and multiple log-log-stability. All these results reveal that the addressed neural networks with activation functions introduced in this paper can generate greater storage capacity than the ones with Mexican-hat-type activation function. Numerical simulations are presented to substantiate the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Diagnostic utility of brain activity flow patterns analysis in attention deficit hyperactivity disorder.

PubMed

Biederman, J; Hammerness, P; Sadeh, B; Peremen, Z; Amit, A; Or-Ly, H; Stern, Y; Reches, A; Geva, A; Faraone, S V

2017-05-01

A previous small study suggested that Brain Network Activation (BNA), a novel ERP-based brain network analysis, may have diagnostic utility in attention deficit hyperactivity disorder (ADHD). In this study we examined the diagnostic capability of a new advanced version of the BNA methodology on a larger population of adults with and without ADHD. Subjects were unmedicated right-handed 18- to 55-year-old adults of both sexes with and without a DSM-IV diagnosis of ADHD. We collected EEG while the subjects were performing a response inhibition task (Go/NoGo) and then applied a spatio-temporal Brain Network Activation (BNA) analysis of the EEG data. This analysis produced a display of qualitative measures of brain states (BNA scores) providing information on cortical connectivity. This complex set of scores was then fed into a machine learning algorithm. The BNA analysis of the EEG data recorded during the Go/NoGo task demonstrated a high discriminative capacity between ADHD patients and controls (AUC = 0.92, specificity = 0.95, sensitivity = 0.86 for the Go condition; AUC = 0.84, specificity = 0.91, sensitivity = 0.76 for the NoGo condition). BNA methodology can help differentiate between ADHD and healthy controls based on functional brain connectivity. The data support the utility of the tool to augment clinical examinations by objective evaluation of electrophysiological changes associated with ADHD. Results also support a network-based approach to the study of ADHD.

Exogenous vs. endogenous attention: Shifting the balance of fronto-parietal activity.

PubMed

Meyer, Kristin N; Du, Feng; Parks, Emily; Hopfinger, Joseph B

2018-03-01

Despite behavioral and electrophysiological evidence for dissociations between endogenous (voluntary) and exogenous (reflexive) attention, fMRI results have yet to consistently and clearly differentiate neural activation patterns between these two types of attention. This study specifically aimed to determine whether activity in the dorsal fronto-parietal network differed between endogenous and exogenous conditions. Participants performed a visual discrimination task in endogenous and exogenous attention conditions while undergoing fMRI scanning. Analyses revealed robust and bilateral activation throughout the dorsal fronto-parietal network for each condition, in line with many previous results. In order to investigate possible differences in the balance of neural activity within this network with greater sensitivity, a priori regions of interest (ROIs) were selected for analysis, centered on the frontal eye fields (FEF) and intraparietal sulcus (IPS) regions identified in previous studies. The results revealed a significant interaction between region, condition, and hemisphere. Specifically, in the left hemisphere, frontal areas were more active than parietal areas, but only during endogenous attention. Activity in the right hemisphere, in contrast, remained relatively consistent for these regions across conditions. Analysis of this activity over time indicates that this left-hemispheric regional imbalance is present within the FEF early, at 3-6.5 s post-stimulus presentation, whereas a regional imbalance in the exogenous condition is not evident until 6.5-8 s post-stimulus presentation. Overall, our results provide new evidence that although the dorsal fronto-parietal network is indeed associated with both types of attentional orienting, regions of the network are differentially engaged over time and across hemispheres depending on the type of attention. Copyright © 2018 Elsevier Ltd. All rights reserved.

Statistical identification of stimulus-activated network nodes in multi-neuron voltage-sensitive dye optical recordings.

PubMed

Fathiazar, Elham; Anemuller, Jorn; Kretzberg, Jutta

2016-08-01

Voltage-Sensitive Dye (VSD) imaging is an optical imaging method that allows measuring the graded voltage changes of multiple neurons simultaneously. In neuroscience, this method is used to reveal networks of neurons involved in certain tasks. However, the recorded relative dye fluorescence changes are usually low and signals are superimposed by noise and artifacts. Therefore, establishing a reliable method to identify which cells are activated by specific stimulus conditions is the first step to identify functional networks. In this paper, we present a statistical method to identify stimulus-activated network nodes as cells, whose activities during sensory network stimulation differ significantly from the un-stimulated control condition. This method is demonstrated based on voltage-sensitive dye recordings from up to 100 neurons in a ganglion of the medicinal leech responding to tactile skin stimulation. Without relying on any prior physiological knowledge, the network nodes identified by our statistical analysis were found to match well with published cell types involved in tactile stimulus processing and to be consistent across stimulus conditions and preparations.

Observations of thunderstorm-related 630 nm airglow depletions

NASA Astrophysics Data System (ADS)

Kendall, E. A.; Bhatt, A.

2015-12-01

The Midlatitude All-sky imaging Network for Geophysical Observations (MANGO) is an NSF-funded network of 630 nm all-sky imagers in the continental United States. MANGO will be used to observe the generation, propagation, and dissipation of medium and large-scale wave activity in the subauroral, mid and low-latitude thermosphere. This network is actively being deployed and will ultimately consist of nine all-sky imagers. These imagers form a network providing continuous coverage over the western United States, including California, Oregon, Washington, Utah, Arizona and Texas extending south into Mexico. This network sees high levels of both medium and large scale wave activity. Apart from the widely reported northeast to southwest propagating wave fronts resulting from the so called Perkins mechanism, this network observes wave fronts propagating to the west, north and northeast. At least three of these anomalous events have been associated with thunderstorm activity. Imager data has been correlated with both GPS data and data from the AIRS (Atmospheric Infrared Sounder) instrument on board NASA's Earth Observing System Aqua satellite. We will present a comprehensive analysis of these events and discuss the potential thunderstorm source mechanism.

Aging-related changes in the default mode network and its anti-correlated networks: a resting-state fMRI study.

PubMed

Wu, Jing-Tao; Wu, Hui-Zhen; Yan, Chao-Gan; Chen, Wen-Xin; Zhang, Hong-Ying; He, Yong; Yang, Hai-Shan

2011-10-17

Intrinsic brain activity in a resting state incorporates components of the task negative network called default mode network (DMN) and task-positive networks called attentional networks. In the present study, the reciprocal neuronal networks in the elder group were compared with the young group to investigate the differences of the intrinsic brain activity using a method of temporal correlation analysis based on seed regions of posterior cingulate cortex (PCC) and ventromedial prefrontal cortex (vmPFC). We found significant decreased positive correlations and negative correlations with the seeds of PCC and vmPFC in the old group. The decreased coactivations in the DMN network components and their negative networks in the old group may reflect age-related alterations in various brain functions such as attention, motor control and inhibition modulation in cognitive processing. These alterations in the resting state anti-correlative networks could provide neuronal substrates for the aging brain. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Multi-level characterization of balanced inhibitory-excitatory cortical neuron network derived from human pluripotent stem cells.

PubMed

Nadadhur, Aishwarya G; Emperador Melero, Javier; Meijer, Marieke; Schut, Desiree; Jacobs, Gerbren; Li, Ka Wan; Hjorth, J J Johannes; Meredith, Rhiannon M; Toonen, Ruud F; Van Kesteren, Ronald E; Smit, August B; Verhage, Matthijs; Heine, Vivi M

2017-01-01

Generation of neuronal cultures from induced pluripotent stem cells (hiPSCs) serve the studies of human brain disorders. However we lack neuronal networks with balanced excitatory-inhibitory activities, which are suitable for single cell analysis. We generated low-density networks of hPSC-derived GABAergic and glutamatergic cortical neurons. We used two different co-culture models with astrocytes. We show that these cultures have balanced excitatory-inhibitory synaptic identities using confocal microscopy, electrophysiological recordings, calcium imaging and mRNA analysis. These simple and robust protocols offer the opportunity for single-cell to multi-level analysis of patient hiPSC-derived cortical excitatory-inhibitory networks; thereby creating advanced tools to study disease mechanisms underlying neurodevelopmental disorders.

On the degelation of networks – Case of the radiochemical degradation of methyl methacrylate – ethylene glycol dimethacrylate copolymers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Richaud, Emmanuel; Gilormini, Pierre; Verdu, Jacques

2016-05-18

Methyl methacrylate networks were synthetized and submitted to radiochemical degradation. Ageing was monitored by means of sol-gel analysis and glass transition temperature measurements. Networks were shown to undergo exclusively chain scission process leading to the degelation of network. The critical conversion degree corresponding to degelation (loss of all elastically active chains) is discussed regarding a statistical theory.

Estimating the Importance of Terrorists in a Terror Network

NASA Astrophysics Data System (ADS)

Elhajj, Ahmed; Elsheikh, Abdallah; Addam, Omar; Alzohbi, Mohamad; Zarour, Omar; Aksaç, Alper; Öztürk, Orkun; Özyer, Tansel; Ridley, Mick; Alhajj, Reda

While criminals may start their activities at individual level, the same is in general not true for terrorists who are mostly organized in well established networks. The effectiveness of a terror network could be realized by watching many factors, including the volume of activities accomplished by its members, the capabilities of its members to hide, and the ability of the network to grow and to maintain its influence even after the loss of some members, even leaders. Social network analysis, data mining and machine learning techniques could play important role in measuring the effectiveness of a network in general and in particular a terror network in support of the work presented in this chapter. We present a framework that employs clustering, frequent pattern mining and some social network analysis measures to determine the effectiveness of a network. The clustering and frequent pattern mining techniques start with the adjacency matrix of the network. For clustering, we utilize entries in the table by considering each row as an object and each column as a feature. Thus features of a network member are his/her direct neighbors. We maintain the weight of links in case of weighted network links. For frequent pattern mining, we consider each row of the adjacency matrix as a transaction and each column as an item. Further, we map entries into a 0/1 scale such that every entry whose value is greater than zero is assigned the value one; entries keep the value zero otherwise. This way we can apply frequent pattern mining algorithms to determine the most influential members in a network as well as the effect of removing some members or even links between members of a network. We also investigate the effect of adding some links between members. The target is to study how the various members in the network change role as the network evolves. This is measured by applying some social network analysis measures on the network at each stage during the development. We report some interesting results related to two benchmark networks: the first is 9/11 and the second is Madrid bombing.

How Did the Information Flow in the #AlphaGo Hashtag Network? A Social Network Analysis of the Large-Scale Information Network on Twitter.

PubMed

Kim, Jinyoung

2017-12-01

As it becomes common for Internet users to use hashtags when posting and searching information on social media, it is important to understand who builds a hashtag network and how information is circulated within the network. This article focused on unlocking the potential of the #AlphaGo hashtag network by addressing the following questions. First, the current study examined whether traditional opinion leadership (i.e., the influentials hypothesis) or grassroot participation by the public (i.e., the interpersonal hypothesis) drove dissemination of information in the hashtag network. Second, several unique patterns of information distribution by key users were identified. Finally, the association between attributes of key users who exerted great influence on information distribution (i.e., the number of followers and follows) and their central status in the network was tested. To answer the proffered research questions, a social network analysis was conducted using a large-scale hashtag network data set from Twitter (n = 21,870). The results showed that the leading actors in the network were actively receiving information from their followers rather than serving as intermediaries between the original information sources and the public. Moreover, the leading actors played several roles (i.e., conversation starters, influencers, and active engagers) in the network. Furthermore, the number of their follows and followers were significantly associated with their central status in the hashtag network. Based on the results, the current research explained how the information was exchanged in the hashtag network by proposing the reciprocal model of information flow.

Pattern Formation on Networks: from Localised Activity to Turing Patterns

PubMed Central

McCullen, Nick; Wagenknecht, Thomas

2016-01-01

Networks of interactions between competing species are used to model many complex systems, such as in genetics, evolutionary biology or sociology and knowledge of the patterns of activity they can exhibit is important for understanding their behaviour. The emergence of patterns on complex networks with reaction-diffusion dynamics is studied here, where node dynamics interact via diffusion via the network edges. Through the application of a generalisation of dynamical systems analysis this work reveals a fundamental connection between small-scale modes of activity on networks and localised pattern formation seen throughout science, such as solitons, breathers and localised buckling. The connection between solutions with a single and small numbers of activated nodes and the fully developed system-scale patterns are investigated computationally using numerical continuation methods. These techniques are also used to help reveal a much larger portion of of the full number of solutions that exist in the system at different parameter values. The importance of network structure is also highlighted, with a key role being played by nodes with a certain so-called optimal degree, on which the interaction between the reaction kinetics and the network structure organise the behaviour of the system. PMID:27273339

Fuzzy Entropy Method for Quantifying Supply Chain Networks Complexity

NASA Astrophysics Data System (ADS)

Zhang, Jihui; Xu, Junqin

Supply chain is a special kind of complex network. Its complexity and uncertainty makes it very difficult to control and manage. Supply chains are faced with a rising complexity of products, structures, and processes. Because of the strong link between a supply chain’s complexity and its efficiency the supply chain complexity management becomes a major challenge of today’s business management. The aim of this paper is to quantify the complexity and organization level of an industrial network working towards the development of a ‘Supply Chain Network Analysis’ (SCNA). By measuring flows of goods and interaction costs between different sectors of activity within the supply chain borders, a network of flows is built and successively investigated by network analysis. The result of this study shows that our approach can provide an interesting conceptual perspective in which the modern supply network can be framed, and that network analysis can handle these issues in practice.

MSEE: Stochastic Cognitive Linguistic Behavior Models for Semantic Sensing

DTIC Science & Technology

2013-09-01

recognition, a Gaussian Process Dynamic Model with Social Network Analysis (GPDM-SNA) for a small human group action recognition, an extended GPDM-SNA...44  3.2. Small Human Group Activity Modeling Based on Gaussian Process Dynamic Model and Social Network Analysis (SN-GPDM...51  Approved for public release; distribution unlimited. 3 3.2.3. Gaussian Process Dynamical Model and

Ideological Think Tanks in the States: An Inventory of Their Prevalence, Networks, and Higher Education Policy Activity

ERIC Educational Resources Information Center

Ness, Erik C.; Gándara, Denisa

2014-01-01

This study takes an inventory of a particular type of intermediary organization ascendant within the state-level higher education policy: ideological think tanks. Our inventory identifies 99 think tanks: 59 affiliated with the conservative State Policy Network and 40 with the Progressive States Network. The analysis shows that state-level…

Challenge Paper: Validation of Forensic Techniques for Criminal Prosecution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Erbacher, Robert F.; Endicott-Popovsky, Barbara E.; Frincke, Deborah A.

2007-04-10

Abstract: As in many domains, there is increasing agreement in the user and research community that digital forensics analysts would benefit from the extension, development and application of advanced techniques in performing large scale and heterogeneous data analysis. Modern digital forensics analysis of cyber-crimes and cyber-enabled crimes often requires scrutiny of massive amounts of data. For example, a case involving network compromise across multiple enterprises might require forensic analysis of numerous sets of network logs and computer hard drives, potentially involving 100?s of gigabytes of heterogeneous data, or even terabytes or petabytes of data. Also, the goal for forensic analysismore » is to not only determine whether the illicit activity being considered is taking place, but also to identify the source of the activity and the full extent of the compromise or impact on the local network. Even after this analysis, there remains the challenge of using the results in subsequent criminal and civil processes.« less

Computational Modeling of Allosteric Regulation in the Hsp90 Chaperones: A Statistical Ensemble Analysis of Protein Structure Networks and Allosteric Communications

PubMed Central

Blacklock, Kristin; Verkhivker, Gennady M.

2014-01-01

A fundamental role of the Hsp90 chaperone in regulating functional activity of diverse protein clients is essential for the integrity of signaling networks. In this work we have combined biophysical simulations of the Hsp90 crystal structures with the protein structure network analysis to characterize the statistical ensemble of allosteric interaction networks and communication pathways in the Hsp90 chaperones. We have found that principal structurally stable communities could be preserved during dynamic changes in the conformational ensemble. The dominant contribution of the inter-domain rigidity to the interaction networks has emerged as a common factor responsible for the thermodynamic stability of the active chaperone form during the ATPase cycle. Structural stability analysis using force constant profiling of the inter-residue fluctuation distances has identified a network of conserved structurally rigid residues that could serve as global mediating sites of allosteric communication. Mapping of the conformational landscape with the network centrality parameters has demonstrated that stable communities and mediating residues may act concertedly with the shifts in the conformational equilibrium and could describe the majority of functionally significant chaperone residues. The network analysis has revealed a relationship between structural stability, global centrality and functional significance of hotspot residues involved in chaperone regulation. We have found that allosteric interactions in the Hsp90 chaperone may be mediated by modules of structurally stable residues that display high betweenness in the global interaction network. The results of this study have suggested that allosteric interactions in the Hsp90 chaperone may operate via a mechanism that combines rapid and efficient communication by a single optimal pathway of structurally rigid residues and more robust signal transmission using an ensemble of suboptimal multiple communication routes. This may be a universal requirement encoded in protein structures to balance the inherent tension between resilience and efficiency of the residue interaction networks. PMID:24922508

Computational modeling of allosteric regulation in the hsp90 chaperones: a statistical ensemble analysis of protein structure networks and allosteric communications.

PubMed

Blacklock, Kristin; Verkhivker, Gennady M

2014-06-01

A fundamental role of the Hsp90 chaperone in regulating functional activity of diverse protein clients is essential for the integrity of signaling networks. In this work we have combined biophysical simulations of the Hsp90 crystal structures with the protein structure network analysis to characterize the statistical ensemble of allosteric interaction networks and communication pathways in the Hsp90 chaperones. We have found that principal structurally stable communities could be preserved during dynamic changes in the conformational ensemble. The dominant contribution of the inter-domain rigidity to the interaction networks has emerged as a common factor responsible for the thermodynamic stability of the active chaperone form during the ATPase cycle. Structural stability analysis using force constant profiling of the inter-residue fluctuation distances has identified a network of conserved structurally rigid residues that could serve as global mediating sites of allosteric communication. Mapping of the conformational landscape with the network centrality parameters has demonstrated that stable communities and mediating residues may act concertedly with the shifts in the conformational equilibrium and could describe the majority of functionally significant chaperone residues. The network analysis has revealed a relationship between structural stability, global centrality and functional significance of hotspot residues involved in chaperone regulation. We have found that allosteric interactions in the Hsp90 chaperone may be mediated by modules of structurally stable residues that display high betweenness in the global interaction network. The results of this study have suggested that allosteric interactions in the Hsp90 chaperone may operate via a mechanism that combines rapid and efficient communication by a single optimal pathway of structurally rigid residues and more robust signal transmission using an ensemble of suboptimal multiple communication routes. This may be a universal requirement encoded in protein structures to balance the inherent tension between resilience and efficiency of the residue interaction networks.

Analysing human mobility patterns of hiking activities through complex network theory.

PubMed

Lera, Isaac; Pérez, Toni; Guerrero, Carlos; Eguíluz, Víctor M; Juiz, Carlos

2017-01-01

The exploitation of high volume of geolocalized data from social sport tracking applications of outdoor activities can be useful for natural resource planning and to understand the human mobility patterns during leisure activities. This geolocalized data represents the selection of hike activities according to subjective and objective factors such as personal goals, personal abilities, trail conditions or weather conditions. In our approach, human mobility patterns are analysed from trajectories which are generated by hikers. We propose the generation of the trail network identifying special points in the overlap of trajectories. Trail crossings and trailheads define our network and shape topological features. We analyse the trail network of Balearic Islands, as a case of study, using complex weighted network theory. The analysis is divided into the four seasons of the year to observe the impact of weather conditions on the network topology. The number of visited places does not decrease despite the large difference in the number of samples of the two seasons with larger and lower activity. It is in summer season where it is produced the most significant variation in the frequency and localization of activities from inland regions to coastal areas. Finally, we compare our model with other related studies where the network possesses a different purpose. One finding of our approach is the detection of regions with relevant importance where landscape interventions can be applied in function of the communities.

Analysing human mobility patterns of hiking activities through complex network theory

PubMed Central

Pérez, Toni; Guerrero, Carlos; Eguíluz, Víctor M.; Juiz, Carlos

2017-01-01

The exploitation of high volume of geolocalized data from social sport tracking applications of outdoor activities can be useful for natural resource planning and to understand the human mobility patterns during leisure activities. This geolocalized data represents the selection of hike activities according to subjective and objective factors such as personal goals, personal abilities, trail conditions or weather conditions. In our approach, human mobility patterns are analysed from trajectories which are generated by hikers. We propose the generation of the trail network identifying special points in the overlap of trajectories. Trail crossings and trailheads define our network and shape topological features. We analyse the trail network of Balearic Islands, as a case of study, using complex weighted network theory. The analysis is divided into the four seasons of the year to observe the impact of weather conditions on the network topology. The number of visited places does not decrease despite the large difference in the number of samples of the two seasons with larger and lower activity. It is in summer season where it is produced the most significant variation in the frequency and localization of activities from inland regions to coastal areas. Finally, we compare our model with other related studies where the network possesses a different purpose. One finding of our approach is the detection of regions with relevant importance where landscape interventions can be applied in function of the communities. PMID:28542280

Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach

PubMed Central

Yu, Zhaoyuan; Yuan, Linwang; Luo, Wen; Feng, Linyao; Lv, Guonian

2015-01-01

Passive infrared (PIR) motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA)-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks. PMID:26729123

Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach.

PubMed

Yu, Zhaoyuan; Yuan, Linwang; Luo, Wen; Feng, Linyao; Lv, Guonian

2015-12-30

Passive infrared (PIR) motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA)-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks.

Associations between Aspects of Friendship Networks, Physical Activity, and Sedentary Behaviour among Adolescents

PubMed Central

McCormack, Gavin R.; Nettel-Aguirre, Alberto; Blackstaffe, Anita; Perry, Rosemary; Hawe, Penelope

2014-01-01

Background. Adolescent friendships have been linked to physical activity levels; however, network characteristics have not been broadly examined. Method. In a cross-sectional analysis of 1061 adolescents (11–15 years), achieving 60 minutes/day of moderate-to-vigorous physical activity (MVPA) and participating in over 2 hours/day of sedentary behaviour were determined based on friendship network characteristics (density; proportion of active/sedentary friends; betweenness centrality; popularity; clique membership) and perceived social support. Results. Adolescents with no friendship nominations participated in less MVPA. For boys and girls, a ten percent point increase in active friends was positively associated with achievement of 60 minutes/day of MVPA (OR 1.11; 95% CI 1.02–1.21, OR 1.14; 95% CI 1.02–1.27, resp.). For boys, higher social support from friends was negatively associated with achieving 60 minutes/day of MVPA (OR 0.63; 95% CI 0.42–0.96). Compared with low density networks, boys in higher density networks were more likely to participate in over 2 hours/day of sedentary behaviour (OR 2.93; 95% CI 1.32–6.49). Social support from friends also modified associations between network characteristics and MVPA and sedentary behaviour. Conclusion. Different network characteristics appeared to have different consequences. The proportion of active close friends was associated with MVPA, while network density was associated with sedentary behaviour. This poses challenges for intervention design. PMID:25328690

Role of Ongoing, Intrinsic Activity of Neuronal Populations for Quantitative Neuroimaging of Functional Magnetic Resonance Imaging–Based Networks

PubMed Central

Herman, Peter; Sanganahalli, Basavaraju G.; Coman, Daniel; Blumenfeld, Hal; Rothman, Douglas L.

2011-01-01

Abstract A primary objective in neuroscience is to determine how neuronal populations process information within networks. In humans and animal models, functional magnetic resonance imaging (fMRI) is gaining increasing popularity for network mapping. Although neuroimaging with fMRI—conducted with or without tasks—is actively discovering new brain networks, current fMRI data analysis schemes disregard the importance of the total neuronal activity in a region. In task fMRI experiments, the baseline is differenced away to disclose areas of small evoked changes in the blood oxygenation level-dependent (BOLD) signal. In resting-state fMRI experiments, the spotlight is on regions revealed by correlations of tiny fluctuations in the baseline (or spontaneous) BOLD signal. Interpretation of fMRI-based networks is obscured further, because the BOLD signal indirectly reflects neuronal activity, and difference/correlation maps are thresholded. Since the small changes of BOLD signal typically observed in cognitive fMRI experiments represent a minimal fraction of the total energy/activity in a given area, the relevance of fMRI-based networks is uncertain, because the majority of neuronal energy/activity is ignored. Thus, another alternative for quantitative neuroimaging of fMRI-based networks is a perspective in which the activity of a neuronal population is accounted for by the demanded oxidative energy (CMRO2). In this article, we argue that network mapping can be improved by including neuronal energy/activity of both the information about baseline and small differences/fluctuations of BOLD signal. Thus, total energy/activity information can be obtained through use of calibrated fMRI to quantify differences of ΔCMRO2 and through resting-state positron emission tomography/magnetic resonance spectroscopy measurements for average CMRO2. PMID:22433047

An Optimal CDS Construction Algorithm with Activity Scheduling in Ad Hoc Networks

PubMed Central

Penumalli, Chakradhar; Palanichamy, Yogesh

2015-01-01

A new energy efficient optimal Connected Dominating Set (CDS) algorithm with activity scheduling for mobile ad hoc networks (MANETs) is proposed. This algorithm achieves energy efficiency by minimizing the Broadcast Storm Problem [BSP] and at the same time considering the node's remaining energy. The Connected Dominating Set is widely used as a virtual backbone or spine in mobile ad hoc networks [MANETs] or Wireless Sensor Networks [WSN]. The CDS of a graph representing a network has a significant impact on an efficient design of routing protocol in wireless networks. Here the CDS is a distributed algorithm with activity scheduling based on unit disk graph [UDG]. The node's mobility and residual energy (RE) are considered as parameters in the construction of stable optimal energy efficient CDS. The performance is evaluated at various node densities, various transmission ranges, and mobility rates. The theoretical analysis and simulation results of this algorithm are also presented which yield better results. PMID:26221627

Classification of functional interactions from multi-electrodes data using conditional modularity analysis

NASA Astrophysics Data System (ADS)

Makhtar, Siti Noormiza; Senik, Mohd Harizal

2018-02-01

The availability of massive amount of neuronal signals are attracting widespread interest in functional connectivity analysis. Functional interactions estimated by multivariate partial coherence analysis in the frequency domain represent the connectivity strength in this study. Modularity is a network measure for the detection of community structure in network analysis. The discovery of community structure for the functional neuronal network was implemented on multi-electrode array (MEA) signals recorded from hippocampal regions in isoflurane-anaesthetized Lister-hooded rats. The analysis is expected to show modularity changes before and after local unilateral kainic acid (KA)-induced epileptiform activity. The result is presented using color-coded graphic of conditional modularity measure for 19 MEA nodes. This network is separated into four sub-regions to show the community detection within each sub-region. The results show that classification of neuronal signals into the inter- and intra-modular nodes is feasible using conditional modularity analysis. Estimation of segregation properties using conditional modularity analysis may provide further information about functional connectivity from MEA data.

Distinctive time-lagged resting-state networks revealed by simultaneous EEG-fMRI.

PubMed

Feige, Bernd; Spiegelhalder, Kai; Kiemen, Andrea; Bosch, Oliver G; Tebartz van Elst, Ludger; Hennig, Jürgen; Seifritz, Erich; Riemann, Dieter

2017-01-15

Functional activation as evidenced by blood oxygen level-dependent (BOLD) functional MRI changes or event-related EEG is known to closely follow patterns of stimulation or self-paced action. Any lags are compatible with axonal conduction velocities and neural integration times. The important analysis of resting state networks is generally based on the assumption that these principles also hold for spontaneous fluctuations in brain activity. Previous observations using simultaneous EEG and fMRI indicate that slower processes, with delays in the seconds range, determine at least part of the relationship between spontaneous EEG and fMRI. To assess this relationship systematically, we used deconvolution analysis of EEG-fMRI during the resting state, assessing the relationship between EEG frequency bands and fMRI BOLD across the whole brain while allowing for time lags of up to 10.5s. Cluster analysis, identifying similar BOLD time courses in relation to EEG band power peaks, showed a clear segregation of functional subsystems of the brain. Our analysis shows that fMRI BOLD increases commonly precede EEG power increases by seconds. Most zero-lag correlations, on the other hand, were negative. This indicates two main distinct neuromodulatory mechanisms: an "idling" mechanism of simultaneous electric and metabolic network anticorrelation and a "regulatory" mechanism in which metabolic network activity precedes increased EEG power by some seconds. This has to be taken into consideration in further studies which address the causal and functional relationship of metabolic and electric brain activity patterns. Copyright © 2016 Elsevier Inc. All rights reserved.

Applying social network analysis to understand the knowledge sharing behaviour of practitioners in a clinical online discussion forum.

PubMed

Stewart, Samuel Alan; Abidi, Syed Sibte Raza

2012-12-04

Knowledge Translation (KT) plays a vital role in the modern health care community, facilitating the incorporation of new evidence into practice. Web 2.0 tools provide a useful mechanism for establishing an online KT environment in which health practitioners share their practice-related knowledge and experiences with an online community of practice. We have implemented a Web 2.0 based KT environment--an online discussion forum--for pediatric pain practitioners across seven different hospitals in Thailand. The online discussion forum enabled the pediatric pain practitioners to share and translate their experiential knowledge to help improve the management of pediatric pain in hospitals. The goal of this research is to investigate the knowledge sharing dynamics of a community of practice through an online discussion forum. We evaluated the communication patterns of the community members using statistical and social network analysis methods in order to better understand how the online community engages to share experiential knowledge. Statistical analyses and visualizations provide a broad overview of the communication patterns within the discussion forum. Social network analysis provides the tools to delve deeper into the social network, identifying the most active members of the community, reporting the overall health of the social network, isolating the potential core members of the social network, and exploring the inter-group relationships that exist across institutions and professions. The statistical analyses revealed a network dominated by a single institution and a single profession, and found a varied relationship between reading and posting content to the discussion forum. The social network analysis discovered a healthy network with strong communication patterns, while identifying which users are at the center of the community in terms of facilitating communication. The group-level analysis suggests that there is strong interprofessional and interregional communication, but a dearth of non-nurse participants has been identified as a shortcoming. The results of the analysis suggest that the discussion forum is active and healthy, and that, though few, the interprofessional and interinstitutional ties are strong.

Comparison Analysis among Large Amount of SNS Sites

NASA Astrophysics Data System (ADS)

Toriumi, Fujio; Yamamoto, Hitoshi; Suwa, Hirohiko; Okada, Isamu; Izumi, Kiyoshi; Hashimoto, Yasuhiro

In recent years, application of Social Networking Services (SNS) and Blogs are growing as new communication tools on the Internet. Several large-scale SNS sites are prospering; meanwhile, many sites with relatively small scale are offering services. Such small-scale SNSs realize small-group isolated type of communication while neither mixi nor MySpace can do that. However, the studies on SNS are almost about particular large-scale SNSs and cannot analyze whether their results apply for general features or for special characteristics on the SNSs. From the point of view of comparison analysis on SNS, comparison with just several types of those cannot reach a statistically significant level. We analyze many SNS sites with the aim of classifying them by using some approaches. Our paper classifies 50,000 sites for small-scale SNSs and gives their features from the points of network structure, patterns of communication, and growth rate of SNS. The result of analysis for network structure shows that many SNS sites have small-world attribute with short path lengths and high coefficients of their cluster. Distribution of degrees of the SNS sites is close to power law. This result indicates the small-scale SNS sites raise the percentage of users with many friends than mixi. According to the analysis of their coefficients of assortativity, those SNS sites have negative values of assortativity, and that means users with high degree tend to connect users with small degree. Next, we analyze the patterns of user communication. A friend network of SNS is explicit while users' communication behaviors are defined as an implicit network. What kind of relationships do these networks have? To address this question, we obtain some characteristics of users' communication structure and activation patterns of users on the SNS sites. By using new indexes, friend aggregation rate and friend coverage rate, we show that SNS sites with high value of friend coverage rate activate diary postings and their comments. Besides, they become activated when hub users with high degree do not behave actively on the sites with high value of friend aggregation rate and high value of friend coverage rate. On the other hand, activation emerges when hub users behave actively on the sites with low value of friend aggregation rate and high value of friend coverage rate. Finally, we observe SNS sites which are increasing the number of users considerably, from the viewpoint of network structure, and extract characteristics of high growth SNS sites. As a result of discrimination on the basis of the decision tree analysis, we can recognize the high growth SNS sites with a high degree of accuracy. Besides, this approach suggests mixi and the other small-scale SNS sites have different character trait.

Short-term memory capacity in networks via the restricted isometry property.

PubMed

Charles, Adam S; Yap, Han Lun; Rozell, Christopher J

2014-06-01

Cortical networks are hypothesized to rely on transient network activity to support short-term memory (STM). In this letter, we study the capacity of randomly connected recurrent linear networks for performing STM when the input signals are approximately sparse in some basis. We leverage results from compressed sensing to provide rigorous nonasymptotic recovery guarantees, quantifying the impact of the input sparsity level, the input sparsity basis, and the network characteristics on the system capacity. Our analysis demonstrates that network memory capacities can scale superlinearly with the number of nodes and in some situations can achieve STM capacities that are much larger than the network size. We provide perfect recovery guarantees for finite sequences and recovery bounds for infinite sequences. The latter analysis predicts that network STM systems may have an optimal recovery length that balances errors due to omission and recall mistakes. Furthermore, we show that the conditions yielding optimal STM capacity can be embodied in several network topologies, including networks with sparse or dense connectivities.

New exponential synchronization criteria for time-varying delayed neural networks with discontinuous activations.

PubMed

Cai, Zuowei; Huang, Lihong; Zhang, Lingling

2015-05-01

This paper investigates the problem of exponential synchronization of time-varying delayed neural networks with discontinuous neuron activations. Under the extended Filippov differential inclusion framework, by designing discontinuous state-feedback controller and using some analytic techniques, new testable algebraic criteria are obtained to realize two different kinds of global exponential synchronization of the drive-response system. Moreover, we give the estimated rate of exponential synchronization which depends on the delays and system parameters. The obtained results extend some previous works on synchronization of delayed neural networks not only with continuous activations but also with discontinuous activations. Finally, numerical examples are provided to show the correctness of our analysis via computer simulations. Our method and theoretical results have a leading significance in the design of synchronized neural network circuits involving discontinuous factors and time-varying delays. Copyright © 2015 Elsevier Ltd. All rights reserved.

Adolescent Friendships, BMI, and Physical Activity: Untangling Selection and Influence Through Longitudinal Social Network Analysis

PubMed Central

Simpkins, Sandra D.; Schaefer, David R.; Price, Chara D.; Vest, Andrea E.

2012-01-01

Bioecological theory suggests that adolescents’ health is a result of selection and socialization processes occurring between adolescents and their microsettings. This study examines the association between adolescents’ friends and health using a social network model and data from the National Longitudinal Study of Adolescent Health (N = 1,896, mean age = 15.97 years). Results indicated evidence of friend influence on BMI and physical activity. Friendships were more likely among adolescents who engaged in greater physical activity and who were similar to one another in BMI and physical activity. These effects emerged after controlling for alternative friend selection factors, such as endogenous social network processes and propinquity through courses and activities. Some selection effects were moderated by gender, popularity, and reciprocity. PMID:24222971

Reorganization of functional brain networks mediates the improvement of cognitive performance following real-time neurofeedback training of working memory.

PubMed

Zhang, Gaoyan; Yao, Li; Shen, Jiahui; Yang, Yihong; Zhao, Xiaojie

2015-05-01

Working memory (WM) is essential for individuals' cognitive functions. Neuroimaging studies indicated that WM fundamentally relied on a frontoparietal working memory network (WMN) and a cinguloparietal default mode network (DMN). Behavioral training studies demonstrated that the two networks can be modulated by WM training. Different from the behavioral training, our recent study used a real-time functional MRI (rtfMRI)-based neurofeedback method to conduct WM training, demonstrating that WM performance can be significantly improved after successfully upregulating the activity of the target region of interest (ROI) in the left dorsolateral prefrontal cortex (Zhang et al., [2013]: PloS One 8:e73735); however, the neural substrate of rtfMRI-based WM training remains unclear. In this work, we assessed the intranetwork and internetwork connectivity changes of WMN and DMN during the training, and their correlations with the change of brain activity in the target ROI as well as with the improvement of post-training behavior. Our analysis revealed an "ROI-network-behavior" correlation relationship underlying the rtfMRI training. Further mediation analysis indicated that the reorganization of functional brain networks mediated the effect of self-regulation of the target brain activity on the improvement of cognitive performance following the neurofeedback training. The results of this study enhance our understanding of the neural basis of real-time neurofeedback and suggest a new direction to improve WM performance by regulating the functional connectivity in the WM related networks. © 2014 Wiley Periodicals, Inc.

Compensatory Motor Network Connectivity is Associated with Motor Sequence Learning after Subcortical Stroke

PubMed Central

Wadden, Katie P.; Woodward, Todd S.; Metzak, Paul D.; Lavigne, Katie M.; Lakhani, Bimal; Auriat, Angela M.; Boyd, Lara A.

2015-01-01

Following stroke, functional networks reorganize and the brain demonstrates widespread alterations in cortical activity. Implicit motor learning is preserved after stroke. However the manner in which brain reorganization occurs, and how it supports behaviour within the damaged brain remains unclear. In this functional magnetic resonance imaging (fMRI) study, we evaluated whole brain patterns of functional connectivity during the performance of an implicit tracking task at baseline and retention, following 5 days of practice. Following motor practice, a significant difference in connectivity within a motor network, consisting of bihemispheric activation of the sensory and motor cortices, parietal lobules, cerebellar and occipital lobules, was observed at retention. Healthy subjects demonstrated greater activity within this motor network during sequence learning compared to random practice. The stroke group did not show the same level of functional network integration, presumably due to the heterogeneity of functional reorganization following stroke. In a secondary analysis, a binary mask of the functional network activated from the aforementioned whole brain analyses was created to assess within-network connectivity, decreasing the spatial distribution and large variability of activation that exists within the lesioned brain. The stroke group demonstrated reduced clusters of connectivity within the masked brain regions as compared to the whole brain approach. Connectivity within this smaller motor network correlated with repeated sequence performance on the retention test. Increased functional integration within the motor network may be an important neurophysiological predictor of motor learning-related change in individuals with stroke. PMID:25757996

Resting-State Network Topology Differentiates Task Signals across the Adult Life Span.

PubMed

Chan, Micaela Y; Alhazmi, Fahd H; Park, Denise C; Savalia, Neil K; Wig, Gagan S

2017-03-08

Brain network connectivity differs across individuals. For example, older adults exhibit less segregated resting-state subnetworks relative to younger adults (Chan et al., 2014). It has been hypothesized that individual differences in network connectivity impact the recruitment of brain areas during task execution. While recent studies have described the spatial overlap between resting-state functional correlation (RSFC) subnetworks and task-evoked activity, it is unclear whether individual variations in the connectivity pattern of a brain area (topology) relates to its activity during task execution. We report data from 238 cognitively normal participants (humans), sampled across the adult life span (20-89 years), to reveal that RSFC-based network organization systematically relates to the recruitment of brain areas across two functionally distinct tasks (visual and semantic). The functional activity of brain areas (network nodes) were characterized according to their patterns of RSFC: nodes with relatively greater connections to nodes in their own functional system ("non-connector" nodes) exhibited greater activity than nodes with relatively greater connections to nodes in other systems ("connector" nodes). This "activation selectivity" was specific to those brain systems that were central to each of the tasks. Increasing age was accompanied by less differentiated network topology and a corresponding reduction in activation selectivity (or differentiation) across relevant network nodes. The results provide evidence that connectional topology of brain areas quantified at rest relates to the functional activity of those areas during task. Based on these findings, we propose a novel network-based theory for previous reports of the "dedifferentiation" in brain activity observed in aging. SIGNIFICANCE STATEMENT Similar to other real-world networks, the organization of brain networks impacts their function. As brain network connectivity patterns differ across individuals, we hypothesized that individual differences in network connectivity would relate to differences in brain activity. Using functional MRI in a group of individuals sampled across the adult life span (20-89 years), we measured correlations at rest and related the functional connectivity patterns to measurements of functional activity during two independent tasks. Brain activity varied in relation to connectivity patterns revealed by large-scale network analysis. This relationship tracked the differences in connectivity patterns accompanied by older age, providing important evidence for a link between the topology of areal connectivity measured at rest and the functional recruitment of these areas during task performance. Copyright © 2017 Chan et al.

A Qualitative Study to Examine Feasibility and Design of an Online Social Networking Intervention to Increase Physical Activity in Teenage Girls.

PubMed

Van Kessel, Gisela; Kavanagh, Madeleine; Maher, Carol

2016-01-01

Online social networks present wide-reaching and flexible platforms through which to deliver health interventions to targeted populations. This study used a social marketing approach to explore teenage girls' perceptions of physical activity and the potential use of online social networks to receive a physical activity intervention. Six focus groups were conducted with 19 Australian teenage girls (ages 13 to 18 years) with varying levels of physical activity and socioeconomic status. A semi-structured format was used, with groups discussion transcribed verbatim. Content analysis identified emergent themes, with triangulation and memos used to ensure accuracy. Physical activity was most appealing when it emphasised sport, exercise and fitness, along with opportunities for socialisation with friends and self-improvement. Participants were receptive to delivery of a physical activity intervention via online social networks, with Facebook the most widely reported site. Participants commonly accessed online social networks via mobile devices and particularly smartphones. Undesirable features included promotion of physical activity in terms of walking; use of cartoon imagery; use of humour; and promotion of the intervention via schools, each of which were considered "uncool". Participants noted that their parents were likely to be supportive of them using an online social networking physical activity intervention, particularly if not promoted as a weight loss intervention. This study identified key features likely to increase the feasibility and retention of an online social networking physical activity intervention for teenage girls. Guidelines for the design of interventions for teenage girls are provided for future applications.

A Qualitative Study to Examine Feasibility and Design of an Online Social Networking Intervention to Increase Physical Activity in Teenage Girls

PubMed Central

Van Kessel, Gisela; Kavanagh, Madeleine; Maher, Carol

2016-01-01

Background Online social networks present wide-reaching and flexible platforms through which to deliver health interventions to targeted populations. This study used a social marketing approach to explore teenage girls’ perceptions of physical activity and the potential use of online social networks to receive a physical activity intervention. Methods Six focus groups were conducted with 19 Australian teenage girls (ages 13 to 18 years) with varying levels of physical activity and socioeconomic status. A semi-structured format was used, with groups discussion transcribed verbatim. Content analysis identified emergent themes, with triangulation and memos used to ensure accuracy. Results Physical activity was most appealing when it emphasised sport, exercise and fitness, along with opportunities for socialisation with friends and self-improvement. Participants were receptive to delivery of a physical activity intervention via online social networks, with Facebook the most widely reported site. Participants commonly accessed online social networks via mobile devices and particularly smartphones. Undesirable features included promotion of physical activity in terms of walking; use of cartoon imagery; use of humour; and promotion of the intervention via schools, each of which were considered “uncool”. Participants noted that their parents were likely to be supportive of them using an online social networking physical activity intervention, particularly if not promoted as a weight loss intervention. Conclusion This study identified key features likely to increase the feasibility and retention of an online social networking physical activity intervention for teenage girls. Guidelines for the design of interventions for teenage girls are provided for future applications. PMID:26934191

PLUS highway network analysis: Case of in-coming traffic burden in 2013

NASA Astrophysics Data System (ADS)

Asrah, Norhaidah Mohd; Djauhari, Maman Abdurachman; Mohamad, Ismail

2017-05-01

PLUS highway is the largest concessionary in Malaysia. The study on PLUS highway development, in order to overcome the demand for efficient road transportation, is crucial. If the highways have better interconnected network, it will help the economic activities such as trade to increase. If economic activities are increasing, the benefit will come to the people and state. In its turn, it will help the leaders to plan and conduct national development program. In this paper, network analysis approach will be used to study the in-coming traffic burden during the year of 2013. The highway network linking all the toll plazas is a dynamic network. The objective of this study is to learn and understand about highway network in terms of the in-coming traffic burden entering to each toll plazas along PLUS highway. For this purpose, the filtered network topology based on the forest of all possible minimum spanning trees is used. The in-coming traffic burden of a city is represented by the number of cars passing through the corresponding toll plaza. To interpret the filtered network, centrality measures such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality are used. An overall centrality will be proposed if those four measures are assumed to have the same role. Based on the results, some suggestions and recommendations for PLUS highway network development will be delivered to PLUS highway management.

The signaling petri net-based simulator: a non-parametric strategy for characterizing the dynamics of cell-specific signaling networks.

PubMed

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T

2008-02-29

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations.

The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

PubMed Central

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T.

2008-01-01

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations. PMID:18463702

A transcriptional dynamic network during Arabidopsis thaliana pollen development.

PubMed

Wang, Jigang; Qiu, Xiaojie; Li, Yuhua; Deng, Youping; Shi, Tieliu

2011-01-01

To understand transcriptional regulatory networks (TRNs), especially the coordinated dynamic regulation between transcription factors (TFs) and their corresponding target genes during development, computational approaches would represent significant advances in the genome-wide expression analysis. The major challenges for the experiments include monitoring the time-specific TFs' activities and identifying the dynamic regulatory relationships between TFs and their target genes, both of which are currently not yet available at the large scale. However, various methods have been proposed to computationally estimate those activities and regulations. During the past decade, significant progresses have been made towards understanding pollen development at each development stage under the molecular level, yet the regulatory mechanisms that control the dynamic pollen development processes remain largely unknown. Here, we adopt Networks Component Analysis (NCA) to identify TF activities over time course, and infer their regulatory relationships based on the coexpression of TFs and their target genes during pollen development. We carried out meta-analysis by integrating several sets of gene expression data related to Arabidopsis thaliana pollen development (stages range from UNM, BCP, TCP, HP to 0.5 hr pollen tube and 4 hr pollen tube). We constructed a regulatory network, including 19 TFs, 101 target genes and 319 regulatory interactions. The computationally estimated TF activities were well correlated to their coordinated genes' expressions during the development process. We clustered the expression of their target genes in the context of regulatory influences, and inferred new regulatory relationships between those TFs and their target genes, such as transcription factor WRKY34, which was identified that specifically expressed in pollen, and regulated several new target genes. Our finding facilitates the interpretation of the expression patterns with more biological relevancy, since the clusters corresponding to the activity of specific TF or the combination of TFs suggest the coordinated regulation of TFs to their target genes. Through integrating different resources, we constructed a dynamic regulatory network of Arabidopsis thaliana during pollen development with gene coexpression and NCA. The network illustrated the relationships between the TFs' activities and their target genes' expression, as well as the interactions between TFs, which provide new insight into the molecular mechanisms that control the pollen development.

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development.

PubMed

Ozerov, Ivan V; Lezhnina, Ksenia V; Izumchenko, Evgeny; Artemov, Artem V; Medintsev, Sergey; Vanhaelen, Quentin; Aliper, Alexander; Vijg, Jan; Osipov, Andreyan N; Labat, Ivan; West, Michael D; Buzdin, Anton; Cantor, Charles R; Nikolsky, Yuri; Borisov, Nikolay; Irincheeva, Irina; Khokhlovich, Edward; Sidransky, David; Camargo, Miguel Luiz; Zhavoronkov, Alex

2016-11-16

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy.

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development

PubMed Central

Ozerov, Ivan V.; Lezhnina, Ksenia V.; Izumchenko, Evgeny; Artemov, Artem V.; Medintsev, Sergey; Vanhaelen, Quentin; Aliper, Alexander; Vijg, Jan; Osipov, Andreyan N.; Labat, Ivan; West, Michael D.; Buzdin, Anton; Cantor, Charles R.; Nikolsky, Yuri; Borisov, Nikolay; Irincheeva, Irina; Khokhlovich, Edward; Sidransky, David; Camargo, Miguel Luiz; Zhavoronkov, Alex

2016-01-01

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy. PMID:27848968

Molecular dynamics simulations and modelling of the residue interaction networks in the BRAF kinase complexes with small molecule inhibitors: probing the allosteric effects of ligand-induced kinase dimerization and paradoxical activation.

PubMed

Verkhivker, G M

2016-10-20

Protein kinases are central to proper functioning of cellular networks and are an integral part of many signal transduction pathways. The family of protein kinases represents by far the largest and most important class of therapeutic targets in oncology. Dimerization-induced activation has emerged as a common mechanism of allosteric regulation in BRAF kinases, which play an important role in growth factor signalling and human diseases. Recent studies have revealed that most of the BRAF inhibitors can induce dimerization and paradoxically stimulate enzyme transactivation by conferring an active conformation in the second monomer of the kinase dimer. The emerging connections between inhibitor binding and BRAF kinase domain dimerization have suggested a molecular basis of the activation mechanism in which BRAF inhibitors may allosterically modulate the stability of the dimerization interface and affect the organization of residue interaction networks in BRAF kinase dimers. In this work, we integrated structural bioinformatics analysis, molecular dynamics and binding free energy simulations with the protein structure network analysis of the BRAF crystal structures to determine dynamic signatures of BRAF conformations in complexes with different types of inhibitors and probe the mechanisms of the inhibitor-induced dimerization and paradoxical activation. The results of this study highlight previously unexplored relationships between types of BRAF inhibitors, inhibitor-induced changes in the residue interaction networks and allosteric modulation of the kinase activity. This study suggests a mechanism by which BRAF inhibitors could promote or interfere with the paradoxical activation of BRAF kinases, which may be useful in informing discovery efforts to minimize the unanticipated adverse biological consequences of these therapeutic agents.

Complex network analysis of brain functional connectivity under a multi-step cognitive task

NASA Astrophysics Data System (ADS)

Cai, Shi-Min; Chen, Wei; Liu, Dong-Bai; Tang, Ming; Chen, Xun

2017-01-01

Functional brain network has been widely studied to understand the relationship between brain organization and behavior. In this paper, we aim to explore the functional connectivity of brain network under a multi-step cognitive task involving consecutive behaviors, and further understand the effect of behaviors on the brain organization. The functional brain networks are constructed based on a high spatial and temporal resolution fMRI dataset and analyzed via complex network based approach. We find that at voxel level the functional brain network shows robust small-worldness and scale-free characteristics, while its assortativity and rich-club organization are slightly restricted to the order of behaviors performed. More interestingly, the functional connectivity of brain network in activated ROIs strongly correlates with behaviors and is obviously restricted to the order of behaviors performed. These empirical results suggest that the brain organization has the generic properties of small-worldness and scale-free characteristics, and its diverse functional connectivity emerging from activated ROIs is strongly driven by these behavioral activities via the plasticity of brain.

[Application of chemometrics in composition-activity relationship research of traditional Chinese medicine].

PubMed

Han, Sheng-Nan

2014-07-01

Chemometrics is a new branch of chemistry which is widely applied to various fields of analytical chemistry. Chemometrics can use theories and methods of mathematics, statistics, computer science and other related disciplines to optimize the chemical measurement process and maximize access to acquire chemical information and other information on material systems by analyzing chemical measurement data. In recent years, traditional Chinese medicine has attracted widespread attention. In the research of traditional Chinese medicine, it has been a key problem that how to interpret the relationship between various chemical components and its efficacy, which seriously restricts the modernization of Chinese medicine. As chemometrics brings the multivariate analysis methods into the chemical research, it has been applied as an effective research tool in the composition-activity relationship research of Chinese medicine. This article reviews the applications of chemometrics methods in the composition-activity relationship research in recent years. The applications of multivariate statistical analysis methods (such as regression analysis, correlation analysis, principal component analysis, etc. ) and artificial neural network (such as back propagation artificial neural network, radical basis function neural network, support vector machine, etc. ) are summarized, including the brief fundamental principles, the research contents and the advantages and disadvantages. Finally, the existing main problems and prospects of its future researches are proposed.

Analysis of the streamflow-gaging station network in Ohio for effectiveness in providing regional streamflow information

USGS Publications Warehouse

Straub, D.E.

1998-01-01

The streamflow-gaging station network in Ohio was evaluated for its effectiveness in providing regional streamflow information. The analysis involved application of the principles of generalized least squares regression between streamflow and climatic and basin characteristics. Regression equations were developed for three flow characteristics: (1) the instantaneous peak flow with a 100-year recurrence interval (P100), (2) the mean annual flow (Qa), and (3) the 7-day, 10-year low flow (7Q10). All active and discontinued gaging stations with 5 or more years of unregulated-streamflow data with respect to each flow characteristic were used to develop the regression equations. The gaging-station network was evaluated for the current (1996) condition of the network and estimated conditions of various network strategies if an additional 5 and 20 years of streamflow data were collected. Any active or discontinued gaging station with (1) less than 5 years of unregulated-streamflow record, (2) previously defined basin and climatic characteristics, and (3) the potential for collection of more unregulated-streamflow record were included in the network strategies involving the additional 5 and 20 years of data. The network analysis involved use of the regression equations, in combination with location, period of record, and cost of operation, to determine the contribution of the data for each gaging station to regional streamflow information. The contribution of each gaging station was based on a cost-weighted reduction of the mean square error (average sampling-error variance) associated with each regional estimating equation. All gaging stations included in the network analysis were then ranked according to their contribution to the regional information for each flow characteristic. The predictive ability of the regression equations developed from the gaging station network could be improved for all three flow characteristics with the collection of additional streamflow data. The addition of new gaging stations to the network would result in an even greater improvement of the accuracy of the regional regression equations. Typically, continued data collection at stations with unregulated streamflow for all flow conditions that had less than 11 years of record with drainage areas smaller than 200 square miles contributed the largest cost-weighted reduction to the average sampling-error variance of the regional estimating equations. The results of the network analyses can be used to prioritize the continued operation of active gaging stations or the reactivation of discontinued gaging stations if the objective is to maximize the regional information content in the streamflow-gaging station network.

IL-32 is a molecular marker of a host defense network in human tuberculosis

PubMed Central

Montoya, Dennis; Inkeles, Megan S.; Liu, Phillip T.; Realegeno, Susan; Teles, Rosane M. B.; Vaidya, Poorva; Munoz, Marcos A.; Schenk, Mirjam; Swindell, William R.; Chun, Rene; Zavala, Kathryn; Hewison, Martin; Adams, John S.; Horvath, Steve; Pellegrini, Matteo; Bloom, Barry R.; Modlin, Robert L.

2014-01-01

Tuberculosis is a leading cause of infectious disease–related death worldwide; however, only 10% of people infected with Mycobacterium tuberculosis develop disease. Factors that contribute to protection could prove to be promising targets for M. tuberculosis therapies. Analysis of peripheral blood gene expression profiles of active tuberculosis patients has identified correlates of risk for disease or pathogenesis. We sought to identify potential human candidate markers of host defense by studying gene expression profiles of macrophages, cells that, upon infection by M. tuberculosis, can mount an antimicrobial response. Weighted gene coexpression network analysis revealed an association between the cytokine interleukin-32 (IL-32) and the vitamin D antimicrobial pathway in a network of interferon-γ– and IL-15–induced “defense response” genes. IL-32 induced the vitamin D–dependent antimicrobial peptides cathelicidin and DEFB4 and to generate antimicrobial activity in vitro, dependent on the presence of adequate 25-hydroxyvitamin D. In addition, the IL-15–induced defense response macrophage gene network was integrated with ranked pairwise comparisons of gene expression from five different clinical data sets of latent compared with active tuberculosis or healthy controls and a coexpression network derived from gene expression in patients with tuberculosis undergoing chemotherapy. Together, these analyses identified eight common genes, including IL-32, as molecular markers of latent tuberculosis and the IL-15–induced gene network. As maintaining M. tuberculosis in a latent state and preventing transition to active disease may represent a form of host resistance, these results identify IL-32 as one functional marker and potential correlate of protection against active tuberculosis. PMID:25143364

IL-32 is a molecular marker of a host defense network in human tuberculosis.

PubMed

Montoya, Dennis; Inkeles, Megan S; Liu, Phillip T; Realegeno, Susan; Teles, Rosane M B; Vaidya, Poorva; Munoz, Marcos A; Schenk, Mirjam; Swindell, William R; Chun, Rene; Zavala, Kathryn; Hewison, Martin; Adams, John S; Horvath, Steve; Pellegrini, Matteo; Bloom, Barry R; Modlin, Robert L

2014-08-20

Tuberculosis is a leading cause of infectious disease-related death worldwide; however, only 10% of people infected with Mycobacterium tuberculosis develop disease. Factors that contribute to protection could prove to be promising targets for M. tuberculosis therapies. Analysis of peripheral blood gene expression profiles of active tuberculosis patients has identified correlates of risk for disease or pathogenesis. We sought to identify potential human candidate markers of host defense by studying gene expression profiles of macrophages, cells that, upon infection by M. tuberculosis, can mount an antimicrobial response. Weighted gene coexpression network analysis revealed an association between the cytokine interleukin-32 (IL-32) and the vitamin D antimicrobial pathway in a network of interferon-γ- and IL-15-induced "defense response" genes. IL-32 induced the vitamin D-dependent antimicrobial peptides cathelicidin and DEFB4 and to generate antimicrobial activity in vitro, dependent on the presence of adequate 25-hydroxyvitamin D. In addition, the IL-15-induced defense response macrophage gene network was integrated with ranked pairwise comparisons of gene expression from five different clinical data sets of latent compared with active tuberculosis or healthy controls and a coexpression network derived from gene expression in patients with tuberculosis undergoing chemotherapy. Together, these analyses identified eight common genes, including IL-32, as molecular markers of latent tuberculosis and the IL-15-induced gene network. As maintaining M. tuberculosis in a latent state and preventing transition to active disease may represent a form of host resistance, these results identify IL-32 as one functional marker and potential correlate of protection against active tuberculosis. Copyright © 2014, American Association for the Advancement of Science.

An Investigation of Synchrony in Transport Networks

NASA Technical Reports Server (NTRS)

Kincaid, Rex K.; Alexandrov, Natalia M.; Holroyd, Michael J.

2007-01-01

The cumulative degree distributions of transport networks, such as air transportation networks and respiratory neuronal networks, follow power laws. The significance of power laws with respect to other network performance measures, such as throughput and synchronization, remains an open question. Evolving methods for the analysis and design of air transportation networks must address network performance in the face of increasing demands and the need to contain and control local network disturbances, such as congestion. Toward this end, we investigate functional relationships that govern the performance of transport networks; for example, the links between the first nontrivial eigenvalue of a network's Laplacian matrix - a quantitative measure of network synchronizability - and other global network parameters. In particular, among networks with a fixed degree distribution and fixed network assortativity (a measure of a network's preference to attach nodes based on a similarity or difference), those with the small eigenvalue are shown to be poor synchronizers, to have much longer shortest paths and to have greater clustering in comparison to those with large. A simulation of a respiratory network adds data to our investigation. This study is a beginning step in developing metrics and design variables for the analysis and active design of air transport networks.

Patent Network Analysis and Quadratic Assignment Procedures to Identify the Convergence of Robot Technologies

PubMed Central

Lee, Woo Jin; Lee, Won Kyung

2016-01-01

Because of the remarkable developments in robotics in recent years, technological convergence has been active in this area. We focused on finding patterns of convergence within robot technology using network analysis of patents in both the USPTO and KIPO. To identify the variables that affect convergence, we used quadratic assignment procedures (QAP). From our analysis, we observed the patent network ecology related to convergence and found technologies that have great potential to converge with other robotics technologies. The results of our study are expected to contribute to setting up convergence based R&D policies for robotics, which can lead new innovation. PMID:27764196

Abnormal neural activities of directional brain networks in patients with long-term bilateral hearing loss.

PubMed

Xu, Long-Chun; Zhang, Gang; Zou, Yue; Zhang, Min-Feng; Zhang, Dong-Sheng; Ma, Hua; Zhao, Wen-Bo; Zhang, Guang-Yu

2017-10-13

The objective of the study is to provide some implications for rehabilitation of hearing impairment by investigating changes of neural activities of directional brain networks in patients with long-term bilateral hearing loss. Firstly, we implemented neuropsychological tests of 21 subjects (11 patients with long-term bilateral hearing loss, and 10 subjects with normal hearing), and these tests revealed significant differences between the deaf group and the controls. Then we constructed the individual specific virtual brain based on functional magnetic resonance data of participants by utilizing effective connectivity and multivariate regression methods. We exerted the stimulating signal to the primary auditory cortices of the virtual brain and observed the brain region activations. We found that patients with long-term bilateral hearing loss presented weaker brain region activations in the auditory and language networks, but enhanced neural activities in the default mode network as compared with normally hearing subjects. Especially, the right cerebral hemisphere presented more changes than the left. Additionally, weaker neural activities in the primary auditor cortices were also strongly associated with poorer cognitive performance. Finally, causal analysis revealed several interactional circuits among activated brain regions, and these interregional causal interactions implied that abnormal neural activities of the directional brain networks in the deaf patients impacted cognitive function.

Working memory activation of neural networks in the elderly as a function of information processing phase and task complexity.

PubMed

Charroud, Céline; Steffener, Jason; Le Bars, Emmanuelle; Deverdun, Jérémy; Bonafe, Alain; Abdennour, Meriem; Portet, Florence; Molino, François; Stern, Yaakov; Ritchie, Karen; Menjot de Champfleur, Nicolas; Akbaraly, Tasnime N

2015-11-01

Changes in working memory are sensitive indicators of both normal and pathological brain aging and associated disability. The present study aims to further understanding of working memory in normal aging using a large cohort of healthy elderly in order to examine three separate phases of information processing in relation to changes in task load activation. Using covariance analysis, increasing and decreasing neural activation was observed on fMRI in response to a delayed item recognition task in 337 cognitively healthy elderly persons as part of the CRESCENDO (Cognitive REServe and Clinical ENDOphenotypes) study. During three phases of the task (stimulation, retention, probe), increased activation was observed with increasing task load in bilateral regions of the prefrontal cortex, parietal lobule, cingulate gyrus, insula and in deep gray matter nuclei, suggesting an involvement of central executive and salience networks. Decreased activation associated with increasing task load was observed during the stimulation phase, in bilateral temporal cortex, parietal lobule, cingulate gyrus and prefrontal cortex. This spatial distribution of decreased activation is suggestive of the default mode network. These findings support the hypothesis of an increased activation in salience and central executive networks and a decreased activation in default mode network concomitant to increasing task load. Copyright © 2015 Elsevier Inc. All rights reserved.

Structural and functional networks in complex systems with delay.

PubMed

Eguíluz, Víctor M; Pérez, Toni; Borge-Holthoefer, Javier; Arenas, Alex

2011-05-01

Functional networks of complex systems are obtained from the analysis of the temporal activity of their components, and are often used to infer their unknown underlying connectivity. We obtain the equations relating topology and function in a system of diffusively delay-coupled elements in complex networks. We solve exactly the resulting equations in motifs (directed structures of three nodes) and in directed networks. The mean-field solution for directed uncorrelated networks shows that the clusterization of the activity is dominated by the in-degree of the nodes, and that the locking frequency decreases with increasing average degree. We find that the exponent of a power law degree distribution of the structural topology γ is related to the exponent of the associated functional network as α=(2-γ)(-1) for γ<2. © 2011 American Physical Society

How the study of online collaborative learning can guide teachers and predict students' performance in a medical course.

PubMed

Saqr, Mohammed; Fors, Uno; Tedre, Matti

2018-02-06

Collaborative learning facilitates reflection, diversifies understanding and stimulates skills of critical and higher-order thinking. Although the benefits of collaborative learning have long been recognized, it is still rarely studied by social network analysis (SNA) in medical education, and the relationship of parameters that can be obtained via SNA with students' performance remains largely unknown. The aim of this work was to assess the potential of SNA for studying online collaborative clinical case discussions in a medical course and to find out which activities correlate with better performance and help predict final grade or explain variance in performance. Interaction data were extracted from the learning management system (LMS) forum module of the Surgery course in Qassim University, College of Medicine. The data were analyzed using social network analysis. The analysis included visual as well as a statistical analysis. Correlation with students' performance was calculated, and automatic linear regression was used to predict students' performance. By using social network analysis, we were able to analyze a large number of interactions in online collaborative discussions and gain an overall insight of the course social structure, track the knowledge flow and the interaction patterns, as well as identify the active participants and the prominent discussion moderators. When augmented with calculated network parameters, SNA offered an accurate view of the course network, each user's position, and level of connectedness. Results from correlation coefficients, linear regression, and logistic regression indicated that a student's position and role in information relay in online case discussions, combined with the strength of that student's network (social capital), can be used as predictors of performance in relevant settings. By using social network analysis, researchers can analyze the social structure of an online course and reveal important information about students' and teachers' interactions that can be valuable in guiding teachers, improve students' engagement, and contribute to learning analytics insights.

Gamma Spectroscopy by Artificial Neural Network Coupled with MCNP

NASA Astrophysics Data System (ADS)

Sahiner, Huseyin

While neutron activation analysis is widely used in many areas, sensitivity of the analysis depends on how the analysis is conducted. Even though the sensitivity of the techniques carries error, compared to chemical analysis, its range is in parts per million or sometimes billion. Due to this sensitivity, the use of neutron activation analysis becomes important when analyzing bio-samples. Artificial neural network is an attractive technique for complex systems. Although there are neural network applications on spectral analysis, training by simulated data to analyze experimental data has not been made. This study offers an improvement on spectral analysis and optimization on neural network for the purpose. The work considers five elements that are considered as trace elements for bio-samples. However, the system is not limited to five elements. The only limitation of the study comes from data library availability on MCNP. A perceptron network was employed to identify five elements from gamma spectra. In quantitative analysis, better results were obtained when the neural fitting tool in MATLAB was used. As a training function, Levenberg-Marquardt algorithm was used with 23 neurons in the hidden layer with 259 gamma spectra in the input. Because the interest of the study deals with five elements, five neurons representing peak counts of five isotopes in the input layer were used. Five output neurons revealed mass information of these elements from irradiated kidney stones. Results showing max error of 17.9% in APA, 24.9% in UA, 28.2% in COM, 27.9% in STRU type showed the success of neural network approach in analyzing gamma spectra. This high error was attributed to Zn that has a very long decay half-life compared to the other elements. The simulation and experiments were made under certain experimental setup (3 hours irradiation, 96 hours decay time, 8 hours counting time). Nevertheless, the approach is subject to be generalized for different setups.

Multiscale Aspects of Generation of High-Gamma Activity during Seizures in Human Neocortex123

PubMed Central

Marcuccilli, Charles J.; Ben-Mabrouk, Faiza; Lew, Sean M.; Goodman, Robert R.; McKhann, Guy M.; Frim, David M.; Kohrman, Michael H.; Schevon, Catherine A.; van Drongelen, Wim

2016-01-01

High-gamma (HG; 80-150 Hz) activity in macroscopic clinical records is considered a marker for critical brain regions involved in seizure initiation; it is correlated with pathological multiunit firing during neocortical seizures in the seizure core, an area identified by correlated multiunit spiking and low frequency seizure activity. However, the effects of the spatiotemporal dynamics of seizure on HG power generation are not well understood. Here, we studied HG generation and propagation, using a three-step, multiscale signal analysis and modeling approach. First, we analyzed concurrent neuronal and microscopic network HG activity in neocortical slices from seven intractable epilepsy patients. We found HG activity in these networks, especially when neurons displayed paroxysmal depolarization shifts and network activity was highly synchronized. Second, we examined HG activity acquired with microelectrode arrays recorded during human seizures (n = 8). We confirmed the presence of synchronized HG power across microelectrode records and the macroscale, both specifically associated with the core region of the seizure. Third, we used volume conduction-based modeling to relate HG activity and network synchrony at different network scales. We showed that local HG oscillations require high levels of synchrony to cross scales, and that this requirement is met at the microscopic scale, but not within macroscopic networks. Instead, we present evidence that HG power at the macroscale may result from harmonics of ongoing seizure activity. Ictal HG power marks the seizure core, but the generating mechanism can differ across spatial scales. PMID:27257623

Default mode of brain function in monkeys.

PubMed

Mantini, Dante; Gerits, Annelis; Nelissen, Koen; Durand, Jean-Baptiste; Joly, Olivier; Simone, Luciano; Sawamura, Hiromasa; Wardak, Claire; Orban, Guy A; Buckner, Randy L; Vanduffel, Wim

2011-09-07

Human neuroimaging has revealed a specific network of brain regions-the default-mode network (DMN)-that reduces its activity during goal-directed behavior. So far, evidence for a similar network in monkeys is mainly indirect, since, except for one positron emission tomography study, it is all based on functional connectivity analysis rather than activity increases during passive task states. Here, we tested whether a consistent DMN exists in monkeys using its defining property. We performed a meta-analysis of functional magnetic resonance imaging data collected in 10 awake monkeys to reveal areas in which activity consistently decreases when task demands shift from passive tasks to externally oriented processing. We observed task-related spatially specific deactivations across 15 experiments, implying in the monkey a functional equivalent of the human DMN. We revealed by resting-state connectivity that prefrontal and medial parietal regions, including areas 9/46d and 31, respectively, constitute the DMN core, being functionally connected to all other DMN areas. We also detected two distinct subsystems composed of DMN areas with stronger functional connections between each other. These clusters included areas 24/32, 8b, and TPOC and areas 23, v23, and PGm, respectively. Such a pattern of functional connectivity largely fits, but is not completely consistent with anatomical tract tracing data in monkeys. Also, analysis of afferent and efferent connections between DMN areas suggests a multisynaptic network structure. Like humans, monkeys increase activity during passive epochs in heteromodal and limbic association regions, suggesting that they also default to internal modes of processing when not actively interacting with the environment.

Default Mode of Brain Function in Monkeys

PubMed Central

Mantini, Dante; Gerits, Annelis; Nelissen, Koen; Durand, Jean-Baptiste; Joly, Olivier; Simone, Luciano; Sawamura, Hiromasa; Wardak, Claire; Orban, Guy A.; Buckner, Randy L.; Vanduffel, Wim

2013-01-01

Human neuroimaging has revealed a specific network of brain regions—the default-mode network (DMN)—that reduces its activity during goal-directed behavior. So far, evidence for a similar network in monkeys is mainly indirect, since, except for one positron emission tomography study, it is all based on functional connectivity analysis rather than activity increases during passive task states. Here, we tested whether a consistent DMN exists in monkeys using its defining property. We performed a meta-analysis of functional magnetic resonance imaging data collected in 10 awake monkeys to reveal areas in which activity consistently decreases when task demands shift from passive tasks to externally oriented processing. We observed task-related spatially specific deactivations across 15 experiments, implying in the monkey a functional equivalent of the human DMN. We revealed by resting-state connectivity that prefrontal and medial parietal regions, including areas 9/46d and 31, respectively, constitute the DMN core, being functionally connected to all other DMN areas. We also detected two distinct subsystems composed of DMN areas with stronger functional connections between each other. These clusters included areas 24/32, 8b, and TPOC and areas 23, v23, and PGm, respectively. Such a pattern of functional connectivity largely fits, but is not completely consistent with anatomical tract tracing data in monkeys. Also, analysis of afferent and efferent connections between DMN areas suggests a multisynaptic network structure. Like humans, monkeys increase activity during passive epochs in heteromodal and limbic association regions, suggesting that they also default to internal modes of processing when not actively interacting with the environment. PMID:21900574

Analysis of stakeholders networks of infant and young child nutrition programmes in Sri Lanka, India, Nepal, Bangladesh and Pakistan.

PubMed

Uddin, Shahadat; Mahmood, Hana; Senarath, Upul; Zahiruddin, Quazi; Karn, Sumit; Rasheed, Sabrina; Dibley, Michael

2017-06-13

Effective public policies are needed to support appropriate infant and young child feeding (IYCF) to ensure adequate child growth and development, especially in low and middle income countries. The aim of this study was to: (i) capture stakeholder networks in relation to funding and technical support for IYCF policy across five countries in South Asia (i.e. Sri Lanka, India, Nepal, Bangladesh and Pakistan); and (ii) understand how stakeholder networks differed between countries, and identify common actors and their patterns in network engagement across the region. The Net-Map method, which is an interview-based mapping technique to visualise and capture connections among different stakeholders that collaborate towards achieving a focused goal, has been used to map funding and technical support networks in all study sites. Our study was conducted at the national level in Bangladesh, India, Nepal, and Sri Lanka, as well as in selected states or provinces in India and Pakistan during 2013-2014. We analysed the network data using a social network analysis software (NodeXL). The number of stakeholders identified as providing technical support was higher than the number of stakeholders providing funding support, across all study sites. India (New Delhi site - national level) site had the highest number of influential stakeholders for both funding (43) and technical support (86) activities. Among all nine study sites, India (New Delhi - national level) and Sri Lanka had the highest number of participating government stakeholders (22) in their respective funding networks. Sri Lanka also had the highest number of participating government stakeholders for technical support (34) among all the study sites. Government stakeholders are more engaged in technical support activities compared with their involvement in funding activities. The United Nations Children's Emergency Fund (UNICEF) and the World Health Organization (WHO) were highly engaged stakeholders for both funding and technical support activities across all study sites. International stakeholders were highly involved in both the funding and technical support activities related to IYCF practices across these nine study sites. Government stakeholders received more support for funding and technical support activities from other stakeholders compared with the support that they offered. Stakeholders were, in general, more engaged for technical support activities compared with the funding activities.

Industrial entrepreneurial network: Structural and functional analysis

NASA Astrophysics Data System (ADS)

Medvedeva, M. A.; Davletbaev, R. H.; Berg, D. B.; Nazarova, J. J.; Parusheva, S. S.

2016-12-01

Structure and functioning of two model industrial entrepreneurial networks are investigated in the present paper. One of these networks is forming when implementing an integrated project and consists of eight agents, which interact with each other and external environment. The other one is obtained from the municipal economy and is based on the set of the 12 real business entities. Analysis of the networks is carried out on the basis of the matrix of mutual payments aggregated over the certain time period. The matrix is created by the methods of experimental economics. Social Network Analysis (SNA) methods and instruments were used in the present research. The set of basic structural characteristics was investigated: set of quantitative parameters such as density, diameter, clustering coefficient, different kinds of centrality, and etc. They were compared with the random Bernoulli graphs of the corresponding size and density. Discovered variations of random and entrepreneurial networks structure are explained by the peculiarities of agents functioning in production network. Separately, were identified the closed exchange circuits (cyclically closed contours of graph) forming an autopoietic (self-replicating) network pattern. The purpose of the functional analysis was to identify the contribution of the autopoietic network pattern in its gross product. It was found that the magnitude of this contribution is more than 20%. Such value allows using of the complementary currency in order to stimulate economic activity of network agents.

Deciding where to attend: Large-scale network mechanisms underlying attention and intention revealed by graph-theoretic analysis.

PubMed

Liu, Yuelu; Hong, Xiangfei; Bengson, Jesse J; Kelley, Todd A; Ding, Mingzhou; Mangun, George R

2017-08-15

The neural mechanisms by which intentions are transformed into actions remain poorly understood. We investigated the network mechanisms underlying spontaneous voluntary decisions about where to focus visual-spatial attention (willed attention). Graph-theoretic analysis of two independent datasets revealed that regions activated during willed attention form a set of functionally-distinct networks corresponding to the frontoparietal network, the cingulo-opercular network, and the dorsal attention network. Contrasting willed attention with instructed attention (where attention is directed by external cues), we observed that the dorsal anterior cingulate cortex was allied with the dorsal attention network in instructed attention, but shifted connectivity during willed attention to interact with the cingulo-opercular network, which then mediated communications between the frontoparietal network and the dorsal attention network. Behaviorally, greater connectivity in network hubs, including the dorsolateral prefrontal cortex, the dorsal anterior cingulate cortex, and the inferior parietal lobule, was associated with faster reaction times. These results, shown to be consistent across the two independent datasets, uncover the dynamic organization of functionally-distinct networks engaged to support intentional acts. Copyright © 2017 Elsevier Inc. All rights reserved.

Controlling self-sustained spiking activity by adding or removing one network link

NASA Astrophysics Data System (ADS)

Xu, Kesheng; Huang, Wenwen; Li, Baowen; Dhamala, Mukesh; Liu, Zonghua

2013-06-01

Being able to control the neuronal spiking activity in specific brain regions is central to a treatment scheme in several brain disorders such as epileptic seizures, mental depression, and Parkinson's diseases. Here, we present an approach for controlling self-sustained oscillations by adding or removing one directed network link in coupled neuronal oscillators, in contrast to previous approaches of adding stimuli or noise. We find that such networks can exhibit a variety of activity patterns such as on-off switch, sustained spikes, and short-term spikes. We derive the condition for a specific link to be the controller of the on-off effect. A qualitative analysis is provided to facilitate the understanding of the mechanism for spiking activity by adding one link. Our findings represent the first report on generating spike activity with the addition of only one directed link to a network and provide a deeper understanding of the microscopic roots of self-sustained spiking.

Combining self-organizing mapping and supervised affinity propagation clustering approach to investigate functional brain networks involved in motor imagery and execution with fMRI measurements.

PubMed

Zhang, Jiang; Liu, Qi; Chen, Huafu; Yuan, Zhen; Huang, Jin; Deng, Lihua; Lu, Fengmei; Zhang, Junpeng; Wang, Yuqing; Wang, Mingwen; Chen, Liangyin

2015-01-01

Clustering analysis methods have been widely applied to identifying the functional brain networks of a multitask paradigm. However, the previously used clustering analysis techniques are computationally expensive and thus impractical for clinical applications. In this study a novel method, called SOM-SAPC that combines self-organizing mapping (SOM) and supervised affinity propagation clustering (SAPC), is proposed and implemented to identify the motor execution (ME) and motor imagery (MI) networks. In SOM-SAPC, SOM was first performed to process fMRI data and SAPC is further utilized for clustering the patterns of functional networks. As a result, SOM-SAPC is able to significantly reduce the computational cost for brain network analysis. Simulation and clinical tests involving ME and MI were conducted based on SOM-SAPC, and the analysis results indicated that functional brain networks were clearly identified with different response patterns and reduced computational cost. In particular, three activation clusters were clearly revealed, which include parts of the visual, ME and MI functional networks. These findings validated that SOM-SAPC is an effective and robust method to analyze the fMRI data with multitasks.

Convergence analysis of stochastic hybrid bidirectional associative memory neural networks with delays

NASA Astrophysics Data System (ADS)

Wan, Li; Zhou, Qinghua

2007-10-01

The stability property of stochastic hybrid bidirectional associate memory (BAM) neural networks with discrete delays is considered. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the delay-independent sufficient conditions to guarantee the exponential stability of the equilibrium solution for such networks are given by using the nonnegative semimartingale convergence theorem.

Assessment of a National Network: The Case of the French Teacher Training Colleges' Health Education Network

ERIC Educational Resources Information Center

Guevel, Marie-Renee; Jourdan, Didier

2009-01-01

The French teacher training colleges' health education (HE) network was set up in 2005 to encourage the inclusion of HE in courses for primary and secondary school teachers. A systematic process of monitoring the activity and the impact of this initiative was implemented. This analysis was systematically compared with the perceptions of teaching…

Characteristics and Impact of the Further Mathematics Knowledge Networks: Analysis of an English Professional Development Initiative on the Teaching of Advanced Mathematics

ERIC Educational Resources Information Center

Ruthven, Kenneth

2014-01-01

Reports from 13 Further Mathematics Knowledge Networks supported by the National Centre for Excellence in the Teaching of Mathematics [NCETM] are analysed. After summarizing basic characteristics of the networks regarding leadership, composition and pattern of activity, each of the following aspects is examined in greater depth: Developmental aims…

Improved security monitoring method for network bordary

NASA Astrophysics Data System (ADS)

Gao, Liting; Wang, Lixia; Wang, Zhenyan; Qi, Aihua

2013-03-01

This paper proposes a network bordary security monitoring system based on PKI. The design uses multiple safe technologies, analysis deeply the association between network data flow and system log, it can detect the intrusion activities and position invasion source accurately in time. The experiment result shows that it can reduce the rate of false alarm or missing alarm of the security incident effectively.

Implementing a social network intervention designed to enhance and diversify support for people with long-term conditions. A qualitative study.

PubMed

Kennedy, Anne; Vassilev, Ivaylo; James, Elizabeth; Rogers, Anne

2016-02-29

For people with long-term conditions, social networks provide a potentially central means of mobilising, mediating and accessing support for health and well-being. Few interventions address the implementation of improving engagement with and through social networks. This paper describes the development and implementation of a web-based tool which comprises: network mapping, user-centred preference elicitation and need assessment and facilitated engagement with resources. The study aimed to determine whether the intervention was acceptable, implementable and acted to enhance support and to add to theory concerning social networks and engagement with resources and activities. A longitudinal design with 15 case studies used ethnographic methods comprising video, non-participant observation of intervention delivery and qualitative interviews (baseline, 6 and 12 months). Participants were people with type 2 diabetes living in a marginalised island community. Facilitators were local health trainers and care navigators. Analysis applied concepts concerning implementation of technology for self-management support to explain how new practices of work were operationalised and how the technology impacted on relationships fit with everyday life and allowed for visual feedback. Most participants reported identifying and taking up new activities as a result of using the tool. Thematic analysis suggested that workability of the tool was predicated on disruption and reconstruction of networks, challenging/supportive facilitation and change and reflection over time concerning network support. Visualisation of the network enabled people to mobilise support and engage in new activities. The tool aligned synergistically with the facilitators' role of linking people to local resources. The social network tool works through a process of initiating positive disruption of established self-management practice through mapping and reflection on personal network membership and support. This opens up possibilities for reconstructing self-management differently from current practice. Key facets of successful implementation were: the visual maps of networks and support options; facilitation characterised by a perceived lack of status difference which assisted engagement and constructive discussion of support and preferences for activities; and background work (a reliable database, tailored preferences, option reduction) for facilitator and user ease of use.

Default and Executive Network Coupling Supports Creative Idea Production

PubMed Central

Beaty, Roger E.; Benedek, Mathias; Barry Kaufman, Scott; Silvia, Paul J.

2015-01-01

The role of attention in creative cognition remains controversial. Neuroimaging studies have reported activation of brain regions linked to both cognitive control and spontaneous imaginative processes, raising questions about how these regions interact to support creative thought. Using functional magnetic resonance imaging (fMRI), we explored this question by examining dynamic interactions between brain regions during a divergent thinking task. Multivariate pattern analysis revealed a distributed network associated with divergent thinking, including several core hubs of the default (posterior cingulate) and executive (dorsolateral prefrontal cortex) networks. The resting-state network affiliation of these regions was confirmed using data from an independent sample of participants. Graph theory analysis assessed global efficiency of the divergent thinking network, and network efficiency was found to increase as a function of individual differences in divergent thinking ability. Moreover, temporal connectivity analysis revealed increased coupling between default and salience network regions (bilateral insula) at the beginning of the task, followed by increased coupling between default and executive network regions at later stages. Such dynamic coupling suggests that divergent thinking involves cooperation between brain networks linked to cognitive control and spontaneous thought, which may reflect focused internal attention and the top-down control of spontaneous cognition during creative idea production. PMID:26084037

Real-Time Visualization of Network Behaviors for Situational Awareness

DOE Office of Scientific and Technical Information (OSTI.GOV)

Best, Daniel M.; Bohn, Shawn J.; Love, Douglas V.

Plentiful, complex, and dynamic data make understanding the state of an enterprise network difficult. Although visualization can help analysts understand baseline behaviors in network traffic and identify off-normal events, visual analysis systems often do not scale well to operational data volumes (in the hundreds of millions to billions of transactions per day) nor to analysis of emergent trends in real-time data. We present a system that combines multiple, complementary visualization techniques coupled with in-stream analytics, behavioral modeling of network actors, and a high-throughput processing platform called MeDICi. This system provides situational understanding of real-time network activity to help analysts takemore » proactive response steps. We have developed these techniques using requirements gathered from the government users for which the tools are being developed. By linking multiple visualization tools to a streaming analytic pipeline, and designing each tool to support a particular kind of analysis (from high-level awareness to detailed investigation), analysts can understand the behavior of a network across multiple levels of abstraction.« less

Muscle networks: Connectivity analysis of EMG activity during postural control

NASA Astrophysics Data System (ADS)

Boonstra, Tjeerd W.; Danna-Dos-Santos, Alessander; Xie, Hong-Bo; Roerdink, Melvyn; Stins, John F.; Breakspear, Michael

2015-12-01

Understanding the mechanisms that reduce the many degrees of freedom in the musculoskeletal system remains an outstanding challenge. Muscle synergies reduce the dimensionality and hence simplify the control problem. How this is achieved is not yet known. Here we use network theory to assess the coordination between multiple muscles and to elucidate the neural implementation of muscle synergies. We performed connectivity analysis of surface EMG from ten leg muscles to extract the muscle networks while human participants were standing upright in four different conditions. We observed widespread connectivity between muscles at multiple distinct frequency bands. The network topology differed significantly between frequencies and between conditions. These findings demonstrate how muscle networks can be used to investigate the neural circuitry of motor coordination. The presence of disparate muscle networks across frequencies suggests that the neuromuscular system is organized into a multiplex network allowing for parallel and hierarchical control structures.

Visual analysis of large heterogeneous social networks by semantic and structural abstraction.

PubMed

Shen, Zeqian; Ma, Kwan-Liu; Eliassi-Rad, Tina

2006-01-01

Social network analysis is an active area of study beyond sociology. It uncovers the invisible relationships between actors in a network and provides understanding of social processes and behaviors. It has become an important technique in a variety of application areas such as the Web, organizational studies, and homeland security. This paper presents a visual analytics tool, OntoVis, for understanding large, heterogeneous social networks, in which nodes and links could represent different concepts and relations, respectively. These concepts and relations are related through an ontology (also known as a schema). OntoVis is named such because it uses information in the ontology associated with a social network to semantically prune a large, heterogeneous network. In addition to semantic abstraction, OntoVis also allows users to do structural abstraction and importance filtering to make large networks manageable and to facilitate analytic reasoning. All these unique capabilities of OntoVis are illustrated with several case studies.

Insights into the etiology-associated gene regulatory networks in hepatocellular carcinoma from The Cancer Genome Atlas.

PubMed

Seshachalam, Veerabrahma Pratap; Sekar, Karthik; Hui, Kam M

2018-04-19

Hepatitis B virus, hepatitis C virus, alcoholic consumption and non-alcoholic fatty liver are the major known risk factors for Hepatocellular carcinoma (HCC). There have been very few studies comparing the underlying biological mechanisms associated with the different etiologies of HCC. In this study, we hypothesized the existence of different regulatory networks associated with different liver disease etiologies involved in hepatocarcinogenesis. Using upstream regulatory analysis tool in ingenuity pathway analysis software, URs were predicted using differential expressed genes for HCC to facilitate the interrogation of global gene regulation. Analysis of regulatory networks for HBV HCC revealed E2F1 as activated UR, regulating genes involved in cell cycle and DNA replication and HNF4A and HNF1A as inhibited UR. In HCV HCC, IFNG, involved in cellular movement and signaling was activated while IL1RN, MAPK1 involved in IL-22 signaling and immune response was inhibited. In Alcoholic-consumption HCC, ERBB2 involved in inflammatory response and cellular movement was activated, whereas HNF4A, NUPR1 were inhibited. For HCC derived from Non-alcoholic fatty liver disease, miR-1249-5p was activated and NUPR1 involved in cell cycle and apoptosis was inhibited. The prognostic value of representative genes identified in the regulatory networks for HBV HCC can be further validated by an independent HBV HCC dataset established in our laboratory with survival data. Our study identified functionally distinct candidate URs for HCC developed from different etiologic risk factors. Further functional validation studies of these regulatory networks could facilitate the management of HCC towards personalized medicine. This article is protected by copyright. All rights reserved.

Tell me twice: A multi-study analysis of the functional connectivity between the cerebrum and cerebellum after repeated trait information.

PubMed

Van Overwalle, Frank; Heleven, Elien; Ma, Ning; Mariën, Peter

2017-01-01

This multi-study analysis (6 fMRI studies; 142 participants) explores the functional activation and connectivity of the cerebellum with the cerebrum during repeated behavioral information uptake informing about personality traits of different persons. The results suggest that trait repetition recruits activity in areas belonging to the mentalizing and executive control networks in the cerebrum, and the executive control areas in the cerebellum. Cerebral activation was observed in the executive control network including the posterior medial frontal cortex (pmFC), the bilateral prefrontal cortex (PFC) and bilateral inferior parietal cortex (IPC), in the mentalizing network including the bilateral middle temporal cortex (MTC) extending to the right superior temporal cortex (STC), as well as in the visual network including the left cuneus (Cun) and the left inferior occipital cortex. Moreover, cerebellar activation was found bilaterally in lobules VI and VII belonging to the executive control network. Importantly, significant patterns of functional connectivity were found linking these cerebellar executive areas with cerebral executive areas in the medial pmFC, the left PFC and the left IPC, and mentalizing areas in the left MTC. In addition, connectivity was found between the cerebral areas in the left hemisphere involved in the executive and mentalizing networks, as well as with their homolog areas in the right hemisphere. The discussion centers on the role of these cerebello-cerebral connections in matching internal predictions generated by the cerebellum with external information from the cerebrum, presumably involving the sequencing of behaviors. Copyright © 2016 Elsevier Inc. All rights reserved.

Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis

PubMed Central

Gopal, Shruti; Miller, Robyn L.; Michael, Andrew; Adali, Tulay; Cetin, Mustafa; Rachakonda, Srinivas; Bustillo, Juan R.; Cahill, Nathan; Baum, Stefi A.; Calhoun, Vince D.

2016-01-01

Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and visual cortex. Tests for differences (HC-SZ) in the spatial variability maps suggest, that at rest, SZs exhibit more activity within externally focused sensory and integrative network and less activity in the default mode network thought to be related to internal reflection. Additionally, tests for difference of variance between groups further emphasize that SZs exhibit greater network variability. These results, consistent with our prediction of increased spatial variability within SZs, enhance our understanding of the disease and suggest that it is not just the amplitude of connectivity that is different in schizophrenia, but also the consistency in spatial connectivity patterns across subjects. PMID:26106217

Networks--New York Subways, A Piece of String, and African Traditions.

ERIC Educational Resources Information Center

Zaslavsky, Claudia

1981-01-01

Introducing network theory to schoolchildren is promoted as a way of encouraging students to deal with new situations, be unafraid of challenges, and learn to think. Several applications, methods of analysis, and suggestions for further activities are provided. (MP)

Stimulus-related independent component and voxel-wise analysis of human brain activity during free viewing of a feature film.

PubMed

Lahnakoski, Juha M; Salmi, Juha; Jääskeläinen, Iiro P; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

2012-01-01

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments.

Stimulus-Related Independent Component and Voxel-Wise Analysis of Human Brain Activity during Free Viewing of a Feature Film

PubMed Central

Lahnakoski, Juha M.; Salmi, Juha; Jääskeläinen, Iiro P.; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

2012-01-01

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments. PMID:22496909

Pathway and network-based analysis of genome-wide association studies and RT-PCR validation in polycystic ovary syndrome

PubMed Central

Shen, Haoran; Liang, Zhou; Zheng, Saihua; Li, Xuelian

2017-01-01

The purpose of this study was to identify promising candidate genes and pathways in polycystic ovary syndrome (PCOS). Microarray dataset GSE345269 obtained from the Gene Expression Omnibus database includes 7 granulosa cell samples from PCOS patients, and 3 normal granulosa cell samples. Differentially expressed genes (DEGs) were screened between PCOS and normal samples. Pathway enrichment analysis was conducted for DEGs using ClueGO and CluePedia plugin of Cytoscape. A Reactome functional interaction (FI) network of the DEGs was built using ReactomeFIViz, and then network modules were extracted, followed by pathway enrichment analysis for the modules. Expression of DEGs in granulosa cell samples was measured using quantitative RT-PCR. A total of 674 DEGs were retained, which were significantly enriched with inflammation and immune-related pathways. Eight modules were extracted from the Reactome FI network. Pathway enrichment analysis revealed significant pathways of each module: module 0, Regulation of RhoA activity and Signaling by Rho GTPases pathways shared ARHGAP4 and ARHGAP9; module 2, GlycoProtein VI-mediated activation cascade pathway was enriched with RHOG; module 3, Thromboxane A2 receptor signaling, Chemokine signaling pathway, CXCR4-mediated signaling events pathways were enriched with LYN, the hub gene of module 3. Results of RT-PCR confirmed the finding of the bioinformatic analysis that ARHGAP4, ARHGAP9, RHOG and LYN were significantly upregulated in PCOS. RhoA-related pathways, GlycoProtein VI-mediated activation cascade pathway, ARHGAP4, ARHGAP9, RHOG and LYN may be involved in the pathogenesis of PCOS. PMID:28949383

Resting-state low-frequency fluctuations reflect individual differences in spoken language learning.

PubMed

Deng, Zhizhou; Chandrasekaran, Bharath; Wang, Suiping; Wong, Patrick C M

2016-03-01

A major challenge in language learning studies is to identify objective, pre-training predictors of success. Variation in the low-frequency fluctuations (LFFs) of spontaneous brain activity measured by resting-state functional magnetic resonance imaging (RS-fMRI) has been found to reflect individual differences in cognitive measures. In the present study, we aimed to investigate the extent to which initial spontaneous brain activity is related to individual differences in spoken language learning. We acquired RS-fMRI data and subsequently trained participants on a sound-to-word learning paradigm in which they learned to use foreign pitch patterns (from Mandarin Chinese) to signal word meaning. We performed amplitude of spontaneous low-frequency fluctuation (ALFF) analysis, graph theory-based analysis, and independent component analysis (ICA) to identify functional components of the LFFs in the resting-state. First, we examined the ALFF as a regional measure and showed that regional ALFFs in the left superior temporal gyrus were positively correlated with learning performance, whereas ALFFs in the default mode network (DMN) regions were negatively correlated with learning performance. Furthermore, the graph theory-based analysis indicated that the degree and local efficiency of the left superior temporal gyrus were positively correlated with learning performance. Finally, the default mode network and several task-positive resting-state networks (RSNs) were identified via the ICA. The "competition" (i.e., negative correlation) between the DMN and the dorsal attention network was negatively correlated with learning performance. Our results demonstrate that a) spontaneous brain activity can predict future language learning outcome without prior hypotheses (e.g., selection of regions of interest--ROIs) and b) both regional dynamics and network-level interactions in the resting brain can account for individual differences in future spoken language learning success. Copyright © 2015 Elsevier Ltd. All rights reserved.

Resting-state low-frequency fluctuations reflect individual differences in spoken language learning

PubMed Central

Deng, Zhizhou; Chandrasekaran, Bharath; Wang, Suiping; Wong, Patrick C.M.

2016-01-01

A major challenge in language learning studies is to identify objective, pre-training predictors of success. Variation in the low-frequency fluctuations (LFFs) of spontaneous brain activity measured by resting-state functional magnetic resonance imaging (RS-fMRI) has been found to reflect individual differences in cognitive measures. In the present study, we aimed to investigate the extent to which initial spontaneous brain activity is related to individual differences in spoken language learning. We acquired RS-fMRI data and subsequently trained participants on a sound-to-word learning paradigm in which they learned to use foreign pitch patterns (from Mandarin Chinese) to signal word meaning. We performed amplitude of spontaneous low-frequency fluctuation (ALFF) analysis, graph theory-based analysis, and independent component analysis (ICA) to identify functional components of the LFFs in the resting-state. First, we examined the ALFF as a regional measure and showed that regional ALFFs in the left superior temporal gyrus were positively correlated with learning performance, whereas ALFFs in the default mode network (DMN) regions were negatively correlated with learning performance. Furthermore, the graph theory-based analysis indicated that the degree and local efficiency of the left superior temporal gyrus were positively correlated with learning performance. Finally, the default mode network and several task-positive resting-state networks (RSNs) were identified via the ICA. The “competition” (i.e., negative correlation) between the DMN and the dorsal attention network was negatively correlated with learning performance. Our results demonstrate that a) spontaneous brain activity can predict future language learning outcome without prior hypotheses (e.g., selection of regions of interest – ROIs) and b) both regional dynamics and network-level interactions in the resting brain can account for individual differences in future spoken language learning success. PMID:26866283

Integrated expression analysis identifies transcription networks in mouse and human gastric neoplasia.

PubMed

Chen, Zheng; Soutto, Mohammed; Rahman, Bushra; Fazili, Muhammad W; Peng, DunFa; Blanca Piazuelo, Maria; Chen, Heidi; Kay Washington, M; Shyr, Yu; El-Rifai, Wael

2017-07-01

Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide. The Tff1 knockout (KO) mouse model develops gastric lesions that include low-grade dysplasia (LGD), high-grade dysplasia (HGD), and adenocarcinomas. In this study, we used Affymetrix microarrays gene expression platforms for analysis of molecular signatures in the mouse stomach [Tff1-KO (LGD) and Tff1 wild-type (normal)] and human gastric cancer tissues and their adjacent normal tissue samples. Combined integrated bioinformatics analysis of mouse and human datasets indicated that 172 genes were consistently deregulated in both human gastric cancer samples and Tff1-KO LGD lesions (P < .05). Using Ingenuity pathway analysis, these genes mapped to important transcription networks that include MYC, STAT3, β-catenin, RELA, NFATC2, HIF1A, and ETS1 in both human and mouse. Further analysis demonstrated activation of FOXM1 and inhibition of TP53 transcription networks in human gastric cancers but not in Tff1-KO LGD lesions. Using real-time RT-PCR, we validated the deregulated expression of several genes (VCAM1, BGN, CLDN2, COL1A1, COL1A2, COL3A1, EpCAM, IFITM1, MMP9, MMP12, MMP14, PDGFRB, PLAU, and TIMP1) that map to altered transcription networks in both mouse and human gastric neoplasia. Our study demonstrates significant similarities in deregulated transcription networks in human gastric cancer and gastric tumorigenesis in the Tff1-KO mouse model. The data also suggest that activation of MYC, STAT3, RELA, and β-catenin transcription networks could be an early molecular step in gastric carcinogenesis. © 2017 Wiley Periodicals, Inc.

Automatic analysis of attack data from distributed honeypot network

NASA Astrophysics Data System (ADS)

Safarik, Jakub; Voznak, MIroslav; Rezac, Filip; Partila, Pavol; Tomala, Karel

2013-05-01

There are many ways of getting real data about malicious activity in a network. One of them relies on masquerading monitoring servers as a production one. These servers are called honeypots and data about attacks on them brings us valuable information about actual attacks and techniques used by hackers. The article describes distributed topology of honeypots, which was developed with a strong orientation on monitoring of IP telephony traffic. IP telephony servers can be easily exposed to various types of attacks, and without protection, this situation can lead to loss of money and other unpleasant consequences. Using a distributed topology with honeypots placed in different geological locations and networks provides more valuable and independent results. With automatic system of gathering information from all honeypots, it is possible to work with all information on one centralized point. Communication between honeypots and centralized data store use secure SSH tunnels and server communicates only with authorized honeypots. The centralized server also automatically analyses data from each honeypot. Results of this analysis and also other statistical data about malicious activity are simply accessible through a built-in web server. All statistical and analysis reports serve as information basis for an algorithm which classifies different types of used VoIP attacks. The web interface then brings a tool for quick comparison and evaluation of actual attacks in all monitored networks. The article describes both, the honeypots nodes in distributed architecture, which monitor suspicious activity, and also methods and algorithms used on the server side for analysis of gathered data.

Design and architecture of the Mars relay network planning and analysis framework

NASA Technical Reports Server (NTRS)

Cheung, K. M.; Lee, C. H.

2002-01-01

In this paper we describe the design and architecture of the Mars Network planning and analysis framework that supports generation and validation of efficient planning and scheduling strategy. The goals are to minimize the transmitting time, minimize the delaying time, and/or maximize the network throughputs. The proposed framework would require (1) a client-server architecture to support interactive, batch, WEB, and distributed analysis and planning applications for the relay network analysis scheme, (2) a high-fidelity modeling and simulation environment that expresses link capabilities between spacecraft to spacecraft and spacecraft to Earth stations as time-varying resources, and spacecraft activities, link priority, Solar System dynamic events, the laws of orbital mechanics, and other limiting factors as spacecraft power and thermal constraints, (3) an optimization methodology that casts the resource and constraint models into a standard linear and nonlinear constrained optimization problem that lends itself to commercial off-the-shelf (COTS)planning and scheduling algorithms.

Identification of Common Neural Circuit Disruptions in Cognitive Control Across Psychiatric Disorders.

PubMed

McTeague, Lisa M; Huemer, Julia; Carreon, David M; Jiang, Ying; Eickhoff, Simon B; Etkin, Amit

2017-07-01

Cognitive deficits are a common feature of psychiatric disorders. The authors investigated the nature of disruptions in neural circuitry underlying cognitive control capacities across psychiatric disorders through a transdiagnostic neuroimaging meta-analysis. A PubMed search was conducted for whole-brain functional neuroimaging articles published through June 2015 that compared activation in patients with axis I disorders and matched healthy control participants during cognitive control tasks. Tasks that probed performance or conflict monitoring, response inhibition or selection, set shifting, verbal fluency, and recognition or working memory were included. Activation likelihood estimation meta-analyses were conducted on peak voxel coordinates. The 283 experiments submitted to meta-analysis included 5,728 control participants and 5,493 patients with various disorders (schizophrenia, bipolar or unipolar depression, anxiety disorders, and substance use disorders). Transdiagnostically abnormal activation was evident in the left prefrontal cortex as well as the anterior insula, the right ventrolateral prefrontal cortex, the right intraparietal sulcus, and the midcingulate/presupplementary motor area. Disruption was also observed in a more anterior cluster in the dorsal cingulate cortex, which overlapped with a network of structural perturbation that the authors previously reported in a transdiagnostic meta-analysis of gray matter volume. These findings demonstrate a common pattern of disruption across major psychiatric disorders that parallels the "multiple-demand network" observed in intact cognition. This network interfaces with the anterior-cingulo-insular or "salience network" demonstrated to be transdiagnostically vulnerable to gray matter reduction. Thus, networks intrinsic to adaptive, flexible cognition are vulnerable to broad-spectrum psychopathology. Dysfunction in these networks may reflect an intermediate transdiagnostic phenotype, which could be leveraged to advance therapeutics.

Applications of self-organizing neural networks in virtual screening and diversity selection.

PubMed

Selzer, Paul; Ertl, Peter

2006-01-01

Artificial neural networks provide a powerful technique for the analysis and modeling of nonlinear relationships between molecular structures and pharmacological activity. Many network types, including Kohonen and counterpropagation, also provide an intuitive method for the visual assessment of correspondence between the input and output data. This work shows how a combination of neural networks and radial distribution function molecular descriptors can be applied in various areas of industrial pharmaceutical research. These applications include the prediction of biological activity, the selection of screening candidates (cherry picking), and the extraction of representative subsets from large compound collections such as combinatorial libraries. The methods described have also been implemented as an easy-to-use Web tool, allowing chemists to perform interactive neural network experiments on the Novartis intranet.

Discriminative Cooperative Networks for Detecting Phase Transitions

NASA Astrophysics Data System (ADS)

Liu, Ye-Hua; van Nieuwenburg, Evert P. L.

2018-04-01

The classification of states of matter and their corresponding phase transitions is a special kind of machine-learning task, where physical data allow for the analysis of new algorithms, which have not been considered in the general computer-science setting so far. Here we introduce an unsupervised machine-learning scheme for detecting phase transitions with a pair of discriminative cooperative networks (DCNs). In this scheme, a guesser network and a learner network cooperate to detect phase transitions from fully unlabeled data. The new scheme is efficient enough for dealing with phase diagrams in two-dimensional parameter spaces, where we can utilize an active contour model—the snake—from computer vision to host the two networks. The snake, with a DCN "brain," moves and learns actively in the parameter space, and locates phase boundaries automatically.

Network access to PCDS (SPAN, ESN, SESNET, ARPANET)

NASA Technical Reports Server (NTRS)

Green, J.

1986-01-01

One of the major goals of the National Space Science Data Center is to increase access to NASA data systems by enhancing networking activities. The activities are centered around three basic networking systems: the Space Physics Analysis Network (SPAN); the Earth Science Network (ESN); and the NASA Packet Switched System (NPSS). Each system is described, linkages among systems are explained, and future plans are announced. The inclusion of several new climate nodes on SPAN or ESN are also mentioned. Presently, the Pilot Climate Data System is accessible through SPAN and will be accessible through NPSS by summer and ESN by the end of 1986. Ambitious plans for implementation are underway. The implementation of these plans will represent a major advance in the utilization and accessibility of data worldwide.

CI-KNOW: Cyberinfrastructure Knowledge Networks on the Web. A Social Network Enabled Recommender System for Locating Resources in Cyberinfrastructures

NASA Astrophysics Data System (ADS)

Green, H. D.; Contractor, N. S.; Yao, Y.

2006-12-01

A knowledge network is a multi-dimensional network created from the interactions and interconnections among the scientists, documents, data, analytic tools, and interactive collaboration spaces (like forums and wikis) associated with a collaborative environment. CI-KNOW is a suite of software tools that leverages automated data collection, social network theories, analysis techniques and algorithms to infer an individual's interests and expertise based on their interactions and activities within a knowledge network. The CI-KNOW recommender system mines the knowledge network associated with a scientific community's use of cyberinfrastructure tools and uses relational metadata to record connections among entities in the knowledge network. Recent developments in social network theories and methods provide the backbone for a modular system that creates recommendations from relational metadata. A network navigation portlet allows users to locate colleagues, documents, data or analytic tools in the knowledge network and to explore their networks through a visual, step-wise process. An internal auditing portlet offers administrators diagnostics to assess the growth and health of the entire knowledge network. The first instantiation of the prototype CI-KNOW system is part of the Environmental Cyberinfrastructure Demonstration project at the National Center for Supercomputing Applications, which supports the activities of hydrologic and environmental science communities (CLEANER and CUAHSI) under the umbrella of the WATERS network environmental observatory planning activities (http://cleaner.ncsa.uiuc.edu). This poster summarizes the key aspects of the CI-KNOW system, highlighting the key inputs, calculation mechanisms, and output modalities.

Application of artificial neural network in precise prediction of cement elements percentages based on the neutron activation analysis

NASA Astrophysics Data System (ADS)

Eftekhari Zadeh, E.; Feghhi, S. A. H.; Roshani, G. H.; Rezaei, A.

2016-05-01

Due to variation of neutron energy spectrum in the target sample during the activation process and to peak overlapping caused by the Compton effect with gamma radiations emitted from activated elements, which results in background changes and consequently complex gamma spectrum during the measurement process, quantitative analysis will ultimately be problematic. Since there is no simple analytical correlation between peaks' counts with elements' concentrations, an artificial neural network for analyzing spectra can be a helpful tool. This work describes a study on the application of a neural network to determine the percentages of cement elements (mainly Ca, Si, Al, and Fe) using the neutron capture delayed gamma-ray spectra of the substance emitted by the activated nuclei as patterns which were simulated via the Monte Carlo N-particle transport code, version 2.7. The Radial Basis Function (RBF) network is developed with four specific peaks related to Ca, Si, Al and Fe, which were extracted as inputs. The proposed RBF model is developed and trained with MATLAB 7.8 software. To obtain the optimal RBF model, several structures have been constructed and tested. The comparison between simulated and predicted values using the proposed RBF model shows that there is a good agreement between them.

Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity.

PubMed

Schmidt, Helmut; Petkov, George; Richardson, Mark P; Terry, John R

2014-11-01

Graph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz) and low-alpha (6-9 Hz) bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic stratification of people with epilepsy leading to improved diagnostics and therapeutics.

Combination of DTI and fMRI reveals the white matter changes correlating with the decline of default-mode network activity in Alzheimer's disease

NASA Astrophysics Data System (ADS)

Wu, Xianjun; Di, Qian; Li, Yao; Zhao, Xiaojie

2009-02-01

Recently, evidences from fMRI studies have shown that there was decreased activity among the default-mode network in Alzheimer's disease (AD), and DTI researches also demonstrated that demyelinations exist in white matter of AD patients. Therefore, combining these two MRI methods may help to reveal the relationship between white matter damages and alterations of the resting state functional connectivity network. In the present study, we tried to address this issue by means of correlation analysis between DTI and resting state fMRI images. The default-mode networks of AD and normal control groups were compared to find the areas with significantly declined activity firstly. Then, the white matter regions whose fractional anisotropy (FA) value correlated with this decline were located through multiple regressions between the FA values and the BOLD response of the default networks. Among these correlating white matter regions, those whose FA values also declined were found by a group comparison between AD patients and healthy elderly control subjects. Our results showed that the areas with decreased activity among default-mode network included left posterior cingulated cortex (PCC), left medial temporal gyrus et al. And the damaged white matter areas correlated with the default-mode network alterations were located around left sub-gyral temporal lobe. These changes may relate to the decreased connectivity between PCC and medial temporal lobe (MTL), and thus correlate with the deficiency of default-mode network activity.

Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data

PubMed Central

Kümmel, Anne; Panke, Sven; Heinemann, Matthias

2006-01-01

As one of the most recent members of the omics family, large-scale quantitative metabolomics data are currently complementing our systems biology data pool and offer the chance to integrate the metabolite level into the functional analysis of cellular networks. Network-embedded thermodynamic analysis (NET analysis) is presented as a framework for mechanistic and model-based analysis of these data. By coupling the data to an operating metabolic network via the second law of thermodynamics and the metabolites' Gibbs energies of formation, NET analysis allows inferring functional principles from quantitative metabolite data; for example it identifies reactions that are subject to active allosteric or genetic regulation as exemplified with quantitative metabolite data from Escherichia coli and Saccharomyces cerevisiae. Moreover, the optimization framework of NET analysis was demonstrated to be a valuable tool to systematically investigate data sets for consistency, for the extension of sub-omic metabolome data sets and for resolving intracompartmental concentrations from cell-averaged metabolome data. Without requiring any kind of kinetic modeling, NET analysis represents a perfectly scalable and unbiased approach to uncover insights from quantitative metabolome data. PMID:16788595

Introducing Co-Activation Pattern Metrics to Quantify Spontaneous Brain Network Dynamics

PubMed Central

Chen, Jingyuan E.; Chang, Catie; Greicius, Michael D.; Glover, Gary H.

2015-01-01

Recently, fMRI researchers have begun to realize that the brain's intrinsic network patterns may undergo substantial changes during a single resting state (RS) scan. However, despite the growing interest in brain dynamics, metrics that can quantify the variability of network patterns are still quite limited. Here, we first introduce various quantification metrics based on the extension of co-activation pattern (CAP) analysis, a recently proposed point-process analysis that tracks state alternations at each individual time frame and relies on very few assumptions; then apply these proposed metrics to quantify changes of brain dynamics during a sustained 2-back working memory (WM) task compared to rest. We focus on the functional connectivity of two prominent RS networks, the default-mode network (DMN) and executive control network (ECN). We first demonstrate less variability of global Pearson correlations with respect to the two chosen networks using a sliding-window approach during WM task compared to rest; then we show that the macroscopic decrease in variations in correlations during a WM task is also well characterized by the combined effect of a reduced number of dominant CAPs, increased spatial consistency across CAPs, and increased fractional contributions of a few dominant CAPs. These CAP metrics may provide alternative and more straightforward quantitative means of characterizing brain network dynamics than time-windowed correlation analyses. PMID:25662866

Acupuncture mobilizes the brain's default mode and its anti-correlated network in healthy subjects.

PubMed

Hui, Kathleen K S; Marina, Ovidiu; Claunch, Joshua D; Nixon, Erika E; Fang, Jiliang; Liu, Jing; Li, Ming; Napadow, Vitaly; Vangel, Mark; Makris, Nikos; Chan, Suk-Tak; Kwong, Kenneth K; Rosen, Bruce R

2009-09-01

Previous work has shown that acupuncture stimulation evokes deactivation of a limbic-paralimbic-neocortical network (LPNN) as well as activation of somatosensory brain regions. This study explores the activity and functional connectivity of these regions during acupuncture vs. tactile stimulation and vs. acupuncture associated with inadvertent sharp pain. Acupuncture during 201 scans and tactile stimulation during 74 scans for comparison at acupoints LI4, ST36 and LV3 was monitored with fMRI and psychophysical response in 48 healthy subjects. Clusters of deactivated regions in the medial prefrontal, medial parietal and medial temporal lobes as well as activated regions in the sensorimotor and a few paralimbic structures can be identified during acupuncture by general linear model analysis and seed-based cross correlation analysis. Importantly, these clusters showed virtual identity with the default mode network and the anti-correlated task-positive network in response to stimulation. In addition, the amygdala and hypothalamus, structures not routinely reported in the default mode literature, were frequently involved in acupuncture. When acupuncture induced sharp pain, the deactivation was attenuated or became activated instead. Tactile stimulation induced greater activation of the somatosensory regions but less extensive deactivation of the LPNN. These results indicate that the deactivation of the LPNN during acupuncture cannot be completely explained by the demand of attention that is commonly proposed in the default mode literature. Our results suggest that acupuncture mobilizes the anti-correlated functional networks of the brain to mediate its actions, and that the effect is dependent on the psychophysical response.

Incorporating spatial constraint in co-activation pattern analysis to explore the dynamics of resting-state networks: An application to Parkinson's disease.

PubMed

Zhuang, Xiaowei; Walsh, Ryan R; Sreenivasan, Karthik; Yang, Zhengshi; Mishra, Virendra; Cordes, Dietmar

2018-05-15

The dynamics of the brain's intrinsic networks have been recently studied using co-activation pattern (CAP) analysis. The CAP method relies on few model assumptions and CAP-based measurements provide quantitative information of network temporal dynamics. One limitation of existing CAP-related methods is that the computed CAPs share considerable spatial overlap that may or may not be functionally distinct relative to specific network dynamics. To more accurately describe network dynamics with spatially distinct CAPs, and to compare network dynamics between different populations, a novel data-driven CAP group analysis method is proposed in this study. In the proposed method, a dominant-CAP (d-CAP) set is synthesized across CAPs from multiple clustering runs for each group with the constraint of low spatial similarities among d-CAPs. Alternating d-CAPs with less overlapping spatial patterns can better capture overall network dynamics. The number of d-CAPs, the temporal fraction and spatial consistency of each d-CAP, and the subject-specific switching probability among all d-CAPs are then calculated for each group and used to compare network dynamics between groups. The spatial dissimilarities among d-CAPs computed with the proposed method were first demonstrated using simulated data. High consistency between simulated ground-truth and computed d-CAPs was achieved, and detailed comparisons between the proposed method and existing CAP-based methods were conducted using simulated data. In an effort to physiologically validate the proposed technique and investigate network dynamics in a relevant brain network disorder, the proposed method was then applied to data from the Parkinson's Progression Markers Initiative (PPMI) database to compare the network dynamics in Parkinson's disease (PD) and normal control (NC) groups. Fewer d-CAPs, skewed distribution of temporal fractions of d-CAPs, and reduced switching probabilities among final d-CAPs were found in most networks in the PD group, as compared to the NC group. Furthermore, an overall negative association between switching probability among d-CAPs and disease severity was observed in most networks in the PD group as well. These results expand upon previous findings from in vivo electrophysiological recording studies in PD. Importantly, this novel analysis also demonstrates that changes in network dynamics can be measured using resting-state fMRI data from subjects with early stage PD. Copyright © 2018 Elsevier Inc. All rights reserved.

Gene Networks and Functional Features of Gravitropic response in Rice Shoot Bases

NASA Astrophysics Data System (ADS)

Hu, Liwei; Zang, Aiping; Ai, Qianru; Chen, Haiying; Li, Lin; Li, Rui; Su, Feng; Chen, Xijiang; Rong, Hui; Dou, Xianying; Reinhold-Hurek, Barbara; Li, Qi; Cai, Weiming

To delineate key genes and the corresponding physiological functions as well as the coordina-tion of genes involved in the gravitropism of rice shoot bases, we used whole-genome microarray analysis of upper and lower parts of rice shoot bases at 0.5 h and 6 h after gravistimulation. And bio-information analysis was applied including GO-analysis, expression tendency and net-work analysis. In the lower shoot bases, auxin-mediated signaling pathway and glutathione transferase activity with the biggest enrichment were activated at 0.5 h, while cytokinin stimu-lus and photosynthesis were activated at 6 h. Meanwhile, several processes were suppressed in the lower shoot bases, including: xyloglucan:xyloglucosyl transferase activity, glucan metabolic processes, and ATPase activity at 0.5 h; and tRNA isopentenyltransferase activity, and chiti-nase activity, etc. at 6 h. Gene expression profile responding to gravistimulation suggested that the asymmetrically activation of several phytohormone signaling pathways including auxin, gib-berellin and cytokinin brassinolide ethylene and cytokinin-related genes were involved in the differentially growth between the upper and lower parts of rice shoot bases, and so do cell wall-related genes. Topological analysis of the coexpression networks revealed the core statue of AY177699.1(apetala3-like protein) and AK105103.1 at 0.5 h; AK062612.1 (ethylene response factor) and AK099932.1 (lectin-like receptor kinase 72) at 6 h. All the core factors have the function "response to endogenous stimulus". Additionally, AK108057.1(similar to germin-like protein precursor) was discovered as the most important core gene in the upper shoot bases in 6h after gravistimualtion while AK067424.1(cellulose synthase-like protein), AK120101.1 (Zinc finger, B-box domain containing protein) and CR278698 (ATPase associated with various cel-lular activities cellulose synthase-like protein) contribute equally to gravitropic response in the lower shoot bases.

Pharmacological modulation of pulvinar resting-state regional oscillations and network dynamics in major depression

PubMed Central

Tadayonnejad, Reza; Ajilore, Olusola; Mickey, Brian J.; Crane, Natania A.; Hsu, David T.; Kumar, Anand; Zubieta, Jon-Kar; Langenecker, Scott A.

2016-01-01

The pulvinar, the largest thalamus nucleus, has rich anatomical connections with several different cortical and subcortical regions suggesting its important involvement in high-level cognitive and emotional functions. Unfortunately, pulvinar dysfunction in psychiatric disorders particularly major depression disorder has not been thoroughly examined to date. In this study we explored the alterations in the baseline regional and network activities of the pulvinar in MDD by applying spectral analysis of resting-state oscillatory activity, functional connectivity and directed (effective) connectivity on resting-state fMRI data acquired from 20 healthy controls and 19 participants with MDD. Furthermore, we tested how pharmacological treatment with duloxetine can modulate the measured local and network variables in ten participants who completed treatment. Our results revealed a frequency-band dependent modulation of power spectrum characteristics of pulvinar regional oscillatory activity. At the network level, we found MDD is associated with aberrant causal interactions between pulvinar and several systems including default-mode and posterior insular networks. It was also shown that duloxetine treatment can correct or overcompensate the pathologic network behavior of the pulvinar. In conclusion, we suggest that pulvinar regional baseline oscillatory activity and its resting-state network dynamics are compromised in MDD and can be modulated therapeutically by pharmacological treatment. PMID:27148894

A model for the multiplex dynamics of two-mode and one-mode networks, with an application to employment preference, friendship, and advice

PubMed Central

Snijders, Tom A.B.; Lomi, Alessandro; Torló, Vanina Jasmine

2012-01-01

We propose a new stochastic actor-oriented model for the co-evolution of two-mode and one-mode networks. The model posits that activities of a set of actors, represented in the two-mode network, co-evolve with exchanges and interactions between the actors, as represented in the one-mode network. The model assumes that the actors, not the activities, have agency. The empirical value of the model is demonstrated by examining how employment preferences co-evolve with friendship and advice relations in a group of seventy-five MBA students. The analysis shows that activity in the two-mode network, as expressed by number of employment preferences, is related to activity in the friendship network, as expressed by outdegrees. Further, advice ties between students lead to agreement with respect to employment preferences. In addition, considering the multiplexity of advice and friendship ties yields a better understanding of the dynamics of the advice relation: tendencies to reciprocation and homophily in advice relations are mediated to an important extent by friendship relations. The discussion pays attention to the implications of this study in the broader context of current efforts to model the co-evolutionary dynamics of social networks and individual behavior. PMID:23690653

Toward Collaboration Sensing

ERIC Educational Resources Information Center

Schneider, Bertrand; Pea, Roy

2014-01-01

We describe preliminary applications of network analysis techniques to eye-tracking data collected during a collaborative learning activity. This paper makes three contributions: first, we visualize collaborative eye-tracking data as networks, where the nodes of the graph represent fixations and edges represent saccades. We found that those…

Spatial fingerprints of community structure in human interaction network for an extensive set of large-scale regions.

PubMed

Kallus, Zsófia; Barankai, Norbert; Szüle, János; Vattay, Gábor

2015-01-01

Human interaction networks inferred from country-wide telephone activity recordings were recently used to redraw political maps by projecting their topological partitions into geographical space. The results showed remarkable spatial cohesiveness of the network communities and a significant overlap between the redrawn and the administrative borders. Here we present a similar analysis based on one of the most popular online social networks represented by the ties between more than 5.8 million of its geo-located users. The worldwide coverage of their measured activity allowed us to analyze the large-scale regional subgraphs of entire continents and an extensive set of examples for single countries. We present results for North and South America, Europe and Asia. In our analysis we used the well-established method of modularity clustering after an aggregation of the individual links into a weighted graph connecting equal-area geographical pixels. Our results show fingerprints of both of the opposing forces of dividing local conflicts and of uniting cross-cultural trends of globalization.

Microarray and network-based identification of functional modules and pathways of active tuberculosis.

PubMed

Bian, Zhong-Rui; Yin, Juan; Sun, Wen; Lin, Dian-Jie

2017-04-01

Diagnose of active tuberculosis (TB) is challenging and treatment response is also difficult to efficiently monitor. The aim of this study was to use an integrated analysis of microarray and network-based method to the samples from publically available datasets to obtain a diagnostic module set and pathways in active TB. Towards this goal, background protein-protein interactions (PPI) network was generated based on global PPI information and gene expression data, following by identification of differential expression network (DEN) from the background PPI network. Then, ego genes were extracted according to the degree features in DEN. Next, module collection was conducted by ego gene expansion based on EgoNet algorithm. After that, differential expression of modules between active TB and controls was evaluated using random permutation test. Finally, biological significance of differential modules was detected by pathways enrichment analysis based on Reactome database, and Fisher's exact test was implemented to extract differential pathways for active TB. Totally, 47 ego genes and 47 candidate modules were identified from the DEN. By setting the cutoff-criteria of gene size >5 and classification accuracy ≥0.9, 7 ego modules (Module 4, Module 7, Module 9, Module 19, Module 25, Module 38 and Module 43) were extracted, and all of them had the statistical significance between active TB and controls. Then, Fisher's exact test was conducted to capture differential pathways for active TB. Interestingly, genes in Module 4, Module 25, Module 38, and Module 43 were enriched in the same pathway, formation of a pool of free 40S subunits. Significant pathway for Module 7 and Module 9 was eukaryotic translation termination, and for Module 19 was nonsense mediated decay enhanced by the exon junction complex (EJC). Accordingly, differential modules and pathways might be potential biomarkers for treating active TB, and provide valuable clues for better understanding of molecular mechanism of active TB. Copyright © 2017 Elsevier Ltd. All rights reserved.

Intrinsic Network Connectivity Patterns Underlying Specific Dimensions of Impulsiveness in Healthy Young Adults.

PubMed

Kubera, Katharina M; Hirjak, Dusan; Wolf, Nadine D; Sambataro, Fabio; Thomann, Philipp A; Wolf, R Christian

2018-05-01

Impulsiveness is a central human personality trait and of high relevance for the development of several mental disorders. Impulsiveness is a multidimensional construct, yet little is known about dimension-specific neural correlates. Here, we address the question whether motor, attentional and non-planning components, as measured by the Barratt Impulsiveness Scale (BIS-11), are associated with distinct or overlapping neural network activity. In this study, we investigated brain activity at rest and its relationship to distinct dimensions of impulsiveness in 30 healthy young adults (m/f = 13/17; age mean/SD = 26.4/2.6 years) using resting-state functional magnetic resonance imaging at 3T. A spatial independent component analysis and a multivariate model selection strategy were used to identify systems loading on distinct impulsivity domains. We first identified eight networks for which we had a-priori hypotheses. These networks included basal ganglia, cortical motor, cingulate and lateral prefrontal systems. From the eight networks, three were associated with impulsiveness measures (p < 0.05, FDR corrected). There were significant relationships between right frontoparietal network function and all three BIS domains. Striatal and midcingulate network activity was associated with motor impulsiveness only. Within the networks regionally confined effects of age and gender were found. These data suggest distinct and overlapping patterns of neural activity underlying specific dimensions of impulsiveness. Motor impulsiveness appears to be specifically related to striatal and midcingulate network activity, in contrast to a domain-unspecific right frontoparietal system. Effects of age and gender have to be considered in young healthy samples.

Privacy Breach Analysis in Social Networks

NASA Astrophysics Data System (ADS)

Nagle, Frank

This chapter addresses various aspects of analyzing privacy breaches in social networks. We first review literature that defines three types of privacy breaches in social networks: interactive, active, and passive. We then survey the various network anonymization schemes that have been constructed to address these privacy breaches. After exploring these breaches and anonymization schemes, we evaluate a measure for determining the level of anonymity inherent in a network graph based on its topological structure. Finally, we close by emphasizing the difficulty of anonymizing social network data while maintaining usability for research purposes and offering areas for future work.

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity.

PubMed

Leuthaeuser, Janelle B; Knutson, Stacy T; Kumar, Kiran; Babbitt, Patricia C; Fetrow, Jacquelyn S

2015-09-01

The development of accurate protein function annotation methods has emerged as a major unsolved biological problem. Protein similarity networks, one approach to function annotation via annotation transfer, group proteins into similarity-based clusters. An underlying assumption is that the edge metric used to identify such clusters correlates with functional information. In this contribution, this assumption is evaluated by observing topologies in similarity networks using three different edge metrics: sequence (BLAST), structure (TM-Align), and active site similarity (active site profiling, implemented in DASP). Network topologies for four well-studied protein superfamilies (enolase, peroxiredoxin (Prx), glutathione transferase (GST), and crotonase) were compared with curated functional hierarchies and structure. As expected, network topology differs, depending on edge metric; comparison of topologies provides valuable information on structure/function relationships. Subnetworks based on active site similarity correlate with known functional hierarchies at a single edge threshold more often than sequence- or structure-based networks. Sequence- and structure-based networks are useful for identifying sequence and domain similarities and differences; therefore, it is important to consider the clustering goal before deciding appropriate edge metric. Further, conserved active site residues identified in enolase and GST active site subnetworks correspond with published functionally important residues. Extension of this analysis yields predictions of functionally determinant residues for GST subgroups. These results support the hypothesis that active site similarity-based networks reveal clusters that share functional details and lay the foundation for capturing functionally relevant hierarchies using an approach that is both automatable and can deliver greater precision in function annotation than current similarity-based methods. © 2015 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity

PubMed Central

Leuthaeuser, Janelle B; Knutson, Stacy T; Kumar, Kiran; Babbitt, Patricia C; Fetrow, Jacquelyn S

2015-01-01

The development of accurate protein function annotation methods has emerged as a major unsolved biological problem. Protein similarity networks, one approach to function annotation via annotation transfer, group proteins into similarity-based clusters. An underlying assumption is that the edge metric used to identify such clusters correlates with functional information. In this contribution, this assumption is evaluated by observing topologies in similarity networks using three different edge metrics: sequence (BLAST), structure (TM-Align), and active site similarity (active site profiling, implemented in DASP). Network topologies for four well-studied protein superfamilies (enolase, peroxiredoxin (Prx), glutathione transferase (GST), and crotonase) were compared with curated functional hierarchies and structure. As expected, network topology differs, depending on edge metric; comparison of topologies provides valuable information on structure/function relationships. Subnetworks based on active site similarity correlate with known functional hierarchies at a single edge threshold more often than sequence- or structure-based networks. Sequence- and structure-based networks are useful for identifying sequence and domain similarities and differences; therefore, it is important to consider the clustering goal before deciding appropriate edge metric. Further, conserved active site residues identified in enolase and GST active site subnetworks correspond with published functionally important residues. Extension of this analysis yields predictions of functionally determinant residues for GST subgroups. These results support the hypothesis that active site similarity-based networks reveal clusters that share functional details and lay the foundation for capturing functionally relevant hierarchies using an approach that is both automatable and can deliver greater precision in function annotation than current similarity-based methods. PMID:26073648

Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics

NASA Astrophysics Data System (ADS)

Lamoš, Martin; Mareček, Radek; Slavíček, Tomáš; Mikl, Michal; Rektor, Ivan; Jan, Jiří

2018-06-01

Objective. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. Approach. The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component’s time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. Main results. We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. Significance. Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.

Local Network-Level Integration Mediates Effects of Transcranial Alternating Current Stimulation.

PubMed

Fuscà, Marco; Ruhnau, Philipp; Neuling, Toralf; Weisz, Nathan

2018-05-01

Transcranial alternating current stimulation (tACS) has been proposed as a tool to draw causal inferences on the role of oscillatory activity in cognitive functioning and has the potential to induce long-term changes in cerebral networks. However, effectiveness of tACS underlies high variability and dependencies, which, as previous modeling works have suggested, may be mediated by local and network-level brain states. We used magnetoencephalography to record brain activity from 17 healthy participants at rest as they kept their eyes open (EO) or eyes closed (EC) while being stimulated with sham, weak, or strong alpha-tACS using a montage commonly assumed to target occipital areas. We reconstructed the activity of sources in all stimulation conditions by means of beamforming. The analysis of resting-state brain activity revealed an interaction of the external stimulation with the endogenous alpha power increase from EO to EC. This interaction was localized to the posterior cingulate, a region remote from occipital cortex. This suggests state-dependent (EO vs. EC) long-range effects of tACS. In a follow-up analysis of this online-tACS effect, we find evidence that this state-dependency effect is mediated by functional network changes: connection strength from the precuneus was significantly correlated with the state-dependency effect in the posterior cingulate during tACS. No analogous correlation could be found for alpha power modulations in occipital cortex. Altogether, this is the first strong evidence to illustrate how functional network architectures can shape tACS effects.

Connectivity, excitability and activity patterns in neuronal networks

NASA Astrophysics Data System (ADS)

le Feber, Joost; Stoyanova, Irina I.; Chiappalone, Michela

2014-06-01

Extremely synchronized firing patterns such as those observed in brain diseases like epilepsy may result from excessive network excitability. Although network excitability is closely related to (excitatory) connectivity, a direct measure for network excitability remains unavailable. Several methods currently exist for estimating network connectivity, most of which are related to cross-correlation. An example is the conditional firing probability (CFP) analysis which calculates the pairwise probability (CFPi,j) that electrode j records an action potential at time t = τ, given that electrode i recorded a spike at t = 0. However, electrode i often records multiple spikes within the analysis interval, and CFP values are biased by the on-going dynamic state of the network. Here we show that in a linear approximation this bias may be removed by deconvoluting CFPi,j with the autocorrelation of i (i.e. CFPi,i), to obtain the single pulse response (SPRi,j)—the average response at electrode j to a single spike at electrode i. Thus, in a linear system SPRs would be independent of the dynamic network state. Nonlinear components of synaptic transmission, such as facilitation and short term depression, will however still affect SPRs. Therefore SPRs provide a clean measure of network excitability. We used carbachol and ghrelin to moderately activate cultured cortical networks to affect their dynamic state. Both neuromodulators transformed the bursting firing patterns of the isolated networks into more dispersed firing. We show that the influence of the dynamic state on SPRs is much smaller than the effect on CFPs, but not zero. The remaining difference reflects the alteration in network excitability. We conclude that SPRs are less contaminated by the dynamic network state and that mild excitation may decrease network excitability, possibly through short term synaptic depression.

Overlapping Networks Engaged during Spoken Language Production and Its Cognitive Control

PubMed Central

Wise, Richard J.S.; Mehta, Amrish; Leech, Robert

2014-01-01

Spoken language production is a complex brain function that relies on large-scale networks. These include domain-specific networks that mediate language-specific processes, as well as domain-general networks mediating top-down and bottom-up attentional control. Language control is thought to involve a left-lateralized fronto-temporal-parietal (FTP) system. However, these regions do not always activate for language tasks and similar regions have been implicated in nonlinguistic cognitive processes. These inconsistent findings suggest that either the left FTP is involved in multidomain cognitive control or that there are multiple spatially overlapping FTP systems. We present evidence from an fMRI study using multivariate analysis to identify spatiotemporal networks involved in spoken language production in humans. We compared spoken language production (Speech) with multiple baselines, counting (Count), nonverbal decision (Decision), and “rest,” to pull apart the multiple partially overlapping networks that are involved in speech production. A left-lateralized FTP network was activated during Speech and deactivated during Count and nonverbal Decision trials, implicating it in cognitive control specific to sentential spoken language production. A mirror right-lateralized FTP network was activated in the Count and Decision trials, but not Speech. Importantly, a second overlapping left FTP network showed relative deactivation in Speech. These three networks, with distinct time courses, overlapped in the left parietal lobe. Contrary to the standard model of the left FTP as being dominant for speech, we revealed a more complex pattern within the left FTP, including at least two left FTP networks with competing functional roles, only one of which was activated in speech production. PMID:24966373

Overlapping networks engaged during spoken language production and its cognitive control.

PubMed

Geranmayeh, Fatemeh; Wise, Richard J S; Mehta, Amrish; Leech, Robert

2014-06-25

Spoken language production is a complex brain function that relies on large-scale networks. These include domain-specific networks that mediate language-specific processes, as well as domain-general networks mediating top-down and bottom-up attentional control. Language control is thought to involve a left-lateralized fronto-temporal-parietal (FTP) system. However, these regions do not always activate for language tasks and similar regions have been implicated in nonlinguistic cognitive processes. These inconsistent findings suggest that either the left FTP is involved in multidomain cognitive control or that there are multiple spatially overlapping FTP systems. We present evidence from an fMRI study using multivariate analysis to identify spatiotemporal networks involved in spoken language production in humans. We compared spoken language production (Speech) with multiple baselines, counting (Count), nonverbal decision (Decision), and "rest," to pull apart the multiple partially overlapping networks that are involved in speech production. A left-lateralized FTP network was activated during Speech and deactivated during Count and nonverbal Decision trials, implicating it in cognitive control specific to sentential spoken language production. A mirror right-lateralized FTP network was activated in the Count and Decision trials, but not Speech. Importantly, a second overlapping left FTP network showed relative deactivation in Speech. These three networks, with distinct time courses, overlapped in the left parietal lobe. Contrary to the standard model of the left FTP as being dominant for speech, we revealed a more complex pattern within the left FTP, including at least two left FTP networks with competing functional roles, only one of which was activated in speech production. Copyright © 2014 Geranmayeh et al.

Health-Related Messages about Physical Activity Promotion: An Analysis of Photographs on Social Networking Sites of Universities

ERIC Educational Resources Information Center

Martínez-Bello, Vladimir E.; Martínez-Rojas, Ángela; Molina-García, Javier

2017-01-01

The main aim of this study was to examine how different physical activity domains are represented on the official social media sites of Spanish universities, through a content analysis of the photographs. Our results show that the representation of different physical activity domains is not balanced. While the analysed images do promote a message…

Network Analysis of Earth's Co-Evolving Geosphere and Biosphere

NASA Astrophysics Data System (ADS)

Hazen, R. M.; Eleish, A.; Liu, C.; Morrison, S. M.; Meyer, M.; Consortium, K. D.

2017-12-01

A fundamental goal of Earth science is the deep understanding of Earth's dynamic, co-evolving geosphere and biosphere through deep time. Network analysis of geo- and bio- `big data' provides an interactive, quantitative, and predictive visualization framework to explore complex and otherwise hidden high-dimension features of diversity, distribution, and change in the evolution of Earth's geochemistry, mineralogy, paleobiology, and biochemistry [1]. Networks also facilitate quantitative comparison of different geological time periods, tectonic settings, and geographical regions, as well as different planets and moons, through network metrics, including density, centralization, diameter, and transitivity.We render networks by employing data related to geographical, paragenetic, environmental, or structural relationships among minerals, fossils, proteins, and microbial taxa. An important recent finding is that the topography of many networks reflects parameters not explicitly incorporated in constructing the network. For example, networks for minerals, fossils, and protein structures reveal embedded qualitative time axes, with additional network geometries possibly related to extinction and/or other punctuation events (see Figure). Other axes related to chemical activities and volatile fugacities, as well as pressure and/or depth of formation, may also emerge from network analysis. These patterns provide new insights into the way planets evolve, especially Earth's co-evolving geosphere and biosphere. 1. Morrison, S.M. et al. (2017) Network analysis of mineralogical systems. American Mineralogist 102, in press. Figure Caption: A network of Phanerozoic Era fossil animals from the past 540 million years includes blue, red, and black circles (nodes) representing family-level taxa and grey lines (links) between coexisting families. Age information was not used in the construction of this network; nevertheless an intrinsic timeline is embedded in the network topology. In addition, two mass extinction events appear as "pinch points" in the network.

ICA model order selection of task co-activation networks.

PubMed

Ray, Kimberly L; McKay, D Reese; Fox, Peter M; Riedel, Michael C; Uecker, Angela M; Beckmann, Christian F; Smith, Stephen M; Fox, Peter T; Laird, Angela R

2013-01-01

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders.

ICA model order selection of task co-activation networks

PubMed Central

Ray, Kimberly L.; McKay, D. Reese; Fox, Peter M.; Riedel, Michael C.; Uecker, Angela M.; Beckmann, Christian F.; Smith, Stephen M.; Fox, Peter T.; Laird, Angela R.

2013-01-01

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders. PMID:24339802

Recurrent Network models of sequence generation and memory

PubMed Central

Rajan, Kanaka; Harvey, Christopher D; Tank, David W

2016-01-01

SUMMARY Sequential activation of neurons is a common feature of network activity during a variety of behaviors, including working memory and decision making. Previous network models for sequences and memory emphasized specialized architectures in which a principled mechanism is pre-wired into their connectivity. Here, we demonstrate that starting from random connectivity and modifying a small fraction of connections, a largely disordered recurrent network can produce sequences and implement working memory efficiently. We use this process, called Partial In-Network training (PINning), to model and match cellular-resolution imaging data from the posterior parietal cortex during a virtual memory-guided two-alternative forced choice task [Harvey, Coen and Tank, 2012]. Analysis of the connectivity reveals that sequences propagate by the cooperation between recurrent synaptic interactions and external inputs, rather than through feedforward or asymmetric connections. Together our results suggest that neural sequences may emerge through learning from largely unstructured network architectures. PMID:26971945

Mathematical analysis techniques for modeling the space network activities

NASA Technical Reports Server (NTRS)

Foster, Lisa M.

1992-01-01

The objective of the present work was to explore and identify mathematical analysis techniques, and in particular, the use of linear programming. This topic was then applied to the Tracking and Data Relay Satellite System (TDRSS) in order to understand the space network better. Finally, a small scale version of the system was modeled, variables were identified, data was gathered, and comparisons were made between actual and theoretical data.

Modeling of intracerebral interictal epileptic discharges: Evidence for network interactions.

PubMed

Meesters, Stephan; Ossenblok, Pauly; Colon, Albert; Wagner, Louis; Schijns, Olaf; Boon, Paul; Florack, Luc; Fuster, Andrea

2018-06-01

The interictal epileptic discharges (IEDs) occurring in stereotactic EEG (SEEG) recordings are in general abundant compared to ictal discharges, but difficult to interpret due to complex underlying network interactions. A framework is developed to model these network interactions. To identify the synchronized neuronal activity underlying the IEDs, the variation in correlation over time of the SEEG signals is related to the occurrence of IEDs using the general linear model. The interdependency is assessed of the brain areas that reflect highly synchronized neural activity by applying independent component analysis, followed by cluster analysis of the spatial distributions of the independent components. The spatiotemporal interactions of the spike clusters reveal the leading or lagging of brain areas. The analysis framework was evaluated for five successfully operated patients, showing that the spike cluster that was related to the MRI-visible brain lesions coincided with the seizure onset zone. The additional value of the framework was demonstrated for two more patients, who were MRI-negative and for whom surgery was not successful. A network approach is promising in case of complex epilepsies. Analysis of IEDs is considered a valuable addition to routine review of SEEG recordings, with the potential to increase the success rate of epilepsy surgery. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Single-Molecule Studies of Actin Assembly and Disassembly Factors

PubMed Central

Smith, Benjamin A.; Gelles, Jeff; Goode, Bruce L.

2014-01-01

The actin cytoskeleton is very dynamic and highly regulated by multiple associated proteins in vivo. Understanding how this system of proteins functions in the processes of actin network assembly and disassembly requires methods to dissect the mechanisms of activity of individual factors and of multiple factors acting in concert. The advent of single-filament and single-molecule fluorescence imaging methods has provided a powerful new approach to discovering actin-regulatory activities and obtaining direct, quantitative insights into the pathways of molecular interactions that regulate actin network architecture and dynamics. Here we describe techniques for acquisition and analysis of single-molecule data, applied to the novel challenges of studying the filament assembly and disassembly activities of actin-associated proteins in vitro. We discuss the advantages of single-molecule analysis in directly visualizing the order of molecular events, measuring the kinetic rates of filament binding and dissociation, and studying the coordination among multiple factors. The methods described here complement traditional biochemical approaches in elucidating actin-regulatory mechanisms in reconstituted filamentous networks. PMID:24630103

Altered effective connectivity of default model brain network underlying amnestic MCI

NASA Astrophysics Data System (ADS)

Yan, Hao; Wang, Yonghui; Tian, Jie

2012-02-01

Mild cognitive impairment (MCI) is the transitional, heterogeneous continuum from healthy elderly to Alzheimer's disease (AD). Previous studies have shown that brain functional activity in the default mode network (DMN) is impaired in MCI patients. However, the altered effective connectivity of the DMN in MCI patients remains largely unknown. The present study combined an independent component analysis (ICA) approach with Granger causality analysis (mGCA) to investigate the effective connectivity within the DMN in 12 amnestic MCI patients and 12 age-matched healthy elderly. Compared to the healthy control, the MCI exhibited decreased functional activity in the posterior DMN regions, as well as a trend towards activity increases in anterior DMN regions. Results from mGCA further supported this conclusion that the causal influence projecting to the precuneus/PCC became much weaker in MCI, while stronger interregional interactions emerged within the frontal-parietal cortices. These findings suggested that abnormal effective connectivity within the DMN may elucidate the dysfunctional and compensatory processes in MCI brain networks.

Research in Network Management Techniques for Tactical Data Communications Networks.

DTIC Science & Technology

1982-09-01

COMPUTER COMMUNICATIONS US A.RMY (CECOM) V September 1980 to August 1982 Principal Investigatoi Robert Boorstyn Aaron Kershenbaum DTIC Basil Niaglaris Philip...COMMUNICATIONS US ARMY (CECOM) September 1980 to August 1982 Principal Investigators: Robert Boorstyn Aaron Kershenbaum Basil Maglaris Philip Sarachik...TABLE OF CONTENTS Summary of Report Personnel Activities Research Reports / , A. Packet Radio Networks A.1 Throughput Analysis of Multihop Packet

Reverse engineering biological networks :applications in immune responses to bio-toxins.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martino, Anthony A.; Sinclair, Michael B.; Davidson, George S.

Our aim is to determine the network of events, or the regulatory network, that defines an immune response to a bio-toxin. As a model system, we are studying T cell regulatory network triggered through tyrosine kinase receptor activation using a combination of pathway stimulation and time-series microarray experiments. Our approach is composed of five steps (1) microarray experiments and data error analysis, (2) data clustering, (3) data smoothing and discretization, (4) network reverse engineering, and (5) network dynamics analysis and fingerprint identification. The technological outcome of this study is a suite of experimental protocols and computational tools that reverse engineermore » regulatory networks provided gene expression data. The practical biological outcome of this work is an immune response fingerprint in terms of gene expression levels. Inferring regulatory networks from microarray data is a new field of investigation that is no more than five years old. To the best of our knowledge, this work is the first attempt that integrates experiments, error analyses, data clustering, inference, and network analysis to solve a practical problem. Our systematic approach of counting, enumeration, and sampling networks matching experimental data is new to the field of network reverse engineering. The resulting mathematical analyses and computational tools lead to new results on their own and should be useful to others who analyze and infer networks.« less

Top-down and bottom-up attention-to-memory: mapping functional connectivity in two distinct networks that underlie cued and uncued recognition memory.

PubMed

Burianová, Hana; Ciaramelli, Elisa; Grady, Cheryl L; Moscovitch, Morris

2012-11-15

The objective of this study was to examine the functional connectivity of brain regions active during cued and uncued recognition memory to test the idea that distinct networks would underlie these memory processes, as predicted by the attention-to-memory (AtoM) hypothesis. The AtoM hypothesis suggests that dorsal parietal cortex (DPC) allocates effortful top-down attention to memory retrieval during cued retrieval, whereas ventral parietal cortex (VPC) mediates spontaneous bottom-up capture of attention by memory during uncued retrieval. To identify networks associated with these two processes, we conducted a functional connectivity analysis of a left DPC and a left VPC region, both identified by a previous analysis of task-related regional activations. We hypothesized that the two parietal regions would be functionally connected with distinct neural networks, reflecting their engagement in the differential mnemonic processes. We found two spatially dissociated networks that overlapped only in the precuneus. During cued trials, DPC was functionally connected with dorsal attention areas, including the superior parietal lobules, right precuneus, and premotor cortex, as well as relevant memory areas, such as the left hippocampus and the middle frontal gyri. During uncued trials, VPC was functionally connected with ventral attention areas, including the supramarginal gyrus, cuneus, and right fusiform gyrus, as well as the parahippocampal gyrus. In addition, activity in the DPC network was associated with faster response times for cued retrieval. This is the first study to show a dissociation of the functional connectivity of posterior parietal regions during episodic memory retrieval, characterized by a top-down AtoM network involving DPC and a bottom-up AtoM network involving VPC. Copyright © 2012 Elsevier Inc. All rights reserved.

Mapping Multiplex Hubs in Human Functional Brain Networks

PubMed Central

De Domenico, Manlio; Sasai, Shuntaro; Arenas, Alex

2016-01-01

Typical brain networks consist of many peripheral regions and a few highly central ones, i.e., hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. First, we show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. We then demonstrate that hubs in the multiplex network, in general different from those ones obtained after discarding or aggregating the measured signals as usual, provide a more accurate map of brain's most important functional regions, allowing to distinguish between healthy and schizophrenic populations better than conventional network approaches. PMID:27471443

Educational network comparative analysis of small groups: Short- and long-term communications

NASA Astrophysics Data System (ADS)

Berg, D. B.; Zvereva, O. M.; Nazarova, Yu. Yu.; Chepurov, E. G.; Kokovin, A. V.; Ranyuk, S. V.

2017-11-01

The present study is devoted to the discussion of small group communication network structures. These communications were observed in student groups, where actors were united with a regular educational activity. The comparative analysis was carried out for networks of short-term (1 hour) and long-term (4 weeks) communications, it was based on seven structural parameters, and consisted of two stages. At the first stage, differences between the network graphs were examined, and the random corresponding Bernoulli graphs were built. At the second stage, revealed differences were compared. Calculations were performed using UCINET software framework. It was found out that networks of long-term and short-term communications are quite different: the structure of a short-term communication network is close to a random one, whereas the most of long-term communication network parameters differ from the corresponding random ones by more than 30%. This difference can be explained by strong "noisiness" of a short-term communication network, and the lack of social in it.

Robust Long-Range Coordination of Spontaneous Neural Activity in Waking, Sleep and Anesthesia.

PubMed

Liu, Xiao; Yanagawa, Toru; Leopold, David A; Fujii, Naotaka; Duyn, Jeff H

2015-09-01

Although the emerging field of functional connectomics relies increasingly on the analysis of spontaneous fMRI signal covariation to infer the spatial fingerprint of the brain's large-scale functional networks, the nature of the underlying neuro-electrical activity remains incompletely understood. In part, this lack in understanding owes to the invasiveness of electrophysiological acquisition, the difficulty in their simultaneous recording over large cortical areas, and the absence of fully established methods for unbiased extraction of network information from these data. Here, we demonstrate a novel, data-driven approach to analyze spontaneous signal variations in electrocorticographic (ECoG) recordings from nearly entire hemispheres of macaque monkeys. Based on both broadband analysis and analysis of specific frequency bands, the ECoG signals were found to co-vary in patterns that resembled the fMRI networks reported in previous studies. The extracted patterns were robust against changes in consciousness associated with sleep and anesthesia, despite profound changes in intrinsic characteristics of the raw signals, including their spectral signatures. These results suggest that the spatial organization of large-scale brain networks results from neural activity with a broadband spectral feature and is a core aspect of the brain's physiology that does not depend on the state of consciousness. Published by Oxford University Press 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Allosteric mechanism of quinoline inhibitors for HIV RT-associated RNase with MD simulation and dynamics fluctuation network.

PubMed

Cai, Yi; Liu, Hao; Chen, Haifeng

2018-03-01

The human immunodeficiency virus (HIV) is a retrovirus which infects T lymphocyte of human body and causes immunodeficiency. Reverse transcriptase inhibitors (RTIs) can inhibit some functions of RT, preventing virus synthesis (double-stranded DNA), so that HIV virus replication can be reduced. Experimental results indicate a series of benzimidazole-based inhibitors which target HIV RT-associated RNase to inhibit the reverse transcription of HIV virus. However, the allosteric mechanism is still unclear. Here, molecular dynamics simulations and dynamics fluctuation network analysis were used to reveal the binding mode between the inhibitors and RT-associated RNase. The most active molecule has more hydrophobic and electrostatic interactions than the less active inhibitor. Dynamics correlation network analysis indicates that the most active inhibitor perturbs the network of RT-associated RNase and decreases the correlation of nodes. 3D-QSAR model suggests that two robust and reliable models were constructed and validated by independent test set. 3D-QSAR model also shows that bulky negatively charged or hydrophilic substituent is favorable to bioactivity. These results reveal the allosteric mechanism of quinoline inhibitors and help to improve the bioactivity. © 2017 John Wiley & Sons A/S.

Innovation diffusion on time-varying activity driven networks

NASA Astrophysics Data System (ADS)

Rizzo, Alessandro; Porfiri, Maurizio

2016-01-01

Since its introduction in the 1960s, the theory of innovation diffusion has contributed to the advancement of several research fields, such as marketing management and consumer behavior. The 1969 seminal paper by Bass [F.M. Bass, Manag. Sci. 15, 215 (1969)] introduced a model of product growth for consumer durables, which has been extensively used to predict innovation diffusion across a range of applications. Here, we propose a novel approach to study innovation diffusion, where interactions among individuals are mediated by the dynamics of a time-varying network. Our approach is based on the Bass' model, and overcomes key limitations of previous studies, which assumed timescale separation between the individual dynamics and the evolution of the connectivity patterns. Thus, we do not hypothesize homogeneous mixing among individuals or the existence of a fixed interaction network. We formulate our approach in the framework of activity driven networks to enable the analysis of the concurrent evolution of the interaction and individual dynamics. Numerical simulations offer a systematic analysis of the model behavior and highlight the role of individual activity on market penetration when targeted advertisement campaigns are designed, or a competition between two different products takes place.

State-level legal preparedness for nuclear and radiological emergencies in the U.S.: a network analysis of state laws and regulations.

PubMed

Guclu, Hasan; Ferrell Bjerke, Elizabeth; Galvan, Jared; Sweeney, Patricia; Potter, Margaret A

2014-01-01

This study explored if and to what extent the laws of U.S. states mirrored the U.S. federal laws for responding to nuclear-radiological emergencies (NREs). Emergency laws from a 12-state sample and the federal government were retrieved and translated into numeric codes representing acting agents, their partner agents, and the purposes of activity in terms of preparedness, response, and recovery. We used network analysis to explore the relationships among agents in terms of legally directed NRE activities. States' legal networks for NREs appear as not highly inclusive, involving an average of 28% of agents among those specified in the federal laws. Certain agents are highly central in NRE networks, so that their capacity and effectiveness might strongly influence an NRE response. State-level lawmakers and planners might consider whether or not greater inclusion of agents, modeled on the federal government laws, would enhance their NRE laws and if more agents should be engaged in planning and policy-making for NRE incidents. Further research should explore if and to what extent legislated NRE directives impose constraints on practical response activities including emergency planning.

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli.

PubMed

Antiqueira, Lucas; Janga, Sarath Chandra; Costa, Luciano da Fontoura

2012-11-01

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.

Meta-connectomics: human brain network and connectivity meta-analyses.

PubMed

Crossley, N A; Fox, P T; Bullmore, E T

2016-04-01

Abnormal brain connectivity or network dysfunction has been suggested as a paradigm to understand several psychiatric disorders. We here review the use of novel meta-analytic approaches in neuroscience that go beyond a summary description of existing results by applying network analysis methods to previously published studies and/or publicly accessible databases. We define this strategy of combining connectivity with other brain characteristics as 'meta-connectomics'. For example, we show how network analysis of task-based neuroimaging studies has been used to infer functional co-activation from primary data on regional activations. This approach has been able to relate cognition to functional network topology, demonstrating that the brain is composed of cognitively specialized functional subnetworks or modules, linked by a rich club of cognitively generalized regions that mediate many inter-modular connections. Another major application of meta-connectomics has been efforts to link meta-analytic maps of disorder-related abnormalities or MRI 'lesions' to the complex topology of the normative connectome. This work has highlighted the general importance of network hubs as hotspots for concentration of cortical grey-matter deficits in schizophrenia, Alzheimer's disease and other disorders. Finally, we show how by incorporating cellular and transcriptional data on individual nodes with network models of the connectome, studies have begun to elucidate the microscopic mechanisms underpinning the macroscopic organization of whole-brain networks. We argue that meta-connectomics is an exciting field, providing robust and integrative insights into brain organization that will likely play an important future role in consolidating network models of psychiatric disorders.

Creative constraints: Brain activity and network dynamics underlying semantic interference during idea production.

PubMed

Beaty, Roger E; Christensen, Alexander P; Benedek, Mathias; Silvia, Paul J; Schacter, Daniel L

2017-03-01

Functional neuroimaging research has recently revealed brain network interactions during performance on creative thinking tasks-particularly among regions of the default and executive control networks-but the cognitive mechanisms related to these interactions remain poorly understood. Here we test the hypothesis that the executive control network can interact with the default network to inhibit salient conceptual knowledge (i.e., pre-potent responses) elicited from memory during creative idea production. Participants studied common noun-verb pairs and were given a cued-recall test with corrective feedback to strengthen the paired association in memory. They then completed a verb generation task that presented either a previously studied noun (high-constraint) or an unstudied noun (low-constraint), and were asked to "think creatively" while searching for a novel verb to relate to the presented noun. Latent Semantic Analysis of verbal responses showed decreased semantic distance values in the high-constraint (i.e., interference) condition, which corresponded to increased neural activity within regions of the default (posterior cingulate cortex and bilateral angular gyri), salience (right anterior insula), and executive control (left dorsolateral prefrontal cortex) networks. Independent component analysis of intrinsic functional connectivity networks extended this finding by revealing differential interactions among these large-scale networks across the task conditions. The results suggest that interactions between the default and executive control networks underlie response inhibition during constrained idea production, providing insight into specific neurocognitive mechanisms supporting creative cognition. Copyright © 2017 Elsevier Inc. All rights reserved.

Epidemiologic research topics in Germany: a keyword network analysis of 2014 DGEpi conference presentations.

PubMed

Peter, Raphael Simon; Brehme, Torben; Völzke, Henry; Muche, Rainer; Rothenbacher, Dietrich; Büchele, Gisela

2016-06-01

Knowledge of epidemiologic research topics as well as trends is useful for scientific societies, researchers and funding agencies. In recent years researchers recognized the usefulness of keyword network analysis for visualizing and analyzing scientific research topics. Therefore, we applied keyword network analysis to present an overview of current epidemiologic research topics in Germany. Accepted submissions to the 9th annual congress of the German Society for Epidemiology (DGEpi) in 2014 were used as data source. Submitters had to choose one of 19 subject areas, and were ask to provide a title, structured abstract, names of authors along with their affiliations, and a list of freely selectable keywords. Keywords had been provided for 262 (82 %) submissions, 1030 keywords in total. Overall the most common keywords were: "migration" (18 times), "prevention" (15 times), followed by "children", "cohort study", "physical activity", and "secondary data analysis" (11 times each). Some keywords showed a certain concentration under one specific subject area, e.g. "migration" with 8 of 18 in social epidemiology or "breast cancer" with 4 of 7 in cancer epidemiology. While others like "physical activity" were equally distributed over multiple subject areas (cardiovascular & metabolic diseases, ageing, methods, paediatrics, prevention & health service research). This keyword network analysis demonstrated the high diversity of epidemiologic research topics with a large number of distinct keywords as presented at the annual conference of the DGEpi.

Meditation-related activations are modulated by the practices needed to obtain it and by the expertise: an ALE meta-analysis study

PubMed Central

Tomasino, Barbara; Fregona, Sara; Skrap, Miran; Fabbro, Franco

2013-01-01

The brain network governing meditation has been studied using a variety of meditation practices and techniques practices eliciting different cognitive processes (e.g., silence, attention to own body, sense of joy, mantras, etc.). It is very possible that different practices of meditation are subserved by largely, if not entirely, disparate brain networks. This assumption was tested by conducting an activation likelihood estimation (ALE) meta-analysis of meditation neuroimaging studies, which assessed 150 activation foci from 24 experiments. Different ALE meta-analyses were carried out. One involved the subsets of studies involving meditation induced through exercising focused attention (FA). The network included clusters bilaterally in the medial gyrus, the left superior parietal lobe, the left insula and the right supramarginal gyrus (SMG). A second analysis addressed the studies involving meditation states induced by chanting or by repetition of words or phrases, known as “mantra.” This type of practice elicited a cluster of activity in the right SMG, the SMA bilaterally and the left postcentral gyrus. Furthermore, the last analyses addressed the effect of meditation experience (i.e., short- vs. long-term meditators). We found that frontal activation was present for short-term, as compared with long-term experience meditators, confirming that experts are better enabled to sustain attentional focus, rather recruiting the right SMG and concentrating on aspects involving disembodiment. PMID:23316154

Burstiness and tie activation strategies in time-varying social networks.

PubMed

Ubaldi, Enrico; Vezzani, Alessandro; Karsai, Márton; Perra, Nicola; Burioni, Raffaella

2017-04-13

The recent developments in the field of social networks shifted the focus from static to dynamical representations, calling for new methods for their analysis and modelling. Observations in real social systems identified two main mechanisms that play a primary role in networks' evolution and influence ongoing spreading processes: the strategies individuals adopt when selecting between new or old social ties, and the bursty nature of the social activity setting the pace of these choices. We introduce a time-varying network model accounting both for ties selection and burstiness and we analytically study its phase diagram. The interplay of the two effects is non trivial and, interestingly, the effects of burstiness might be suppressed in regimes where individuals exhibit a strong preference towards previously activated ties. The results are tested against numerical simulations and compared with two empirical datasets with very good agreement. Consequently, the framework provides a principled method to classify the temporal features of real networks, and thus yields new insights to elucidate the effects of social dynamics on spreading processes.

Link Prediction in Evolving Networks Based on Popularity of Nodes.

PubMed

Wang, Tong; He, Xing-Sheng; Zhou, Ming-Yang; Fu, Zhong-Qian

2017-08-02

Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict missing edges or identify the spurious edges. The key issue of link prediction is to estimate the likelihood of potential links in networks. Most classical static-structure based methods ignore the temporal aspects of networks, limited by the time-varying features, such approaches perform poorly in evolving networks. In this paper, we propose a hypothesis that the ability of each node to attract links depends not only on its structural importance, but also on its current popularity (activeness), since active nodes have much more probability to attract future links. Then a novel approach named popularity based structural perturbation method (PBSPM) and its fast algorithm are proposed to characterize the likelihood of an edge from both existing connectivity structure and current popularity of its two endpoints. Experiments on six evolving networks show that the proposed methods outperform state-of-the-art methods in accuracy and robustness. Besides, visual results and statistical analysis reveal that the proposed methods are inclined to predict future edges between active nodes, rather than edges between inactive nodes.

Node fingerprinting: an efficient heuristic for aligning biological networks.

PubMed

Radu, Alex; Charleston, Michael

2014-10-01

With the continuing increase in availability of biological data and improvements to biological models, biological network analysis has become a promising area of research. An emerging technique for the analysis of biological networks is through network alignment. Network alignment has been used to calculate genetic distance, similarities between regulatory structures, and the effect of external forces on gene expression, and to depict conditional activity of expression modules in cancer. Network alignment is algorithmically complex, and therefore we must rely on heuristics, ideally as efficient and accurate as possible. The majority of current techniques for network alignment rely on precomputed information, such as with protein sequence alignment, or on tunable network alignment parameters, which may introduce an increased computational overhead. Our presented algorithm, which we call Node Fingerprinting (NF), is appropriate for performing global pairwise network alignment without precomputation or tuning, can be fully parallelized, and is able to quickly compute an accurate alignment between two biological networks. It has performed as well as or better than existing algorithms on biological and simulated data, and with fewer computational resources. The algorithmic validation performed demonstrates the low computational resource requirements of NF.

Visual behavior characterization for intrusion and misuse detection

NASA Astrophysics Data System (ADS)

Erbacher, Robert F.; Frincke, Deborah

2001-05-01

As computer and network intrusions become more and more of a concern, the need for better capabilities, to assist in the detection and analysis of intrusions also increase. System administrators typically rely on log files to analyze usage and detect misuse. However, as a consequence of the amount of data collected by each machine, multiplied by the tens or hundreds of machines under the system administrator's auspices, the entirety of the data available is neither collected nor analyzed. This is compounded by the need to analyze network traffic data as well. We propose a methodology for analyzing network and computer log information visually based on the analysis of the behavior of the users. Each user's behavior is the key to determining their intent and overriding activity, whether they attempt to hide their actions or not. Proficient hackers will attempt to hide their ultimate activities, which hinders the reliability of log file analysis. Visually analyzing the users''s behavior however, is much more adaptable and difficult to counteract.

Evolution of the research collaboration network in a productive department.

PubMed

Katerndahl, David

2012-02-01

Understanding collaboration networks can facilitate the research growth of new or developing departments. The purpose of this study was to use social network analysis to understand how the research collaboration network evolved within a productive department. Over a 13-year period, a departmental faculty completed an annual survey describing their research collaborations. Data were analyzed using social network analysis. Network measures focused on connectedness, distance, groupings and heterogeneity of distribution, while measures for the research director and external collaboration focused on centrality and roles within the network. Longitudinal patterns of network collaboration were assessed using Simulation Investigation for Empirical Network Analysis software (University of Groningen, Groningen, Netherlands). Based upon the number of active research projects, research development can be divided into three phases. The initial development phase was characterized by increasing centralization and collaboration focused within a single subject area. During the maintenance phase, measures went through cycles, possibly because of changes in faculty composition. While the research director was not a 'key player' within the network during the first several years, external collaboration played a central role during all phases. Longitudinal analysis found that forming ties was more likely when the opportunity for network closure existed and when those around you are principal investigators (PIs). Initial development of research relied heavily upon a centralized network involving external collaboration; a central position of the research director during research development was not important. Changes in collaboration depended upon faculty gender and tenure track as well as transitivity and the 'popularity of PIs'. © 2011 Blackwell Publishing Ltd.

Brain network response underlying decisions about abstract reinforcers.

PubMed

Mills-Finnerty, Colleen; Hanson, Catherine; Hanson, Stephen Jose

2014-12-01

Decision making studies typically use tasks that involve concrete action-outcome contingencies, in which subjects do something and get something. No studies have addressed decision making involving abstract reinforcers, where there are no action-outcome contingencies and choices are entirely hypothetical. The present study examines these kinds of choices, as well as whether the same biases that exist for concrete reinforcer decisions, specifically framing effects, also apply during abstract reinforcer decisions. We use both General Linear Model as well as Bayes network connectivity analysis using the Independent Multi-sample Greedy Equivalence Search (IMaGES) algorithm to examine network response underlying choices for abstract reinforcers under positive and negative framing. We find for the first time that abstract reinforcer decisions activate the same network of brain regions as concrete reinforcer decisions, including the striatum, insula, anterior cingulate, and VMPFC, results that are further supported via comparison to a meta-analysis of decision making studies. Positive and negative framing activated different parts of this network, with stronger activation in VMPFC during negative framing and in DLPFC during positive, suggesting different decision making pathways depending on frame. These results were further clarified using connectivity analysis, which revealed stronger connections between anterior cingulate, insula, and accumbens during negative framing compared to positive. Taken together, these results suggest that not only do abstract reinforcer decisions rely on the same brain substrates as concrete reinforcers, but that the response underlying framing effects on abstract reinforcers also resemble those for concrete reinforcers, specifically increased limbic system connectivity during negative frames. Copyright © 2014 Elsevier Inc. All rights reserved.

A multivariate analysis of age-related differences in functional networks supporting conflict resolution.

PubMed

Salami, Alireza; Rieckmann, Anna; Fischer, Håkan; Bäckman, Lars

2014-02-01

Functional neuroimaging studies demonstrate age-related differences in recruitment of a large-scale attentional network during interference resolution, especially within dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC). These alterations in functional responses have been frequently observed despite equivalent task performance, suggesting age-related reallocation of neural resources, although direct evidence for a facilitating effect in aging is sparse. We used the multi-source interference task and multivariate partial-least-squares to investigate age-related differences in the neuronal signature of conflict resolution, and their behavioral implications in younger and older adults. There were interference-related increases in activity, involving fronto-parietal and basal ganglia networks that generalized across age. In addition an age-by-task interaction was observed within a distributed network, including DLPFC and ACC, with greater activity during interference in the old. Next, we combined brain-behavior and functional connectivity analyses to investigate whether compensatory brain changes were present in older adults, using DLPFC and ACC as regions of interest (i.e. seed regions). This analysis revealed two networks differentially related to performance across age groups. A structural analysis revealed age-related gray-matter losses in regions facilitating performance in the young, suggesting that functional reorganization may partly reflect structural alterations in aging. Collectively, these findings suggest that age-related structural changes contribute to reductions in the efficient recruitment of a youth-like interference network, which cascades into instantiation of a different network facilitating conflict resolution in elderly people. © 2013. Published by Elsevier Inc. All rights reserved.

Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation

PubMed Central

Li, Wenyuan; Liu, Chun-Chi; Zhang, Tong; Li, Haifeng; Waterman, Michael S.; Zhou, Xianghong Jasmine

2011-01-01

The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks. PMID:21698123

Organizational Analysis of the TIDES Project and the STAR-TIDES Network Using the 7-S Framework

DTIC Science & Technology

2013-04-01

data, provided some useful rec- ommendations.8 Since that time, TIDES has continued to grow and change. The present study was undertaken to update the...information across platforms and within the secure NDU network. For ex- ample, many contacts made by the Director are preserved within his Blackberry ...the active participation of STAR-TIDES network members, and to grow the network. 5. Skills Skills refers to the talents and abilities of the

[Mechanism of Tongsaimai tablet for atherosclerosis based on network pharmacology].

PubMed

Li, Na; Zhang, Xin-Zhuang; Wang, Yan-Ru; Cao, Liang; Ding, Gang; Wang, Zhen-Zhong; Xiao, Wei; Xu, Xiao-Jie

2016-05-01

Network pharmacology method was adopted in this study to explore the active compounds and mechanism of Tongsaimai tablets for atherosclerosis. In molecular docking and molecular-target protein network analysis, 97 molecules in Tongsaimai tablets showed good interaction with the atherosclerosis-related target protein (docking score ≥ 7), and 37 molecules of them could act on more than 2 targets (≥ 2) with higher betweenness, suggesting that these 37 molecules might be the main active compounds group in Tongsaimai tablets for atherosclerosis treatment. Furthermore, the predicted active compounds contained more flavonoids and saponins, reminding more attention should be paid on flavonoids and saponins in study of effective compounds and quality standards of Tongsaimai tablets. Targets network analysis showed that, the active compounds of Tongsaimai tablets could regulate inflammation, stabilize plaque, protect vascular endothelial cell, regulate blood lipid and inhibit blood coagulation through acting on the main 22 target proteins, such as Toll-like receptors (TLR1, TLR2), matrix metalloproteinase (MMP1, MMP2, MMP3, MMP9), angiotensin converting enzyme (ACE), leukotriene A4 hydrolase (LTA4-H), 5-lipoxidase (5-LOX), peroxisome proliferators-activated receptors (PPARα, PPARγ). These active compounds can participate in regulating different pathologic stages of atherosclerosis and thus treat atherosclerosis finally. This study revealed the main active compounds and possible mechanism of Tongsaimai tablets for treatment of atherosclerosis and meanwhile, verified the characteristics of multi-components, multi-targets and integral regulation for Tongsaimai tablets, providing theoretical references for the following systematic laboratory experiments on effective compounds and action mechanism of Tongsaimai Tablet. Copyright© by the Chinese Pharmaceutical Association.

Coronal Heating and the Magnetic Flux Content of the Network

NASA Technical Reports Server (NTRS)

Falconer, D. A.; Moore, R. L.; Porter, J. G.; Hathaway, D. H.; Rose, M. Franklin (Technical Monitor)

2001-01-01

Previously, from analysis of SOHO coronal images in combination with Kitt Peak magnetograms, we found that the quiet corona is the sum of two components: the large-scale corona and the coronal network. The large-scale corona consists of all coronal-temperature (T approximately 10(exp 6) K) structures larger than supergranules (greater than approximately 30,000 kilometers). The coronal network (1) consists of all coronal-temperature structures smaller than supergranules, (2) is rooted in and loosely traces the photospheric magnetic network, (3) has its brightest features seated on polarity dividing lines (neutral lines) in the network magnetic flux, and (4) produces only about 5% of the total coronal emission in quiet regions. The heating of the coronal network is apparently magnetic in origin. Here, from analysis of EIT coronal images of quiet regions in combination with magnetograms of the same quiet regions from SOHO/MDI and from Kitt Peak, we examine the other 95% of the quiet corona and its relation to the underlying magnetic network. We find: (1) Dividing the large-scale corona into its bright and dim halves divides the area into bright "continents" and dark "oceans" having spans of 2-4 supergranules. (2) These patterns are also present in the photospheric magnetograms: the network is stronger under the bright half and weaker under the dim half. (3) The radiation from the large-scale corona increases roughly as the cube root of the magnetic flux content of the underlying magnetic network. In contrast, the coronal radiation from an active region increases roughly linearly with the magnetic flux content of the active region. We assume, as is widely held, that nearly all of the large-scale corona is magnetically rooted in the network. Our results suggest that either the coronal heating in quiet regions has a large non-magnetic component, or, if the heating is predominantly produced via the magnetic field, the mechanism is significantly different than in active regions.

The salience network causally influences default mode network activity during moral reasoning

PubMed Central

Wilson, Stephen M.; D’Esposito, Mark; Kayser, Andrew S.; Grossman, Scott N.; Poorzand, Pardis; Seeley, William W.; Miller, Bruce L.; Rankin, Katherine P.

2013-01-01

Large-scale brain networks are integral to the coordination of human behaviour, and their anatomy provides insights into the clinical presentation and progression of neurodegenerative illnesses such as Alzheimer’s disease, which targets the default mode network, and behavioural variant frontotemporal dementia, which targets a more anterior salience network. Although the default mode network is recruited when healthy subjects deliberate about ‘personal’ moral dilemmas, patients with Alzheimer’s disease give normal responses to these dilemmas whereas patients with behavioural variant frontotemporal dementia give abnormal responses to these dilemmas. We hypothesized that this apparent discrepancy between activation- and patient-based studies of moral reasoning might reflect a modulatory role for the salience network in regulating default mode network activation. Using functional magnetic resonance imaging to characterize network activity of patients with behavioural variant frontotemporal dementia and healthy control subjects, we present four converging lines of evidence supporting a causal influence from the salience network to the default mode network during moral reasoning. First, as previously reported, the default mode network is recruited when healthy subjects deliberate about ‘personal’ moral dilemmas, but patients with behavioural variant frontotemporal dementia producing atrophy in the salience network give abnormally utilitarian responses to these dilemmas. Second, patients with behavioural variant frontotemporal dementia have reduced recruitment of the default mode network compared with healthy control subjects when deliberating about these dilemmas. Third, a Granger causality analysis of functional neuroimaging data from healthy control subjects demonstrates directed functional connectivity from nodes of the salience network to nodes of the default mode network during moral reasoning. Fourth, this Granger causal influence is diminished in patients with behavioural variant frontotemporal dementia. These findings are consistent with a broader model in which the salience network modulates the activity of other large-scale networks, and suggest a revision to a previously proposed ‘dual-process’ account of moral reasoning. These findings also characterize network interactions underlying abnormal moral reasoning in frontotemporal dementia, which may serve as a model for the aberrant judgement and interpersonal behaviour observed in this disease and in other disorders of social function. More broadly, these findings link recent work on the dynamic interrelationships between large-scale brain networks to observable impairments in dementia syndromes, which may shed light on how diseases that target one network also alter the function of interrelated networks. PMID:23576128

Understanding the structure of community collaboration: the case of one Canadian health promotion network.

PubMed

Barnes, Martha; Maclean, Joanne; Cousens, Laura

2010-06-01

In 2004, over 6.8 million Canadians were considered overweight, with an additional 2.4 million labeled clinically obese. Due to these escalating levels of obesity in Canada, physical activity is being championed by politicians, physicians, educators and community members as a means to address this health crisis. In doing so, many organizations are being called upon to provide essential physical activity services and programs to combat rising obesity rates. Yet, strategies for achieving these organizations' mandates, which invariably involve stretching already scarce resources, are difficult to implement and sustain. One strategy for improving the health and physical activity levels of people in communities has been the creation of inter-organizational networks of service providers. Yet, little is known about whether networks are effective in addressing policy issues in non-clinical health settings. The purpose of this investigation was 2-fold; to use whole network analysis to determine the structure of one health promotion network in Canada, and to identify the types of ties shared by actors in the health network. Findings revealed a network wherein information sharing constituted the basis for collaboration, whereas efforts related to sharing resources, marketing and/or fundraising endeavors were less evident.

Living network meta-analysis compared with pairwise meta-analysis in comparative effectiveness research: empirical study

PubMed Central

Nikolakopoulou, Adriani; Mavridis, Dimitris; Furukawa, Toshi A; Cipriani, Andrea; Tricco, Andrea C; Straus, Sharon E; Siontis, George C M; Egger, Matthias

2018-01-01

Abstract Objective To examine whether the continuous updating of networks of prospectively planned randomised controlled trials (RCTs) (“living” network meta-analysis) provides strong evidence against the null hypothesis in comparative effectiveness of medical interventions earlier than the updating of conventional, pairwise meta-analysis. Design Empirical study of the accumulating evidence about the comparative effectiveness of clinical interventions. Data sources Database of network meta-analyses of RCTs identified through searches of Medline, Embase, and the Cochrane Database of Systematic Reviews until 14 April 2015. Eligibility criteria for study selection Network meta-analyses published after January 2012 that compared at least five treatments and included at least 20 RCTs. Clinical experts were asked to identify in each network the treatment comparison of greatest clinical interest. Comparisons were excluded for which direct and indirect evidence disagreed, based on side, or node, splitting test (P<0.10). Outcomes and analysis Cumulative pairwise and network meta-analyses were performed for each selected comparison. Monitoring boundaries of statistical significance were constructed and the evidence against the null hypothesis was considered to be strong when the monitoring boundaries were crossed. A significance level was defined as α=5%, power of 90% (β=10%), and an anticipated treatment effect to detect equal to the final estimate from the network meta-analysis. The frequency and time to strong evidence was compared against the null hypothesis between pairwise and network meta-analyses. Results 49 comparisons of interest from 44 networks were included; most (n=39, 80%) were between active drugs, mainly from the specialties of cardiology, endocrinology, psychiatry, and rheumatology. 29 comparisons were informed by both direct and indirect evidence (59%), 13 by indirect evidence (27%), and 7 by direct evidence (14%). Both network and pairwise meta-analysis provided strong evidence against the null hypothesis for seven comparisons, but for an additional 10 comparisons only network meta-analysis provided strong evidence against the null hypothesis (P=0.002). The median time to strong evidence against the null hypothesis was 19 years with living network meta-analysis and 23 years with living pairwise meta-analysis (hazard ratio 2.78, 95% confidence interval 1.00 to 7.72, P=0.05). Studies directly comparing the treatments of interest continued to be published for eight comparisons after strong evidence had become evident in network meta-analysis. Conclusions In comparative effectiveness research, prospectively planned living network meta-analyses produced strong evidence against the null hypothesis more often and earlier than conventional, pairwise meta-analyses. PMID:29490922

Nonsmooth Finite-Time Synchronization of Switched Coupled Neural Networks.

PubMed

Liu, Xiaoyang; Cao, Jinde; Yu, Wenwu; Song, Qiang

2016-10-01

This paper is concerned with the finite-time synchronization (FTS) issue of switched coupled neural networks with discontinuous or continuous activations. Based on the framework of nonsmooth analysis, some discontinuous or continuous controllers are designed to force the coupled networks to synchronize to an isolated neural network. Some sufficient conditions are derived to ensure the FTS by utilizing the well-known finite-time stability theorem for nonlinear systems. Compared with the previous literatures, such synchronization objective will be realized when the activations and the controllers are both discontinuous. The obtained results in this paper include and extend the earlier works on the synchronization issue of coupled networks with Lipschitz continuous conditions. Moreover, an upper bound of the settling time for synchronization is estimated. Finally, numerical simulations are given to demonstrate the effectiveness of the theoretical results.

Experiments in clustered neuronal networks: A paradigm for complex modular dynamics

NASA Astrophysics Data System (ADS)

Teller, Sara; Soriano, Jordi

2016-06-01

Uncovering the interplay activity-connectivity is one of the major challenges in neuroscience. To deepen in the understanding of how a neuronal circuit shapes network dynamics, neuronal cultures have emerged as remarkable systems given their accessibility and easy manipulation. An attractive configuration of these in vitro systems consists in an ensemble of interconnected clusters of neurons. Using calcium fluorescence imaging to monitor spontaneous activity in these clustered neuronal networks, we were able to draw functional maps and reveal their topological features. We also observed that these networks exhibit a hierarchical modular dynamics, in which clusters fire in small groups that shape characteristic communities in the network. The structure and stability of these communities is sensitive to chemical or physical action, and therefore their analysis may serve as a proxy for network health. Indeed, the combination of all these approaches is helping to develop models to quantify damage upon network degradation, with promising applications for the study of neurological disorders in vitro.

Computational exploration of neuron and neural network models in neurobiology.

PubMed

Prinz, Astrid A

2007-01-01

The electrical activity of individual neurons and neuronal networks is shaped by the complex interplay of a large number of non-linear processes, including the voltage-dependent gating of ion channels and the activation of synaptic receptors. These complex dynamics make it difficult to understand how individual neuron or network parameters-such as the number of ion channels of a given type in a neuron's membrane or the strength of a particular synapse-influence neural system function. Systematic exploration of cellular or network model parameter spaces by computational brute force can overcome this difficulty and generate comprehensive data sets that contain information about neuron or network behavior for many different combinations of parameters. Searching such data sets for parameter combinations that produce functional neuron or network output provides insights into how narrowly different neural system parameters have to be tuned to produce a desired behavior. This chapter describes the construction and analysis of databases of neuron or neuronal network models and describes some of the advantages and downsides of such exploration methods.

[Study based on ICA of "dorsal attention network" in patients with temporal lobe epilepsy].

PubMed

Yang, Zhigen; Wang, Huinan; Zhang, Zhiqiang; Zhong, Yuan; Chen, Zhili; Lu, Guangming

2010-02-01

Many functional magnetic resonance imaging (fMRI) studies have revealed the deactivation phenomenon of default mode network in the patients with epilepsy; however, nearly not any of the reports has focused on the dorsal attention network of epilepsy. In this paper, independent component analysis (ICA) was used to isolate the dorsal attention network of 16 patients with temporal lobe epilepsy (TLE) and of 20 healthy normals; and a goodness-of-fit analysis was applied at the individual subject level to choose the interesting component. Intra-group analysis and inter-group analysis were performed. The results indicated that the dorsal attention network included bilateral intraparietal sulcus, middle frontal gyrus, human frontal eye field, posterior lobe of right cerebellum, etc. The TLE group showed decreased functional connectivity in most of the dorsal attention regions with the predominance in the bilateral intraparietal sulcus, middle frontal gyrus, and posterior lobe of right cerebellum. These data suggested that the intrinsic organization of the brain function might be disrupted in TLE. In addition, the decrease of goodness-of-fit scores suggests that activity in the dorsal attention network may ultimately prove a sensitive biomarker for TLE.

Distributed task coding throughout the multiple demand network of the human frontal-insular cortex.

PubMed

Stiers, Peter; Mennes, Maarten; Sunaert, Stefan

2010-08-01

The large variety of tasks that humans can perform is governed by a small number of key frontal-insular regions that are commonly active during task performance. Little is known about how this network distinguishes different tasks. We report on fMRI data in twelve participants while they performed four cognitive tasks. Of 20 commonly active frontal-insular regions in each hemisphere, five showed a BOLD response increase with increased task demands, regardless of the task. Although active in all tasks, each task invoked a unique response pattern across the voxels in each area that proved reliable in split-half multi-voxel correlation analysis. Consequently, voxels differed in their preference for one or more of the tasks. Voxel-based functional connectivity analyses revealed that same preference voxels distributed across all areas of the network constituted functional sub-networks that characterized the task being executed. Copyright 2010 Elsevier Inc. All rights reserved.

A neural network ActiveX based integrated image processing environment.

PubMed

Ciuca, I; Jitaru, E; Alaicescu, M; Moisil, I

2000-01-01

The paper outlines an integrated image processing environment that uses neural networks ActiveX technology for object recognition and classification. The image processing environment which is Windows based, encapsulates a Multiple-Document Interface (MDI) and is menu driven. Object (shape) parameter extraction is focused on features that are invariant in terms of translation, rotation and scale transformations. The neural network models that can be incorporated as ActiveX components into the environment allow both clustering and classification of objects from the analysed image. Mapping neural networks perform an input sensitivity analysis on the extracted feature measurements and thus facilitate the removal of irrelevant features and improvements in the degree of generalisation. The program has been used to evaluate the dimensions of the hydrocephalus in a study for calculating the Evans index and the angle of the frontal horns of the ventricular system modifications.

Altering the threshold of an excitable signal transduction network changes cell migratory modes.

PubMed

Miao, Yuchuan; Bhattacharya, Sayak; Edwards, Marc; Cai, Huaqing; Inoue, Takanari; Iglesias, Pablo A; Devreotes, Peter N

2017-04-01

The diverse migratory modes displayed by different cell types are generally believed to be idiosyncratic. Here we show that the migratory behaviour of Dictyostelium was switched from amoeboid to keratocyte-like and oscillatory modes by synthetically decreasing phosphatidylinositol-4,5-bisphosphate levels or increasing Ras/Rap-related activities. The perturbations at these key nodes of an excitable signal transduction network initiated a causal chain of events: the threshold for network activation was lowered, the speed and range of propagating waves of signal transduction activity increased, actin-driven cellular protrusions expanded and, consequently, the cell migratory mode transitions ensued. Conversely, innately keratocyte-like and oscillatory cells were promptly converted to amoeboid by inhibition of Ras effectors with restoration of directed migration. We use computational analysis to explain how thresholds control cell migration and discuss the architecture of the signal transduction network that gives rise to excitability.

[Not Available].

PubMed

Yanashima, Ryoji; Kitagawa, Noriyuki; Matsubara, Yoshiya; Weatheritt, Robert; Oka, Kotaro; Kikuchi, Shinichi; Tomita, Masaru; Ishizaki, Shun

2009-01-01

The scale-free and small-world network models reflect the functional units of networks. However, when we investigated the network properties of a signaling pathway using these models, no significant differences were found between the original undirected graphs and the graphs in which inactive proteins were eliminated from the gene expression data. We analyzed signaling networks by focusing on those pathways that best reflected cellular function. Therefore, our analysis of pathways started from the ligands and progressed to transcription factors and cytoskeletal proteins. We employed the Python module to assess the target network. This involved comparing the original and restricted signaling cascades as a directed graph using microarray gene expression profiles of late onset Alzheimer's disease. The most commonly used method of shortest-path analysis neglects to consider the influences of alternative pathways that can affect the activation of transcription factors or cytoskeletal proteins. We therefore introduced included k-shortest paths and k-cycles in our network analysis using the Python modules, which allowed us to attain a reasonable computational time and identify k-shortest paths. This technique reflected results found in vivo and identified pathways not found when shortest path or degree analysis was applied. Our module enabled us to comprehensively analyse the characteristics of biomolecular networks and also enabled analysis of the effects of diseases considering the feedback loop and feedforward loop control structures as an alternative path.

Finite-time robust stabilization of uncertain delayed neural networks with discontinuous activations via delayed feedback control.

PubMed

Wang, Leimin; Shen, Yi; Sheng, Yin

2016-04-01

This paper is concerned with the finite-time robust stabilization of delayed neural networks (DNNs) in the presence of discontinuous activations and parameter uncertainties. By using the nonsmooth analysis and control theory, a delayed controller is designed to realize the finite-time robust stabilization of DNNs with discontinuous activations and parameter uncertainties, and the upper bound of the settling time functional for stabilization is estimated. Finally, two examples are provided to demonstrate the effectiveness of the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Extracting neuronal functional network dynamics via adaptive Granger causality analysis.

PubMed

Sheikhattar, Alireza; Miran, Sina; Liu, Ji; Fritz, Jonathan B; Shamma, Shihab A; Kanold, Patrick O; Babadi, Behtash

2018-04-24

Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca 2+ imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.

Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities

PubMed Central

Santangelo, Valerio

2018-01-01

Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010) to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets. This design allowed comparing a condition in which participants monitored one stimulus/modality (either visual or auditory) in two spatial locations vs. a condition in which participants monitored two stimuli/modalities (both visual and auditory) in one spatial location. The analysis of the independent components (ICs) revealed that dividing attentional resources across two spatial locations necessitated a brain network involving the left ventro- and dorso-lateral prefrontal cortex plus the posterior parietal cortex, including the intraparietal sulcus (IPS) and the angular gyrus, bilaterally. The analysis of Granger causality highlighted that the activity of lateral prefrontal regions were predictive of the activity of all of the posteriors parietal nodes. By contrast, dividing attention across two sensory modalities necessitated a brain network including nodes belonging to the dorsal frontoparietal network, i.e., the bilateral frontal eye-fields (FEF) and IPS, plus nodes belonging to the salience network, i.e., the anterior cingulated cortex and the left and right anterior insular cortex (aIC). The analysis of Granger causality highlights a tight interdependence between the dorsal frontoparietal and salience nodes in trials requiring divided attention between different sensory modalities. The current findings therefore highlighted a dissociation among brain networks implicated during divided attention across spatial locations and sensory modalities, pointing out the importance of investigating effective connectivity of large-scale brain networks supporting complex behavior. PMID:29535614

Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities.

PubMed

Santangelo, Valerio

2018-01-01

Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010) to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets. This design allowed comparing a condition in which participants monitored one stimulus/modality (either visual or auditory) in two spatial locations vs. a condition in which participants monitored two stimuli/modalities (both visual and auditory) in one spatial location. The analysis of the independent components (ICs) revealed that dividing attentional resources across two spatial locations necessitated a brain network involving the left ventro- and dorso-lateral prefrontal cortex plus the posterior parietal cortex, including the intraparietal sulcus (IPS) and the angular gyrus, bilaterally. The analysis of Granger causality highlighted that the activity of lateral prefrontal regions were predictive of the activity of all of the posteriors parietal nodes. By contrast, dividing attention across two sensory modalities necessitated a brain network including nodes belonging to the dorsal frontoparietal network, i.e., the bilateral frontal eye-fields (FEF) and IPS, plus nodes belonging to the salience network, i.e., the anterior cingulated cortex and the left and right anterior insular cortex (aIC). The analysis of Granger causality highlights a tight interdependence between the dorsal frontoparietal and salience nodes in trials requiring divided attention between different sensory modalities. The current findings therefore highlighted a dissociation among brain networks implicated during divided attention across spatial locations and sensory modalities, pointing out the importance of investigating effective connectivity of large-scale brain networks supporting complex behavior.

Time-Frequency Cross Mutual Information Analysis of the Brain Functional Networks Underlying Multiclass Motor Imagery.

PubMed

Gong, Anmin; Liu, Jianping; Chen, Si; Fu, Yunfa

2018-01-01

To study the physiologic mechanism of the brain during different motor imagery (MI) tasks, the authors employed a method of brain-network modeling based on time-frequency cross mutual information obtained from 4-class (left hand, right hand, feet, and tongue) MI tasks recorded as brain-computer interface (BCI) electroencephalography data. The authors explored the brain network revealed by these MI tasks using statistical analysis and the analysis of topologic characteristics, and observed significant differences in the reaction level, reaction time, and activated target during 4-class MI tasks. There was a great difference in the reaction level between the execution and resting states during different tasks: the reaction level of the left-hand MI task was the greatest, followed by that of the right-hand, feet, and tongue MI tasks. The reaction time required to perform the tasks also differed: during the left-hand and right-hand MI tasks, the brain networks of subjects reacted promptly and strongly, but there was a delay during the feet and tongue MI task. Statistical analysis and the analysis of network topology revealed the target regions of the brain network during different MI processes. In conclusion, our findings suggest a new way to explain the neural mechanism behind MI.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Christoph, G.G; Jackson, K.A.; Neuman, M.C.

An effective method for detecting computer misuse is the automatic auditing and analysis of on-line user activity. This activity is reflected in the system audit record, by changes in the vulnerability posture of the system configuration, and in other evidence found through active testing of the system. In 1989 we started developing an automatic misuse detection system for the Integrated Computing Network (ICN) at Los Alamos National Laboratory. Since 1990 this system has been operational, monitoring a variety of network systems and services. We call it the Network Anomaly Detection and Intrusion Reporter, or NADIR. During the last year andmore » a half, we expanded NADIR to include processing of audit and activity records for the Cray UNICOS operating system. This new component is called the UNICOS Real-time NADIR, or UNICORN. UNICORN summarizes user activity and system configuration information in statistical profiles. In near real-time, it can compare current activity to historical profiles and test activity against expert rules that express our security policy and define improper or suspicious behavior. It reports suspicious behavior to security auditors and provides tools to aid in follow-up investigations. UNICORN is currently operational on four Crays in Los Alamos` main computing network, the ICN.« less

Assessing user engagement in a health promotion website using social networking.

PubMed

Tague, Rhys; Maeder, Anthony J; Vandelanotte, Corneel; Kolt, Gregory S; Caperchione, Cristina M; Rosenkranz, Richard R; Savage, Trevor N; Van Itallie, Anetta

2014-01-01

Remote provision of supportive mechanisms for preventive health is a fast-growing area in eHealth. Web-based interventions have been suggested as an effective way to increase adoption and maintenance of healthy lifestyle behaviours. This paper describes results obtained in the "Walk 2.0" trial to promote physical activity through a self-managed walking programme, using a social networking website that provided an online collaborative environment. Engagement of participants with the website was assessed by monitoring usage of the individual social networking functions (e.g. status post). The results demonstrate that users generally preferred contributing non-interactive public posts of information concerned with their individual physical activity levels, and more occasionally communicating privately to friends. Further analysis of topics within posts was done by classifying word usage frequencies. Results indicated that the dominant topics are well aligned with the social environment within which physical activity takes place. Topics centred around four main areas: description of the activity, timing of the activity, affective response to the activity, and context within which the activity occurs. These findings suggest that strong levels of user awareness and communication occur in the social networking setting, indicative of beneficial self-image and self-actualisation effects.

Signal Processing in Periodically Forced Gradient Frequency Neural Networks

PubMed Central

Kim, Ji Chul; Large, Edward W.

2015-01-01

Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing. PMID:26733858

Pathway and network-based analysis of genome-wide association studies and RT-PCR validation in polycystic ovary syndrome.

PubMed

Shen, Haoran; Liang, Zhou; Zheng, Saihua; Li, Xuelian

2017-11-01

The purpose of this study was to identify promising candidate genes and pathways in polycystic ovary syndrome (PCOS). Microarray dataset GSE345269 obtained from the Gene Expression Omnibus database includes 7 granulosa cell samples from PCOS patients, and 3 normal granulosa cell samples. Differentially expressed genes (DEGs) were screened between PCOS and normal samples. Pathway enrichment analysis was conducted for DEGs using ClueGO and CluePedia plugin of Cytoscape. A Reactome functional interaction (FI) network of the DEGs was built using ReactomeFIViz, and then network modules were extracted, followed by pathway enrichment analysis for the modules. Expression of DEGs in granulosa cell samples was measured using quantitative RT-PCR. A total of 674 DEGs were retained, which were significantly enriched with inflammation and immune-related pathways. Eight modules were extracted from the Reactome FI network. Pathway enrichment analysis revealed significant pathways of each module: module 0, Regulation of RhoA activity and Signaling by Rho GTPases pathways shared ARHGAP4 and ARHGAP9; module 2, GlycoProtein VI-mediated activation cascade pathway was enriched with RHOG; module 3, Thromboxane A2 receptor signaling, Chemokine signaling pathway, CXCR4-mediated signaling events pathways were enriched with LYN, the hub gene of module 3. Results of RT-PCR confirmed the finding of the bioinformatic analysis that ARHGAP4, ARHGAP9, RHOG and LYN were significantly upregulated in PCOS. RhoA-related pathways, GlycoProtein VI-mediated activation cascade pathway, ARHGAP4, ARHGAP9, RHOG and LYN may be involved in the pathogenesis of PCOS.

The Role of Probability-Based Inference in an Intelligent Tutoring System.

ERIC Educational Resources Information Center

Mislevy, Robert J.; Gitomer, Drew H.

Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring…

Persistent ISR: the social network analysis connection

NASA Astrophysics Data System (ADS)

Bowman, Elizabeth K.

2012-06-01

Persistent surveillance provides decision makers with unprecedented access to multisource data collected from humans and sensor assets around the globe, yet these data exist in the physical world and provide few overt clues to meaning behind actions. In this paper we explore the recent growth in online social networking and ask the questions: 1) can these sites provide value-added information to compliment physical sensing and 2) what are the mechanisms by which these data could inform situational awareness and decision making? In seeking these answers we consider the range of options provided by Social Network Analysis (SNA), and focus especially on the dynamic nature of these networks. In our discussion we focus on the wave of reform experienced by the North African nations in early 2011 known as the Arab Spring. Demonstrators made widespread use of social networking applications to coordinate, document, and publish material to aid their cause. Unlike members of covert social networks who hide their activity and associations, these demonstrators openly posted multimedia information to coordinate activity and stimulate global support. In this paper we provide a review of SNA approaches and consider how one might track network adaptations by capturing temporal and conceptual trends. We identify opportunities and challenges for merging SNA with physical sensor output, and conclude by addressing future challenges in the persistent ISR domain with respect to SNA.

Detection of 5-hydroxytryptamine (5-HT) in vitro using a hippocampal neuronal network-based biosensor with extracellular potential analysis of neurons.

PubMed

Hu, Liang; Wang, Qin; Qin, Zhen; Su, Kaiqi; Huang, Liquan; Hu, Ning; Wang, Ping

2015-04-15

5-hydroxytryptamine (5-HT) is an important neurotransmitter in regulating emotions and related behaviors in mammals. To detect and monitor the 5-HT, effective and convenient methods are demanded in investigation of neuronal network. In this study, hippocampal neuronal networks (HNNs) endogenously expressing 5-HT receptors were employed as sensing elements to build an in vitro neuronal network-based biosensor. The electrophysiological characteristics were analyzed in both neuron and network levels. The firing rates and amplitudes were derived from signal to determine the biosensor response characteristics. The experimental results demonstrate a dose-dependent inhibitory effect of 5-HT on hippocampal neuron activities, indicating the effectiveness of this hybrid biosensor in detecting 5-HT with a response range from 0.01μmol/L to 10μmol/L. In addition, the cross-correlation analysis of HNNs activities suggests 5-HT could weaken HNN connectivity reversibly, providing more specificity of this biosensor in detecting 5-HT. Moreover, 5-HT induced spatiotemporal firing pattern alterations could be monitored in neuron and network levels simultaneously by this hybrid biosensor in a convenient and direct way. With those merits, this neuronal network-based biosensor will be promising to be a valuable and utility platform for the study of neurotransmitter in vitro. Copyright © 2014 Elsevier B.V. All rights reserved.

Multiple brain networks for visual self-recognition with different sensitivity for motion and body part.

PubMed

Sugiura, Motoaki; Sassa, Yuko; Jeong, Hyeonjeong; Miura, Naoki; Akitsuki, Yuko; Horie, Kaoru; Sato, Shigeru; Kawashima, Ryuta

2006-10-01

Multiple brain networks may support visual self-recognition. It has been hypothesized that the left ventral occipito-temporal cortex processes one's own face as a symbol, and the right parieto-frontal network processes self-image in association with motion-action contingency. Using functional magnetic resonance imaging, we first tested these hypotheses based on the prediction that these networks preferentially respond to a static self-face and to moving one's whole body, respectively. Brain activation specifically related to self-image during familiarity judgment was compared across four stimulus conditions comprising a two factorial design: factor Motion contrasted picture (Picture) and movie (Movie), and factor Body part a face (Face) and whole body (Body). Second, we attempted to segregate self-specific networks using a principal component analysis (PCA), assuming an independent pattern of inter-subject variability in activation over the four stimulus conditions in each network. The bilateral ventral occipito-temporal and the right parietal and frontal cortices exhibited self-specific activation. The left ventral occipito-temporal cortex exhibited greater self-specific activation for Face than for Body, in Picture, consistent with the prediction for this region. The activation profiles of the right parietal and frontal cortices did not show preference for Movie Body predicted by the assumed roles of these regions. The PCA extracted two cortical networks, one with its peaks in the right posterior, and another in frontal cortices; their possible roles in visuo-spatial and conceptual self-representations, respectively, were suggested by previous findings. The results thus supported and provided evidence of multiple brain networks for visual self-recognition.

Association between suicidal symptoms and repeat suicidal behaviour within a sample of hospital-treated suicide attempters.

PubMed

de Beurs, Derek P; van Borkulo, Claudia D; O'Connor, Rory C

2017-05-01

Suicidal behaviour is the end result of the complex relation between many factors which are biological, psychological and environmental in nature. Network analysis is a novel method that may help us better understand the complex association between different factors. To examine the relationship between suicidal symptoms as assessed by the Beck Scale for Suicide Ideation and future suicidal behaviour in patients admitted to hospital following a suicide attempt, using network analysis. Secondary analysis was conducted on previously collected data from a sample of 366 patients who were admitted to a Scottish hospital following a suicide attempt. Network models were estimated to visualise and test the association between baseline symptom network structure and suicidal behaviour at 15-month follow-up. Network analysis showed that the desire for an active attempt was found to be the most central, strongly related suicide symptom. Of the 19 suicide symptoms that were assessed at baseline, 10 symptoms were directly related to repeat suicidal behaviour. When comparing baseline network structure of repeaters ( n =94) with the network of non-repeaters ( n =272), no significant differences were found. Network analysis can help us better understand suicidal behaviour by visualising the complex relation between relevant symptoms and by indicating which symptoms are most central within the network. These insights have theoretical implications as well as informing the assessment and treatment of suicidal behaviour. None. © The Royal College of Psychiatrists 2017. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license.

Abnormal-induced theta activity supports early directed-attention network deficits in progressive MCI.

PubMed

Deiber, Marie-Pierre; Ibañez, Vicente; Missonnier, Pascal; Herrmann, François; Fazio-Costa, Lara; Gold, Gabriel; Giannakopoulos, Panteleimon

2009-09-01

The electroencephalography (EEG) theta frequency band reacts to memory and selective attention paradigms. Global theta oscillatory activity includes a posterior phase-locked component related to stimulus processing and a frontal-induced component modulated by directed attention. To investigate the presence of early deficits in the directed attention-related network in elderly individuals with mild cognitive impairment (MCI), time-frequency analysis at baseline was used to assess global and induced theta oscillatory activity (4-6Hz) during n-back working memory tasks in 29 individuals with MCI and 24 elderly controls (EC). At 1-year follow-up, 13 MCI patients were still stable and 16 had progressed. Baseline task performance was similar in stable and progressive MCI cases. Induced theta activity at baseline was significantly reduced in progressive MCI as compared to EC and stable MCI in all n-back tasks, which were similar in terms of directed attention requirements. While performance is maintained, the decrease of induced theta activity suggests early deficits in the directed-attention network in progressive MCI, whereas this network is functionally preserved in stable MCI.

Actor-network theory: a tool to support ethical analysis of commercial genetic testing.

PubMed

Williams-Jones, Bryn; Graham, Janice E

2003-12-01

Social, ethical and policy analysis of the issues arising from gene patenting and commercial genetic testing is enhanced by the application of science and technology studies, and Actor-Network Theory (ANT) in particular. We suggest the potential for transferring ANT's flexible nature to an applied heuristic methodology for gathering empirical information and for analysing the complex networks involved in the development of genetic technologies. Three concepts are explored in this paper--actor-networks, translation, and drift--and applied to the case of Myriad Genetics and their commercial BRACAnalysis genetic susceptibility test for hereditary breast cancer. Treating this test as an active participant in socio-technical networks clarifies the extent to which it interacts with, shapes and is shaped by people, other technologies, and institutions. Such an understanding enables more sophisticated and nuanced technology assessment, academic analysis, as well as public debate about the social, ethical and policy implications of the commercialization of new genetic technologies.

The contribution of extracurricular activities to adolescent friendships: new insights through social network analysis.

PubMed

Schaefer, David R; Simpkins, Sandra D; Vest, Andrea E; Price, Chara D

2011-07-01

Extracurricular activities are settings that are theorized to help adolescents maintain existing friendships and develop new friendships. The overarching goal of the current investigation was to examine whether coparticipating in school-based extracurricular activities supported adolescents' school-based friendships. We used social network methods and data from the National Longitudinal Study of Adolescent Health to examine whether dyadic friendship ties were more likely to exist among activity coparticipants while controlling for alternative friendship processes, namely dyadic homophily (e.g., demographic and behavioral similarities) and network-level processes (e.g., triadic closure). Results provide strong evidence that activities were associated with current friendships and promoted the formation of new friendships. These associations varied based on school level (i.e., middle vs. high school) and activity type (i.e., sports, academic, arts). Results of this study provide new insight into the complex relations between activities and friendship that can inform theories of their developmental outcomes. PsycINFO Database Record (c) 2011 APA, all rights reserved

The Contribution of Extracurricular Activities to Adolescent Friendships: New Insights through Social Network Analysis

PubMed Central

Schaefer, David R.; Simpkins, Sandra D.; Vest, Andrea E.; Price, Chara D.

2011-01-01

Extracurricular activities are settings that are theorized to help adolescents maintain existing friendships and develop new friendships. The overarching goal of the current investigation was to examine whether co-participating in school-based extracurricular activities supported adolescents’ school-based friendships. We utilized social network methods and data from the National Longitudinal Study of Adolescent Health to examine whether dyadic friendship ties were more likely to exist among activity co-participants while controlling for alternative friendship processes, namely dyadic homophily (e.g., demographic and behavioral similarities) and network-level processes (e.g., triadic closure). Results provide strong evidence that activities were associated with current friendships and promoted the formation of new friendships. These associations varied based on school level (i.e., middle versus high school) and activity type (i.e., sports, academic, arts). Results of this study provide new insight into the complex relations between activities and friendship that can inform theories of their developmental outcomes. PMID:21639618

Best core stabilization exercise to facilitate subcortical neuroplasticity: A functional MRI neuroimaging study.

PubMed

Kim, Do Hyun; Lee, Jae Jin; You, Sung Joshua Hyun

2018-03-23

To investigate the effects of conscious (ADIM) and subconscious (DNS) core stabilization exercises on cortical changes in adults with core instability. Five non-symptomatic participants with core instability. A novel core stabilization task switching paradigm was designed to separate cortical or subcortical neural substrates during a series of DNS or ADIM core stabilization tasks. fMRI blood BOLD analysis revealed a distinctive subcortical activation pattern during the performance of the DNS, whereas the cortical motor network was primarily activated during an ADIM. Peak voxel volume values showed significantly greater DNS (11.08 ± 1.51) compared with the ADIM (8.81 ± 0.21) (p= 0.043). The ADIM exercise activated the cortical PMC-SMC-SMA motor network, whereas the DNS exercise activated both these same cortical areas and the subcortical cerebellum-BG-thalamus-cingulate cortex network.

RNA Sequencing and Bioinformatics Analysis Implicate the Regulatory Role of a Long Noncoding RNA-mRNA Network in Hepatic Stellate Cell Activation.

PubMed

Guo, Can-Jie; Xiao, Xiao; Sheng, Li; Chen, Lili; Zhong, Wei; Li, Hai; Hua, Jing; Ma, Xiong

2017-01-01

To analyze the long noncoding (lncRNA)-mRNA expression network and potential roles in rat hepatic stellate cells (HSCs) during activation. LncRNA expression was analyzed in quiescent and culture-activated HSCs by RNA sequencing, and differentially expressed lncRNAs verified by quantitative reverse transcription polymerase chain reaction (qRT-PCR) were subjected to bioinformatics analysis. In vivo analyses of differential lncRNA-mRNA expression were performed on a rat model of liver fibrosis. We identified upregulation of 12 lncRNAs and 155 mRNAs and downregulation of 12 lncRNAs and 374 mRNAs in activated HSCs. Additionally, we identified the differential expression of upregulated lncRNAs (NONRATT012636.2, NONRATT016788.2, and NONRATT021402.2) and downregulated lncRNAs (NONRATT007863.2, NONRATT019720.2, and NONRATT024061.2) in activated HSCs relative to levels observed in quiescent HSCs, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that changes in lncRNAs associated with HSC activation revealed 11 significantly enriched pathways according to their predicted targets. Moreover, based on the predicted co-expression network, the relative dynamic levels of NONRATT013819.2 and lysyl oxidase (Lox) were compared during HSC activation both in vitro and in vivo. Our results confirmed the upregulation of lncRNA NONRATT013819.2 and Lox mRNA associated with the extracellular matrix (ECM)-related signaling pathway in HSCs and fibrotic livers. Our results detailing a dysregulated lncRNA-mRNA network might provide new treatment strategies for hepatic fibrosis based on findings indicating potentially critical roles for NONRATT013819.2 and Lox in ECM remodeling during HSC activation. © 2017 The Author(s). Published by S. Karger AG, Basel.

Living network meta-analysis compared with pairwise meta-analysis in comparative effectiveness research: empirical study.

PubMed

Nikolakopoulou, Adriani; Mavridis, Dimitris; Furukawa, Toshi A; Cipriani, Andrea; Tricco, Andrea C; Straus, Sharon E; Siontis, George C M; Egger, Matthias; Salanti, Georgia

2018-02-28

To examine whether the continuous updating of networks of prospectively planned randomised controlled trials (RCTs) ("living" network meta-analysis) provides strong evidence against the null hypothesis in comparative effectiveness of medical interventions earlier than the updating of conventional, pairwise meta-analysis. Empirical study of the accumulating evidence about the comparative effectiveness of clinical interventions. Database of network meta-analyses of RCTs identified through searches of Medline, Embase, and the Cochrane Database of Systematic Reviews until 14 April 2015. Network meta-analyses published after January 2012 that compared at least five treatments and included at least 20 RCTs. Clinical experts were asked to identify in each network the treatment comparison of greatest clinical interest. Comparisons were excluded for which direct and indirect evidence disagreed, based on side, or node, splitting test (P<0.10). Cumulative pairwise and network meta-analyses were performed for each selected comparison. Monitoring boundaries of statistical significance were constructed and the evidence against the null hypothesis was considered to be strong when the monitoring boundaries were crossed. A significance level was defined as α=5%, power of 90% (β=10%), and an anticipated treatment effect to detect equal to the final estimate from the network meta-analysis. The frequency and time to strong evidence was compared against the null hypothesis between pairwise and network meta-analyses. 49 comparisons of interest from 44 networks were included; most (n=39, 80%) were between active drugs, mainly from the specialties of cardiology, endocrinology, psychiatry, and rheumatology. 29 comparisons were informed by both direct and indirect evidence (59%), 13 by indirect evidence (27%), and 7 by direct evidence (14%). Both network and pairwise meta-analysis provided strong evidence against the null hypothesis for seven comparisons, but for an additional 10 comparisons only network meta-analysis provided strong evidence against the null hypothesis (P=0.002). The median time to strong evidence against the null hypothesis was 19 years with living network meta-analysis and 23 years with living pairwise meta-analysis (hazard ratio 2.78, 95% confidence interval 1.00 to 7.72, P=0.05). Studies directly comparing the treatments of interest continued to be published for eight comparisons after strong evidence had become evident in network meta-analysis. In comparative effectiveness research, prospectively planned living network meta-analyses produced strong evidence against the null hypothesis more often and earlier than conventional, pairwise meta-analyses. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Characterizing Social Interaction in Tobacco-Oriented Social Networks: An Empirical Analysis.

PubMed

Liang, Yunji; Zheng, Xiaolong; Zeng, Daniel Dajun; Zhou, Xingshe; Leischow, Scott James; Chung, Wingyan

2015-06-19

Social media is becoming a new battlefield for tobacco "wars". Evaluating the current situation is very crucial for the advocacy of tobacco control in the age of social media. To reveal the impact of tobacco-related user-generated content, this paper characterizes user interaction and social influence utilizing social network analysis and information theoretic approaches. Our empirical studies demonstrate that the exploding pro-tobacco content has long-lasting effects with more active users and broader influence, and reveal the shortage of social media resources in global tobacco control. It is found that the user interaction in the pro-tobacco group is more active, and user-generated content for tobacco promotion is more successful in obtaining user attention. Furthermore, we construct three tobacco-related social networks and investigate the topological patterns of these tobacco-related social networks. We find that the size of the pro-tobacco network overwhelms the others, which suggests a huge number of users are exposed to the pro-tobacco content. These results indicate that the gap between tobacco promotion and tobacco control is widening and tobacco control may be losing ground to tobacco promotion in social media.

Characterizing Social Interaction in Tobacco-Oriented Social Networks: An Empirical Analysis

PubMed Central

Liang, Yunji; Zheng, Xiaolong; Zeng, Daniel Dajun; Zhou, Xingshe; Leischow, Scott James; Chung, Wingyan

2015-01-01

Social media is becoming a new battlefield for tobacco “wars”. Evaluating the current situation is very crucial for the advocacy of tobacco control in the age of social media. To reveal the impact of tobacco-related user-generated content, this paper characterizes user interaction and social influence utilizing social network analysis and information theoretic approaches. Our empirical studies demonstrate that the exploding pro-tobacco content has long-lasting effects with more active users and broader influence, and reveal the shortage of social media resources in global tobacco control. It is found that the user interaction in the pro-tobacco group is more active, and user-generated content for tobacco promotion is more successful in obtaining user attention. Furthermore, we construct three tobacco-related social networks and investigate the topological patterns of these tobacco-related social networks. We find that the size of the pro-tobacco network overwhelms the others, which suggests a huge number of users are exposed to the pro-tobacco content. These results indicate that the gap between tobacco promotion and tobacco control is widening and tobacco control may be losing ground to tobacco promotion in social media. PMID:26091553

Cognitive and default-mode resting state networks: do male and female brains "rest" differently?

PubMed

Weissman-Fogel, Irit; Moayedi, Massieh; Taylor, Keri S; Pope, Geoff; Davis, Karen D

2010-11-01

Variability in human behavior related to sex is supported by neuroimaging studies showing differences in brain activation patterns during cognitive task performance. An emerging field is examining the human connectome, including networks of brain regions that are not only temporally-correlated during different task conditions, but also networks that show highly correlated spontaneous activity during a task-free state. Both task-related and task-free network activity has been associated with individual task performance and behavior under certain conditions. Therefore, our aim was to determine whether sex differences exist during a task-free resting state for two networks associated with cognitive task performance (executive control network (ECN), salience network (SN)) and the default mode network (DMN). Forty-nine healthy subjects (26 females, 23 males) underwent a 5-min task-free fMRI scan in a 3T MRI. An independent components analysis (ICA) was performed to identify the best-fit IC for each network based on specific spatial nodes defined in previous studies. To determine the consistency of these networks across subjects we performed self-organizing group-level ICA analyses. There were no significant differences between sexes in the functional connectivity of the brain areas within the ECN, SN, or the DMN. These important findings highlight the robustness of intrinsic connectivity of these resting state networks and their similarity between sexes. Furthermore, our findings suggest that resting state fMRI studies do not need to be controlled for sex. © 2010 Wiley-Liss, Inc.

Probing Intrinsic Resting-State Networks in the Infant Rat Brain

PubMed Central

Bajic, Dusica; Craig, Michael M.; Borsook, David; Becerra, Lino

2016-01-01

Resting-state functional magnetic resonance imaging (rs-fMRI) measures spontaneous fluctuations in blood oxygenation level-dependent (BOLD) signal in the absence of external stimuli. It has become a powerful tool for mapping large-scale brain networks in humans and animal models. Several rs-fMRI studies have been conducted in anesthetized and awake adult rats, reporting consistent patterns of brain activity at the systems level. However, the evolution to adult patterns of resting-state activity has not yet been evaluated and quantified in the developing rat brain. In this study, we hypothesized that large-scale intrinsic networks would be easily detectable but not fully established as specific patterns of activity in lightly anesthetized 2-week-old rats (N = 11). Independent component analysis (ICA) identified 8 networks in 2-week-old-rats. These included Default mode, Sensory (Exteroceptive), Salience (Interoceptive), Basal Ganglia-Thalamic-Hippocampal, Basal Ganglia, Autonomic, Cerebellar, as well as Thalamic-Brainstem networks. Many of these networks consisted of more than one component, possibly indicative of immature, underdeveloped networks at this early time point. Except for the Autonomic network, infant rat networks showed reduced connectivity with subcortical structures in comparison to previously published adult networks. Reported slow fluctuations in the BOLD signal that correspond to functionally relevant resting-state networks in 2-week-old rats can serve as an important tool for future studies of brain development in the settings of different pharmacological applications or disease. PMID:27803653

P-glycoprotein (ABCB1) inhibited network of mitochondrion transport along microtubule and BMP signal-induced cell shape in chimpanzee left cerebrum by systems-theoretical analysis.

PubMed

Lin, Hong; Wang, Lin; Jiang, Minghu; Huang, Juxiang; Qi, Lianxiu

2012-10-01

We constructed the significant low-expression P-glycoprotein (ABCB1) inhibited transport and signal network in chimpanzee compared with high-expression (fold change ≥2) the human left cerebrum in GEO data set, by using integration of gene regulatory activated and inhibited network inference method with gene ontology (GO) analysis. Our result showed that ABCB1 transport and signal upstream network RAB2A inhibited ABCB1, and downstream ABCB1-inhibited SMAD1_2, NCK2, SLC25A46, GDF10, RASGRP1, EGFR, LRPPRC, RASSF2, RASA4, CA2, CBLB, UBR5, SLC25A16, ITGB3BP, DDIT4, PDPN, RAB2A in chimpanzee left cerebrum. We obtained that the different biological processes of ABCB1 inhibited transport and signal network repressed carbon dioxide transport, ER to Golgi vesicle-mediated transport, folic acid transport, mitochondrion transport along microtubule, water transport, BMP signaling pathway, Ras protein signal transduction, transforming growth factor beta receptor signaling pathway in chimpanzee compared with the inhibited network of the human left cerebrum, as a result of inducing inhibition of mitochondrion transport along microtubule and BMP signal-induced cell shape in chimpanzee left cerebrum. Our hypothesis was verified by the same and different biological processes of ABCB1 inhibited transport and signal network of chimpanzee compared with the corresponding activated network of chimpanzee and the human left cerebrum, respectively. Copyright © 2012 John Wiley & Sons, Ltd.

Tightly Regulated Expression of Autographa californica Multicapsid Nucleopolyhedrovirus Immediate Early Genes Emerges from Their Interactions and Possible Collective Behaviors

PubMed Central

Taka, Hitomi; Asano, Shin-ichiro; Matsuura, Yoshiharu; Bando, Hisanori

2015-01-01

To infect their hosts, DNA viruses must successfully initiate the expression of viral genes that control subsequent viral gene expression and manipulate the host environment. Viral genes that are immediately expressed upon infection play critical roles in the early infection process. In this study, we investigated the expression and regulation of five canonical regulatory immediate-early (IE) genes of Autographa californica multicapsid nucleopolyhedrovirus: ie0, ie1, ie2, me53, and pe38. A systematic transient gene-expression analysis revealed that these IE genes are generally transactivators, suggesting the existence of a highly interactive regulatory network. A genetic analysis using gene knockout viruses demonstrated that the expression of these IE genes was tolerant to the single deletions of activator IE genes in the early stage of infection. A network graph analysis on the regulatory relationships observed in the transient expression analysis suggested that the robustness of IE gene expression is due to the organization of the IE gene regulatory network and how each IE gene is activated. However, some regulatory relationships detected by the genetic analysis were contradictory to those observed in the transient expression analysis, especially for IE0-mediated regulation. Statistical modeling, combined with genetic analysis using knockout alleles for ie0 and ie1, showed that the repressor function of ie0 was due to the interaction between ie0 and ie1, not ie0 itself. Taken together, these systematic approaches provided insight into the topology and nature of the IE gene regulatory network. PMID:25816136

Interconnected network motifs control podocyte morphology and kidney function.

PubMed

Azeloglu, Evren U; Hardy, Simon V; Eungdamrong, Narat John; Chen, Yibang; Jayaraman, Gomathi; Chuang, Peter Y; Fang, Wei; Xiong, Huabao; Neves, Susana R; Jain, Mohit R; Li, Hong; Ma'ayan, Avi; Gordon, Ronald E; He, John Cijiang; Iyengar, Ravi

2014-02-04

Podocytes are kidney cells with specialized morphology that is required for glomerular filtration. Diseases, such as diabetes, or drug exposure that causes disruption of the podocyte foot process morphology results in kidney pathophysiology. Proteomic analysis of glomeruli isolated from rats with puromycin-induced kidney disease and control rats indicated that protein kinase A (PKA), which is activated by adenosine 3',5'-monophosphate (cAMP), is a key regulator of podocyte morphology and function. In podocytes, cAMP signaling activates cAMP response element-binding protein (CREB) to enhance expression of the gene encoding a differentiation marker, synaptopodin, a protein that associates with actin and promotes its bundling. We constructed and experimentally verified a β-adrenergic receptor-driven network with multiple feedback and feedforward motifs that controls CREB activity. To determine how the motifs interacted to regulate gene expression, we mapped multicompartment dynamical models, including information about protein subcellular localization, onto the network topology using Petri net formalisms. These computational analyses indicated that the juxtaposition of multiple feedback and feedforward motifs enabled the prolonged CREB activation necessary for synaptopodin expression and actin bundling. Drug-induced modulation of these motifs in diseased rats led to recovery of normal morphology and physiological function in vivo. Thus, analysis of regulatory motifs using network dynamics can provide insights into pathophysiology that enable predictions for drug intervention strategies to treat kidney disease.

Interconnected Network Motifs Control Podocyte Morphology and Kidney Function

PubMed Central

Azeloglu, Evren U.; Hardy, Simon V.; Eungdamrong, Narat John; Chen, Yibang; Jayaraman, Gomathi; Chuang, Peter Y.; Fang, Wei; Xiong, Huabao; Neves, Susana R.; Jain, Mohit R.; Li, Hong; Ma’ayan, Avi; Gordon, Ronald E.; He, John Cijiang; Iyengar, Ravi

2014-01-01

Podocytes are kidney cells with specialized morphology that is required for glomerular filtration. Diseases, such as diabetes, or drug exposure that causes disruption of the podocyte foot process morphology results in kidney pathophysiology. Proteomic analysis of glomeruli isolated from rats with puromycin-induced kidney disease and control rats indicated that protein kinase A (PKA), which is activated by adenosine 3′,5′-monophosphate (cAMP), is a key regulator of podocyte morphology and function. In podocytes, cAMP signaling activates cAMP response element–binding protein (CREB) to enhance expression of the gene encoding a differentiation marker, synaptopodin, a protein that associates with actin and promotes its bundling. We constructed and experimentally verified a β-adrenergic receptor–driven network with multiple feedback and feedforward motifs that controls CREB activity. To determine how the motifs interacted to regulate gene expression, we mapped multicompartment dynamical models, including information about protein subcellular localization, onto the network topology using Petri net formalisms. These computational analyses indicated that the juxtaposition of multiple feedback and feedforward motifs enabled the prolonged CREB activation necessary for synaptopodin expression and actin bundling. Drug-induced modulation of these motifs in diseased rats led to recovery of normal morphology and physiological function in vivo. Thus, analysis of regulatory motifs using network dynamics can provide insights into pathophysiology that enable predictions for drug intervention strategies to treat kidney disease. PMID:24497609

Effects of Increasing Neuromuscular Electrical Stimulation Current Intensity on Cortical Sensorimotor Network Activation: A Time Domain fNIRS Study

PubMed Central

Zucchelli, Lucia; Perrey, Stephane; Contini, Davide; Caffini, Matteo; Spinelli, Lorenzo; Kerr, Graham; Quaresima, Valentina; Ferrari, Marco; Torricelli, Alessandro

2015-01-01

Neuroimaging studies have shown neuromuscular electrical stimulation (NMES)-evoked movements activate regions of the cortical sensorimotor network, including the primary sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), and secondary somatosensory area (S2), as well as regions of the prefrontal cortex (PFC) known to be involved in pain processing. The aim of this study, on nine healthy subjects, was to compare the cortical network activation profile and pain ratings during NMES of the right forearm wrist extensor muscles at increasing current intensities up to and slightly over the individual maximal tolerated intensity (MTI), and with reference to voluntary (VOL) wrist extension movements. By exploiting the capability of the multi-channel time domain functional near-infrared spectroscopy technique to relate depth information to the photon time-of-flight, the cortical and superficial oxygenated (O2Hb) and deoxygenated (HHb) hemoglobin concentrations were estimated. The O2Hb and HHb maps obtained using the General Linear Model (NIRS-SPM) analysis method, showed that the VOL and NMES-evoked movements significantly increased activation (i.e., increase in O2Hb and corresponding decrease in HHb) in the cortical layer of the contralateral sensorimotor network (SMC, PMC/SMA, and S2). However, the level and area of contralateral sensorimotor network (including PFC) activation was significantly greater for NMES than VOL. Furthermore, there was greater bilateral sensorimotor network activation with the high NMES current intensities which corresponded with increased pain ratings. In conclusion, our findings suggest that greater bilateral sensorimotor network activation profile with high NMES current intensities could be in part attributable to increased attentional/pain processing and to increased bilateral sensorimotor integration in these cortical regions. PMID:26158464

Effects of Increasing Neuromuscular Electrical Stimulation Current Intensity on Cortical Sensorimotor Network Activation: A Time Domain fNIRS Study.

PubMed

Muthalib, Makii; Re, Rebecca; Zucchelli, Lucia; Perrey, Stephane; Contini, Davide; Caffini, Matteo; Spinelli, Lorenzo; Kerr, Graham; Quaresima, Valentina; Ferrari, Marco; Torricelli, Alessandro

2015-01-01

Neuroimaging studies have shown neuromuscular electrical stimulation (NMES)-evoked movements activate regions of the cortical sensorimotor network, including the primary sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), and secondary somatosensory area (S2), as well as regions of the prefrontal cortex (PFC) known to be involved in pain processing. The aim of this study, on nine healthy subjects, was to compare the cortical network activation profile and pain ratings during NMES of the right forearm wrist extensor muscles at increasing current intensities up to and slightly over the individual maximal tolerated intensity (MTI), and with reference to voluntary (VOL) wrist extension movements. By exploiting the capability of the multi-channel time domain functional near-infrared spectroscopy technique to relate depth information to the photon time-of-flight, the cortical and superficial oxygenated (O2Hb) and deoxygenated (HHb) hemoglobin concentrations were estimated. The O2Hb and HHb maps obtained using the General Linear Model (NIRS-SPM) analysis method, showed that the VOL and NMES-evoked movements significantly increased activation (i.e., increase in O2Hb and corresponding decrease in HHb) in the cortical layer of the contralateral sensorimotor network (SMC, PMC/SMA, and S2). However, the level and area of contralateral sensorimotor network (including PFC) activation was significantly greater for NMES than VOL. Furthermore, there was greater bilateral sensorimotor network activation with the high NMES current intensities which corresponded with increased pain ratings. In conclusion, our findings suggest that greater bilateral sensorimotor network activation profile with high NMES current intensities could be in part attributable to increased attentional/pain processing and to increased bilateral sensorimotor integration in these cortical regions.

Infraslow Electroencephalographic and Dynamic Resting State Network Activity.

PubMed

Grooms, Joshua K; Thompson, Garth J; Pan, Wen-Ju; Billings, Jacob; Schumacher, Eric H; Epstein, Charles M; Keilholz, Shella D

2017-06-01

A number of studies have linked the blood oxygenation level dependent (BOLD) signal to electroencephalographic (EEG) signals in traditional frequency bands (δ, θ, α, β, and γ), but the relationship between BOLD and its direct frequency correlates in the infraslow band (<1 Hz) has been little studied. Previously, work in rodents showed that infraslow local field potentials play a role in functional connectivity, particularly in the dynamic organization of large-scale networks. To examine the relationship between infraslow activity and network dynamics in humans, direct current (DC) EEG and resting state magnetic resonance imaging data were acquired simultaneously. The DC EEG signals were correlated with the BOLD signal in patterns that resembled resting state networks. Subsequent dynamic analysis showed that the correlation between DC EEG and the BOLD signal varied substantially over time, even within individual subjects. The variation in DC EEG appears to reflect the time-varying contribution of different resting state networks. Furthermore, some of the patterns of DC EEG and BOLD correlation are consistent with previous work demonstrating quasiperiodic spatiotemporal patterns of large-scale network activity in resting state. These findings demonstrate that infraslow electrical activity is linked to BOLD fluctuations in humans and that it may provide a basis for large-scale organization comparable to that observed in animal studies.

Infraslow Electroencephalographic and Dynamic Resting State Network Activity

PubMed Central

Grooms, Joshua K.; Thompson, Garth J.; Pan, Wen-Ju; Billings, Jacob; Schumacher, Eric H.; Epstein, Charles M.

2017-01-01

Abstract A number of studies have linked the blood oxygenation level dependent (BOLD) signal to electroencephalographic (EEG) signals in traditional frequency bands (δ, θ, α, β, and γ), but the relationship between BOLD and its direct frequency correlates in the infraslow band (<1 Hz) has been little studied. Previously, work in rodents showed that infraslow local field potentials play a role in functional connectivity, particularly in the dynamic organization of large-scale networks. To examine the relationship between infraslow activity and network dynamics in humans, direct current (DC) EEG and resting state magnetic resonance imaging data were acquired simultaneously. The DC EEG signals were correlated with the BOLD signal in patterns that resembled resting state networks. Subsequent dynamic analysis showed that the correlation between DC EEG and the BOLD signal varied substantially over time, even within individual subjects. The variation in DC EEG appears to reflect the time-varying contribution of different resting state networks. Furthermore, some of the patterns of DC EEG and BOLD correlation are consistent with previous work demonstrating quasiperiodic spatiotemporal patterns of large-scale network activity in resting state. These findings demonstrate that infraslow electrical activity is linked to BOLD fluctuations in humans and that it may provide a basis for large-scale organization comparable to that observed in animal studies. PMID:28462586

Comparison of functional network connectivity for passive-listening and active-response narrative comprehension in adolescents.

PubMed

Wang, Yingying; Holland, Scott K

2014-05-01

Comprehension of narrative stories plays an important role in the development of language skills. In this study, we compared brain activity elicited by a passive-listening version and an active-response (AR) version of a narrative comprehension task by using independent component (IC) analysis on functional magnetic resonance imaging data from 21 adolescents (ages 14-18 years). Furthermore, we explored differences in functional network connectivity engaged by two versions of the task and investigated the relationship between the online response time and the strength of connectivity between each pair of ICs. Despite similar brain region involvements in auditory, temporoparietal, and frontoparietal language networks for both versions, the AR version engages some additional network elements including the left dorsolateral prefrontal, anterior cingulate, and sensorimotor networks. These additional involvements are likely associated with working memory and maintenance of attention, which can be attributed to the differences in cognitive strategic aspects of the two versions. We found significant positive correlation between the online response time and the strength of connectivity between an IC in left inferior frontal region and an IC in sensorimotor region. An explanation for this finding is that longer reaction time indicates stronger connection between the frontal and sensorimotor networks caused by increased activation in adolescents who require more effort to complete the task.

Research and application of knowledge resources network for product innovation.

PubMed

Li, Chuan; Li, Wen-qiang; Li, Yan; Na, Hui-zhen; Shi, Qian

2015-01-01

In order to enhance the capabilities of knowledge service in product innovation design service platform, a method of acquiring knowledge resources supporting for product innovation from the Internet and providing knowledge active push is proposed. Through knowledge modeling for product innovation based on ontology, the integrated architecture of knowledge resources network is put forward. The technology for the acquisition of network knowledge resources based on focused crawler and web services is studied. Knowledge active push is provided for users by user behavior analysis and knowledge evaluation in order to improve users' enthusiasm for participation in platform. Finally, an application example is illustrated to prove the effectiveness of the method.

Network analysis of online bidding activity

NASA Astrophysics Data System (ADS)

Yang, I.; Oh, E.; Kahng, B.

2006-07-01

With the advent of digital media, people are increasingly resorting to online channels for commercial transactions. The online auction is a prototypical example. In such online transactions, the pattern of bidding activity is more complex than traditional offline transactions; this is because the number of bidders participating in a given transaction is not bounded and the bidders can also easily respond to the bidding instantaneously. By using the recently developed network theory, we study the interaction patterns between bidders (items) who (that) are connected when they bid for the same item (if the item is bid by the same bidder). The resulting network is analyzed by using the hierarchical clustering algorithm, which is used for clustering analysis for expression data from DNA microarrays. A dendrogram is constructed for the item subcategories; this dendrogram is compared to a traditional classification scheme. The implication of the difference between the two is discussed.

Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated?

PubMed

Emmert, Kirsten; Kopel, Rotem; Sulzer, James; Brühl, Annette B; Berman, Brian D; Linden, David E J; Horovitz, Silvina G; Breimhorst, Markus; Caria, Andrea; Frank, Sabine; Johnston, Stephen; Long, Zhiying; Paret, Christian; Robineau, Fabien; Veit, Ralf; Bartsch, Andreas; Beckmann, Christian F; Van De Ville, Dimitri; Haller, Sven

2016-01-01

An increasing number of studies using real-time fMRI neurofeedback have demonstrated that successful regulation of neural activity is possible in various brain regions. Since these studies focused on the regulated region(s), little is known about the target-independent mechanisms associated with neurofeedback-guided control of brain activation, i.e. the regulating network. While the specificity of the activation during self-regulation is an important factor, no study has effectively determined the network involved in self-regulation in general. In an effort to detect regions that are responsible for the act of brain regulation, we performed a post-hoc analysis of data involving different target regions based on studies from different research groups. We included twelve suitable studies that examined nine different target regions amounting to a total of 175 subjects and 899 neurofeedback runs. Data analysis included a standard first- (single subject, extracting main paradigm) and second-level (single subject, all runs) general linear model (GLM) analysis of all participants taking into account the individual timing. Subsequently, at the third level, a random effects model GLM included all subjects of all studies, resulting in an overall mixed effects model. Since four of the twelve studies had a reduced field of view (FoV), we repeated the same analysis in a subsample of eight studies that had a well-overlapping FoV to obtain a more global picture of self-regulation. The GLM analysis revealed that the anterior insula as well as the basal ganglia, notably the striatum, were consistently active during the regulation of brain activation across the studies. The anterior insula has been implicated in interoceptive awareness of the body and cognitive control. Basal ganglia are involved in procedural learning, visuomotor integration and other higher cognitive processes including motivation. The larger FoV analysis yielded additional activations in the anterior cingulate cortex, the dorsolateral and ventrolateral prefrontal cortex, the temporo-parietal area and the visual association areas including the temporo-occipital junction. In conclusion, we demonstrate that several key regions, such as the anterior insula and the basal ganglia, are consistently activated during self-regulation in real-time fMRI neurofeedback independent of the targeted region-of-interest. Our results imply that if the real-time fMRI neurofeedback studies target regions of this regulation network, such as the anterior insula, care should be given whether activation changes are related to successful regulation, or related to the regulation process per se. Furthermore, future research is needed to determine how activation within this regulation network is related to neurofeedback success. Copyright © 2015 Elsevier Inc. All rights reserved.

Graph-based network analysis of resting-state functional MRI.

PubMed

Wang, Jinhui; Zuo, Xinian; He, Yong

2010-01-01

In the past decade, resting-state functional MRI (R-fMRI) measures of brain activity have attracted considerable attention. Based on changes in the blood oxygen level-dependent signal, R-fMRI offers a novel way to assess the brain's spontaneous or intrinsic (i.e., task-free) activity with both high spatial and temporal resolutions. The properties of both the intra- and inter-regional connectivity of resting-state brain activity have been well documented, promoting our understanding of the brain as a complex network. Specifically, the topological organization of brain networks has been recently studied with graph theory. In this review, we will summarize the recent advances in graph-based brain network analyses of R-fMRI signals, both in typical and atypical populations. Application of these approaches to R-fMRI data has demonstrated non-trivial topological properties of functional networks in the human brain. Among these is the knowledge that the brain's intrinsic activity is organized as a small-world, highly efficient network, with significant modularity and highly connected hub regions. These network properties have also been found to change throughout normal development, aging, and in various pathological conditions. The literature reviewed here suggests that graph-based network analyses are capable of uncovering system-level changes associated with different processes in the resting brain, which could provide novel insights into the understanding of the underlying physiological mechanisms of brain function. We also highlight several potential research topics in the future.

Structural and functional social network attributes moderate the association of self-rated health with mental health in midlife and older adults.

PubMed

Windsor, Tim D; Rioseco, Pilar; Fiori, Katherine L; Curtis, Rachel G; Booth, Heather

2016-01-01

Social relationships are multifaceted, and different social network components can operate via different processes to influence well-being. This study examined associations of social network structure and relationship quality (positive and negative social exchanges) with mental health in midlife and older adults. The focus was on both direct associations of network structure and relationship quality with mental health, and whether these social network attributes moderated the association of self-rated health (SRH) with mental health. Analyses were based on survey data provided by 2001 (Mean age = 65, SD = 8.07) midlife and older adults. We used Latent Class Analysis (LCA) to classify participants into network types based on network structure (partner status, network size, contact frequency, and activity engagement), and used continuous measures of positive and negative social exchanges to operationalize relationship quality. Regression analysis was used to test moderation. LCA revealed network types generally consistent with those reported in previous studies. Participants in more diverse networks reported better mental health than those categorized into a restricted network type after adjustment for age, sex, education, and employment status. Analysis of moderation indicated that those with poorer SRH were less likely to report poorer mental health if they were classified into more diverse networks. A similar moderation effect was also evident for positive exchanges. The findings suggest that both quantity and quality of social relationships can play a role in buffering against the negative implications of physical health decline for mental health.

Bayesian network analysis revealed the connectivity difference of the default mode network from the resting-state to task-state

PubMed Central

Wu, Xia; Yu, Xinyu; Yao, Li; Li, Rui

2014-01-01

Functional magnetic resonance imaging (fMRI) studies have converged to reveal the default mode network (DMN), a constellation of regions that display co-activation during resting-state but co-deactivation during attention-demanding tasks in the brain. Here, we employed a Bayesian network (BN) analysis method to construct a directed effective connectivity model of the DMN and compared the organizational architecture and interregional directed connections under both resting-state and task-state. The analysis results indicated that the DMN was consistently organized into two closely interacting subsystems in both resting-state and task-state. The directed connections between DMN regions, however, changed significantly from the resting-state to task-state condition. The results suggest that the DMN intrinsically maintains a relatively stable structure whether at rest or performing tasks but has different information processing mechanisms under varied states. PMID:25309414

Aberrant neural networks for the recognition memory of socially relevant information in patients with schizophrenia.

PubMed

Oh, Jooyoung; Chun, Ji-Won; Kim, Eunseong; Park, Hae-Jeong; Lee, Boreom; Kim, Jae-Jin

2017-01-01

Patients with schizophrenia exhibit several cognitive deficits, including memory impairment. Problems with recognition memory can hinder socially adaptive behavior. Previous investigations have suggested that altered activation of the frontotemporal area plays an important role in recognition memory impairment. However, the cerebral networks related to these deficits are not known. The aim of this study was to elucidate the brain networks required for recognizing socially relevant information in patients with schizophrenia performing an old-new recognition task. Sixteen patients with schizophrenia and 16 controls participated in this study. First, the subjects performed the theme-identification task during functional magnetic resonance imaging. In this task, pictures depicting social situations were presented with three words, and the subjects were asked to select the best theme word for each picture. The subjects then performed an old-new recognition task in which they were asked to discriminate whether the presented words were old or new. Task performance and neural responses in the old-new recognition task were compared between the subject groups. An independent component analysis of the functional connectivity was performed. The patients with schizophrenia exhibited decreased discriminability and increased activation of the right superior temporal gyrus compared with the controls during correct responses. Furthermore, aberrant network activities were found in the frontopolar and language comprehension networks in the patients. The functional connectivity analysis showed aberrant connectivity in the frontopolar and language comprehension networks in the patients with schizophrenia, and these aberrations possibly contribute to their low recognition performance and social dysfunction. These results suggest that the frontopolar and language comprehension networks are potential therapeutic targets in patients with schizophrenia.

Preliminary Evidence of Reduced Brain Network Activation in Patients with Post-traumatic Migraine following Concussion

PubMed Central

Kontos, Anthony P.; Reches, Amit; Elbin, R. J.; Dickman, Dalia; Laufer, Ilan; Geva, Amir; Shacham, Galit; DeWolf, Ryan; Collins, Michael W.

2015-01-01

Post-traumatic migraine (PTM) (i.e., headache, nausea, light and/or noise sensitivity) is an emerging risk factor for prolonged recovery following concussion. Concussions and migraine share similar pathophysiology characterized by specific ionic imbalances in the brain. Given these similarities, patients with PTM following concussion may exhibit distinct electrophysiological patterns, although researchers have yet to examine the electrophysiological brain activation in patients with PTM following concussion. A novel approach that may help differentiate brain activation in patients with and without PTM is brain network activation (BNA) analysis. BNA involves an algorithmic analysis applied to multichannel EEG-ERP data that provides a network map of cortical activity and quantitative data during specific tasks. A prospective, repeated measures design was used to evaluate BNA (during Go/NoGo task), EEG-ERP, cognitive performance, and concussion related symptoms at 1, 2, 3, and 4-week post-injury intervals among athletes with a medically diagnosed concussion with PTM (n = 15) and without (NO-PTM) (n = 22); and age, sex, and concussion history matched controls without concussion (CONTROL) (n = 20). Participants with PTM had significantly reduced BNA compared to NO-PTM and CONTROLS for Go and NoGo components at 3 weeks and for NoGo component at 4 weeks post-injury. The PTM group also demonstrated a more prominent deviation of network activity compared to the other two groups over a longer period of time. The composite BNA algorithm may be a more sensitive measure of electrophysiological change in the brain that can augment established cognitive assessment tools for detecting impairment in individuals with PTM. PMID:26091725

A data fusion approach to indications and warnings of terrorist attacks

NASA Astrophysics Data System (ADS)

McDaniel, David; Schaefer, Gregory

2014-05-01

Indications and Warning (I&W) of terrorist attacks, particularly IED attacks, require detection of networks of agents and patterns of behavior. Social Network Analysis tries to detect a network; activity analysis tries to detect anomalous activities. This work builds on both to detect elements of an activity model of terrorist attack activity - the agents, resources, networks, and behaviors. The activity model is expressed as RDF triples statements where the tuple positions are elements or subsets of a formal ontology for activity models. The advantage of a model is that elements are interdependent and evidence for or against one will influence others so that there is a multiplier effect. The advantage of the formality is that detection could occur hierarchically, that is, at different levels of abstraction. The model matching is expressed as a likelihood ratio between input text and the model triples. The likelihood ratio is designed to be analogous to track correlation likelihood ratios common in JDL fusion level 1. This required development of a semantic distance metric for positive and null hypotheses as well as for complex objects. The metric uses the Web 1Terabype database of one to five gram frequencies for priors. This size requires the use of big data technologies so a Hadoop cluster is used in conjunction with OpenNLP natural language and Mahout clustering software. Distributed data fusion Map Reduce jobs distribute parts of the data fusion problem to the Hadoop nodes. For the purposes of this initial testing, open source models and text inputs of similar complexity to terrorist events were used as surrogates for the intended counter-terrorist application.

Design and implementation of dynamic hybrid Honeypot network

NASA Astrophysics Data System (ADS)

Qiao, Peili; Hu, Shan-Shan; Zhai, Ji-Qiang

2013-05-01

The method of constructing a dynamic and self-adaptive virtual network is suggested to puzzle adversaries, delay and divert attacks, exhaust attacker resources and collect attacking information. The concepts of Honeypot and Honeyd, which is the frame of virtual Honeypot are introduced. The techniques of network scanning including active fingerprint recognition are analyzed. Dynamic virtual network system is designed and implemented. A virtual network similar to real network topology is built according to the collected messages from real environments in this system. By doing this, the system can perplex the attackers when Hackers attack and can further analyze and research the attacks. The tests to this system prove that this design can successfully simulate real network environment and can be used in network security analysis.

Social network analysis of Iranian researchers in the field of violence.

PubMed

Salamati, Payman; Soheili, Faramarz

2016-10-01

The social network analysis (SNA) is a paradigm for analyzing structural patterns in social re- lations, testing knowledge sharing process and identifying bottlenecks of information flow. The purpose of this study was to determine the status of research in the fleld of violence in Iran using SNA. Research population included all the papers with at least one Iranian affiliation published in violence fleld indexed in SCIE, PubMed and Scopus databases. The co-word maps, co-authorship network and structural holes were drawn using related software. In the next step, the active authors and some measures of our network including degree centrality (DC), closeness, eigenvector, betweeness, density, diameter, compactness and size of the main component were assessed. Likewise, the trend of the published articles was evaluated based on the number of documents and their citations from 1972 to 2014. Five hundred and seventy one records were obtained. The five main clusters and hot spots were mental health, violence, war, psychiatric disorders and suicide. The co-authorship network was complex, tangled and scale free. The top nine authors with cut point role and top ten active authors were identified. The mean (standard deviation) of normalized DC, closeness, eigenvector and betweeness were 0.449 (0.805), 0.609 (0.214), 2.373 (7.353) and 0.338 (1.122), respectively. The density, diameter and mean compactness of our co-authorship network were 0.0494, 3.955 and 0.125, respectively. The main component consisted of 216 nodes that formed 17% of total size of the network. Both the number of the documents and their citations has increased in the field of violence in the recent years. Although the number of the documents has recently increased in the field of violence, the information flow is slow and there are not many relations among the authors in the network. However, the active authors have ability to influence the flow of knowledge within the network.

Interorganizational relationships within state tobacco control networks: a social network analysis.

PubMed

Krauss, Melissa; Mueller, Nancy; Luke, Douglas

2004-10-01

State tobacco control programs are implemented by networks of public and private agencies with a common goal to reduce tobacco use. The degree of a program's comprehensiveness depends on the scope of its activities and the variety of agencies involved in the network. Structural aspects of these networks could help describe the process of implementing a state's tobacco control program, but have not yet been examined. Social network analysis was used to examine the structure of five state tobacco control networks. Semi-structured interviews with key agencies collected quantitative and qualitative data on frequency of contact among network partners, money flow, relationship productivity, level of network effectiveness, and methods for improvement. Most states had hierarchical communication structures in which partner agencies had frequent contact with one or two central agencies. Lead agencies had the highest control over network communication. Networks with denser communication structures had denser productivity structures. Lead agencies had the highest financial influence within the networks, while statewide coalitions were financially influenced by others. Lead agencies had highly productive relationships with others, while agencies with narrow roles had fewer productive relationships. Statewide coalitions that received Robert Wood Johnson Foundation funding had more highly productive relationships than coalitions that did not receive the funding. Results suggest that frequent communication among network partners is related to more highly productive relationships. Results also highlight the importance of lead agencies and statewide coalitions in implementing a comprehensive state tobacco control program. Network analysis could be useful in developing process indicators for state tobacco control programs.

Dynamics and nature of support in the personal networks of people with type 2 diabetes living in Europe: qualitative analysis of network properties.

PubMed

Kennedy, Anne; Rogers, Anne; Vassilev, Ivaylo; Todorova, Elka; Roukova, Poli; Foss, Christina; Knutsen, Ingrid; Portillo, Mari Carmen; Mujika, Agurtzane; Serrano-Gil, Manuel; Lionis, Christos; Angelaki, Agapi; Ratsika, Nikoleta; Koetsenruijter, Jan; Wensing, Michel

2015-12-01

Living with and self-managing a long-term condition implicates a diversity of networked relationships. This qualitative study examines the personal communities of support of people with type 2 diabetes. We conducted 170 biographical interviews in six European countries (Bulgaria, Greece, the Netherlands, Norway, Spain and UK) to explore social support and networks. Analysis was framed with reference to three predetermined social support mechanisms: the negotiation of support enabling engagement with healthy practices, navigation to sources of support and collective efficacy. Each interview was summarized to describe navigation and negotiation of participants' networks and the degree of collective efficacy. Analysis highlighted the similarities and differences between countries and provided insights into capacities of networks to support self-management. The network support mechanisms were identified in all interviews, and losses and gains in networks impacted on diabetes management. There were contextual differences between countries, most notably the impact of financial austerity on network dynamics. Four types of network are suggested: generative, diverse and beneficial to individuals; proxy, network members undertook diabetes management work; avoidant, support not engaged with; and struggling, diabetes management a struggle or not prioritized. It is possible to differentiate types of network input to living with and managing diabetes. Recognizing the nature of active, generative aspects of networks support is likely to have relevance for self-management support interventions either through encouraging continuing development and maintenance of these contacts or intervening to address struggling networks through introducing the means to connect people to additional sources of support. © 2014 John Wiley & Sons Ltd.

Increased sensorimotor network activity in DYT1 dystonia: a functional imaging study

PubMed Central

Argyelan, Miklos; Habeck, Christian; Ghilardi, M. Felice; Fitzpatrick, Toni; Dhawan, Vijay; Pourfar, Michael; Bressman, Susan B.; Eidelberg, David

2010-01-01

Neurophysiological studies have provided evidence of primary motor cortex hyperexcitability in primary dystonia, but several functional imaging studies suggest otherwise. To address this issue, we measured sensorimotor activation at both the regional and network levels in carriers of the DYT1 dystonia mutation and in control subjects. We used 15Oxygen-labelled water and positron emission tomography to scan nine manifesting DYT1 carriers, 10 non-manifesting DYT1 carriers and 12 age-matched controls while they performed a kinematically controlled motor task; they were also scanned in a non-motor audio-visual control condition. Within- and between-group contrasts were analysed with statistical parametric mapping. For network analysis, we first identified a normal motor-related activation pattern in a set of 39 motor and audio-visual scans acquired in an independent cohort of 18 healthy volunteer subjects. The expression of this pattern was prospectively quantified in the motor and control scans acquired in each of the gene carriers and controls. Network values for the three groups were compared with ANOVA and post hoc contrasts. Voxel-wise comparison of DYT1 carriers and controls revealed abnormally increased motor activation responses in the former group (P < 0.05, corrected; statistical parametric mapping), localized to the sensorimotor cortex, dorsal premotor cortex, supplementary motor area and the inferior parietal cortex. Network analysis of the normative derivation cohort revealed a significant normal motor-related activation pattern topography (P < 0.0001) characterized by covarying neural activity in the sensorimotor cortex, dorsal premotor cortex, supplementary motor area and cerebellum. In the study cohort, normal motor-related activation pattern expression measured during movement was abnormally elevated in the manifesting gene carriers (P < 0.001) but not in their non-manifesting counterparts. In contrast, in the non-motor control condition, abnormal increases in network activity were present in both groups of gene carriers (P < 0.001). In this condition, normal motor-related activation pattern expression in non-manifesting carriers was greater than in controls, but lower than in affected carriers. In the latter group, measures of normal motor-related activation pattern expression in the audio-visual condition correlated with independent dystonia clinical ratings (r = 0.70, P = 0.04). These findings confirm that overexcitability of the sensorimotor system is a robust feature of dystonia. The presence of elevated normal motor-related activation pattern expression in the non-motor condition suggests that abnormal integration of audio-visual input with sensorimotor network activity is an important trait feature of this disorder. Lastly, quantification of normal motor-related activation pattern expression in individual cases may have utility as an objective descriptor of therapeutic response in trials of new treatments for dystonia and related disorders. PMID:20207699

Network Analysis of Reconnaissance and Intrusion of an Industrial Control System

DTIC Science & Technology

2016-09-01

simulated a plant engineer using the engineering workstation web browser to authenticate to the vegetable cooker HMI. While the engineer established the...observed the vegetable cooker HMI web display, the attacker stopped capturing network traffic. Acting as the attacker, we searched the attacker’s pcap...manually controlled by human activity. In this testbed network, only web browser traffic (HTTP) is created by an operator to view an HMI status

Applications of artificial neural network in AIDS research and therapy.

PubMed

Sardari, S; Sardari, D

2002-01-01

In recent years considerable effort has been devoted to applying pattern recognition techniques to the complex task of data analysis in drug research. Artificial neural networks (ANN) methodology is a modeling method with great ability to adapt to a new situation, or control an unknown system, using data acquired in previous experiments. In this paper, a brief history of ANN and the basic concepts behind the computing, the mathematical and algorithmic formulation of each of the techniques, and their developmental background is presented. Based on the abilities of ANNs in pattern recognition and estimation of system outputs from the known inputs, the neural network can be considered as a tool for molecular data analysis and interpretation. Analysis by neural networks improves the classification accuracy, data quantification and reduces the number of analogues necessary for correct classification of biologically active compounds. Conformational analysis and quantifying the components in mixtures using NMR spectra, aqueous solubility prediction and structure-activity correlation are among the reported applications of ANN as a new modeling method. Ranging from drug design and discovery to structure and dosage form design, the potential pharmaceutical applications of the ANN methodology are significant. In the areas of clinical monitoring, utilization of molecular simulation and design of bioactive structures, ANN would make the study of the status of the health and disease possible and brings their predicted chemotherapeutic response closer to reality.

Recovery of rhythmic activity in a central pattern generator: analysis of the role of neuromodulator and activity-dependent mechanisms.

PubMed

Zhang, Yili; Golowasch, Jorge

2011-11-01

The pyloric network of decapods crustaceans can undergo dramatic rhythmic activity changes. Under normal conditions the network generates low frequency rhythmic activity that depends obligatorily on the presence of neuromodulatory input from the central nervous system. When this input is removed (decentralization) the rhythmic activity ceases. In the continued absence of this input, periodic activity resumes after a few hours in the form of episodic bursting across the entire network that later turns into stable rhythmic activity that is nearly indistinguishable from control (recovery). It has been proposed that an activity-dependent modification of ionic conductance levels in the pyloric pacemaker neuron drives the process of recovery of activity. Previous modeling attempts have captured some aspects of the temporal changes observed experimentally, but key features could not be reproduced. Here we examined a model in which slow activity-dependent regulation of ionic conductances and slower neuromodulator-dependent regulation of intracellular Ca(2+) concentration reproduce all the temporal features of this recovery. Key aspects of these two regulatory mechanisms are their independence and their different kinetics. We also examined the role of variability (noise) in the activity-dependent regulation pathway and observe that it can help to reduce unrealistic constraints that were otherwise required on the neuromodulator-dependent pathway. We conclude that small variations in intracellular Ca(2+) concentration, a Ca(2+) uptake regulation mechanism that is directly targeted by neuromodulator-activated signaling pathways, and variability in the Ca(2+) concentration sensing signaling pathway can account for the observed changes in neuronal activity. Our conclusions are all amenable to experimental analysis.

OncoPPi-informed discovery of mitogen-activated protein kinase kinase 3 as a novel binding partner of c-Myc | Office of Cancer Genomics

Cancer.gov

Mitogen-activated protein kinase kinase 3 (MKK3) is a dual threonine/tyrosine protein kinase that regulates inflammation, proliferation and apoptosis through specific phosphorylation and activation of the p38 mitogen-activated protein kinase. However, the role of MKK3 beyond p38-signaling remains elusive. Recently, we reported a protein-protein interaction (PPI) network of cancer-associated genes, termed OncoPPi, as a resource for the scientific community to generate new biological models. Analysis of the OncoPPi connectivity identified MKK3 as one of the major hub proteins in the network.

Global Mittag-Leffler stability analysis of fractional-order impulsive neural networks with one-side Lipschitz condition.

PubMed

Zhang, Xinxin; Niu, Peifeng; Ma, Yunpeng; Wei, Yanqiao; Li, Guoqiang

2017-10-01

This paper is concerned with the stability analysis issue of fractional-order impulsive neural networks. Under the one-side Lipschitz condition or the linear growth condition of activation function, the existence of solution is analyzed respectively. In addition, the existence, uniqueness and global Mittag-Leffler stability of equilibrium point of the fractional-order impulsive neural networks with one-side Lipschitz condition are investigated by the means of contraction mapping principle and Lyapunov direct method. Finally, an example with numerical simulation is given to illustrate the validity and feasibility of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Laboratory surveillance for wild and vaccine-derived polioviruses, January 2004-June 2005.

PubMed

2005-09-30

A global network of 145 virology laboratories has been established by the World Health Organization (WHO) to support surveillance activities of the Polio Eradication Initiative (PEI). The Global Polio Laboratory Network analyzes stool specimens from patients with acute flaccid paralysis (AFP) and environmental samples for the presence of polioviruses. Surveillance systems detect at least one AFP case per 100,000 persons aged <15 years, collect adequate stool samples from patients, and send the samples to network laboratories for analysis. Laboratory data are used to identify locations where wild polioviruses (WPVs) or vaccine-derived polioviruses (VDPVs) are circulating, target supplementary immunization activities (SIAs) to interrupt transmission chains, and investigate genetic relationships among viral isolates. This report updates previous publications and describes the laboratory network's performance during the period January 2004-June 2005.

Locking of correlated neural activity to ongoing oscillations

PubMed Central

Helias, Moritz

2017-01-01

Population-wide oscillations are ubiquitously observed in mesoscopic signals of cortical activity. In these network states a global oscillatory cycle modulates the propensity of neurons to fire. Synchronous activation of neurons has been hypothesized to be a separate channel of signal processing information in the brain. A salient question is therefore if and how oscillations interact with spike synchrony and in how far these channels can be considered separate. Experiments indeed showed that correlated spiking co-modulates with the static firing rate and is also tightly locked to the phase of beta-oscillations. While the dependence of correlations on the mean rate is well understood in feed-forward networks, it remains unclear why and by which mechanisms correlations tightly lock to an oscillatory cycle. We here demonstrate that such correlated activation of pairs of neurons is qualitatively explained by periodically-driven random networks. We identify the mechanisms by which covariances depend on a driving periodic stimulus. Mean-field theory combined with linear response theory yields closed-form expressions for the cyclostationary mean activities and pairwise zero-time-lag covariances of binary recurrent random networks. Two distinct mechanisms cause time-dependent covariances: the modulation of the susceptibility of single neurons (via the external input and network feedback) and the time-varying variances of single unit activities. For some parameters, the effectively inhibitory recurrent feedback leads to resonant covariances even if mean activities show non-resonant behavior. Our analytical results open the question of time-modulated synchronous activity to a quantitative analysis. PMID:28604771

The Collaborative Action Research Network: 30 Years of Agency in Developing Educational Action Research

ERIC Educational Resources Information Center

Somekh, Bridget

2010-01-01

This article provides an analysis of the Collaborative Action Research Network's (CARN) origins and development since its foundation in 1976. The author brings the unique perspective of active involvement in CARN almost from its inception, and editorship for many years of its journal "Educational Action Research". Cultural-historical…

Emergent Complex Behavior in Social Networks: Examples from the Ktunaxa Speech Community

ERIC Educational Resources Information Center

Horsethief, Christopher

2012-01-01

Language serves as a primary tool for structuring identity and loss of language represents the loss of that identity. This study utilizes a social network analysis of Ktunaxa speech community activities for evidence of internally generated revitalization efforts. These behaviors include instances of self-organized emergence. Such emergent behavior…

Examining the Impact of Pre-Induction Social Networking on the Student Transition into Higher Education

ERIC Educational Resources Information Center

Ribchester, Chris; Ross, Kim; Rees, Emma L. E.

2014-01-01

This research paper considers how bespoke online social networks have been used to support students' transition into higher education during the weeks immediately prior to formal "on-site" induction. An analysis of online activities showed some differences in the pattern of engagement between two contrasting departments (Geography and…

Network Analysis Reveals a Common Host-Pathogen Interaction Pattern in Arabidopsis Immune Responses.

PubMed

Li, Hong; Zhou, Yuan; Zhang, Ziding

2017-01-01

Many plant pathogens secrete virulence effectors into host cells to target important proteins in host cellular network. However, the dynamic interactions between effectors and host cellular network have not been fully understood. Here, an integrative network analysis was conducted by combining Arabidopsis thaliana protein-protein interaction network, known targets of Pseudomonas syringae and Hyaloperonospora arabidopsidis effectors, and gene expression profiles in the immune response. In particular, we focused on the characteristic network topology of the effector targets and differentially expressed genes (DEGs). We found that effectors tended to manipulate key network positions with higher betweenness centrality. The effector targets, especially those that are common targets of an individual effector, tended to be clustered together in the network. Moreover, the distances between the effector targets and DEGs increased over time during infection. In line with this observation, pathogen-susceptible mutants tended to have more DEGs surrounding the effector targets compared with resistant mutants. Our results suggest a common plant-pathogen interaction pattern at the cellular network level, where pathogens employ potent local impact mode to interfere with key positions in the host network, and plant organizes an in-depth defense by sequentially activating genes distal to the effector targets.

Spatial Fingerprints of Community Structure in Human Interaction Network for an Extensive Set of Large-Scale Regions

PubMed Central

Kallus, Zsófia; Barankai, Norbert; Szüle, János; Vattay, Gábor

2015-01-01

Human interaction networks inferred from country-wide telephone activity recordings were recently used to redraw political maps by projecting their topological partitions into geographical space. The results showed remarkable spatial cohesiveness of the network communities and a significant overlap between the redrawn and the administrative borders. Here we present a similar analysis based on one of the most popular online social networks represented by the ties between more than 5.8 million of its geo-located users. The worldwide coverage of their measured activity allowed us to analyze the large-scale regional subgraphs of entire continents and an extensive set of examples for single countries. We present results for North and South America, Europe and Asia. In our analysis we used the well-established method of modularity clustering after an aggregation of the individual links into a weighted graph connecting equal-area geographical pixels. Our results show fingerprints of both of the opposing forces of dividing local conflicts and of uniting cross-cultural trends of globalization. PMID:25993329

Temporal Lobe and “Default” Hemodynamic Brain Modes Discriminate Between Schizophrenia and Bipolar Disorder

PubMed Central

Calhoun, Vince D.; Maciejewski, Paul K.; Pearlson, Godfrey D.; Kiehl, Kent A.

2009-01-01

Schizophrenia and bipolar disorder are currently diagnosed on the basis of psychiatric symptoms and longitudinal course. The determination of a reliable, biologically-based diagnostic indicator of these diseases (a biomarker) could provide the groundwork for developing more rigorous tools for differential diagnosis and treatment assignment. Recently, methods have been used to identify distinct sets of brain regions or “spatial modes” exhibiting temporally coherent brain activity. Using functional magnetic resonance imaging (fMRI) data and a multivariate analysis method, independent component analysis, we combined the temporal lobe and the default modes to discriminate subjects with bipolar disorder, chronic schizophrenia, and healthy controls. Temporal lobe and default mode networks were reliably identified in all participants. Classification results on an independent set of individuals revealed an average sensitivity and specificity of 90 and 95%, respectively. The use of coherent brain networks such as the temporal lobe and default mode networks may provide a more reliable measure of disease state than task-correlated fMRI activity. A combination of two such hemodynamic brain networks shows promise as a biomarker for schizophrenia and bipolar disorder. PMID:17894392

Temporal lobe and "default" hemodynamic brain modes discriminate between schizophrenia and bipolar disorder.

PubMed

Calhoun, Vince D; Maciejewski, Paul K; Pearlson, Godfrey D; Kiehl, Kent A

2008-11-01

Schizophrenia and bipolar disorder are currently diagnosed on the basis of psychiatric symptoms and longitudinal course. The determination of a reliable, biologically-based diagnostic indicator of these diseases (a biomarker) could provide the groundwork for developing more rigorous tools for differential diagnosis and treatment assignment. Recently, methods have been used to identify distinct sets of brain regions or "spatial modes" exhibiting temporally coherent brain activity. Using functional magnetic resonance imaging (fMRI) data and a multivariate analysis method, independent component analysis, we combined the temporal lobe and the default modes to discriminate subjects with bipolar disorder, chronic schizophrenia, and healthy controls. Temporal lobe and default mode networks were reliably identified in all participants. Classification results on an independent set of individuals revealed an average sensitivity and specificity of 90 and 95%, respectively. The use of coherent brain networks such as the temporal lobe and default mode networks may provide a more reliable measure of disease state than task-correlated fMRI activity. A combination of two such hemodynamic brain networks shows promise as a biomarker for schizophrenia and bipolar disorder.

Network Analysis of Associations between Serum Interferon Alpha Activity, Autoantibodies, and Clinical Features in Systemic Lupus Erythematosus

PubMed Central

Weckerle, Corinna E.; Franek, Beverly S.; Kelly, Jennifer A.; Kumabe, Marissa; Mikolaitis, Rachel A.; Green, Stephanie L.; Utset, Tammy O.; Jolly, Meenakshi; James, Judith A.; Harley, John B.; Niewold, Timothy B.

2010-01-01

Background Interferon-alpha (IFN-α) is a primary pathogenic factor in systemic lupus erythematosus (SLE), and high IFN-α levels may be associated with particular clinical manifestations. The prevalence of individual clinical and serologic features differs significantly by ancestry. We used multivariate and network analyses to detect associations between clinical and serologic disease manifestations and serum IFN-α activity in a large diverse SLE cohort. Methods 1089 SLE patients were studied (387 African-American, 186 Hispanic-American, and 516 European-American). Presence or absence of ACR clinical criteria for SLE, autoantibodies, and serum IFN-α activity data were analyzed in univariate and multivariate models. Iterative multivariate logistic regression was performed in each background separately to establish the network of associations between variables that were independently significant following Bonferroni correction. Results In all ancestral backgrounds, high IFN-α activity was associated with anti-Ro and anti-dsDNA antibodies (p-values 4.6×10−18 and 2.9 × 10−16 respectively). Younger age, non-European ancestry, and anti-RNP were also independently associated with increased serum IFN-α activity (p≤6.7×10−4). We found 14 unique associations between variables in network analysis, and only 7 of these associations were shared by more than one ancestral background. Associations between clinical criteria were different in different ancestral backgrounds, while autoantibody-IFN-α relationships were similar across backgrounds. IFN-α activity and autoantibodies were not associated with ACR clinical features in multivariate models. Conclusions Serum IFN-α activity was strongly and consistently associated with autoantibodies, and not independently associated with clinical features in SLE. IFN-α may be more relevant to humoral tolerance and initial pathogenesis than later clinical disease manifestations. PMID:21162028

Forecasting Flare Activity Using Deep Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Hernandez, T.

2017-12-01

Current operational flare forecasting relies on human morphological analysis of active regions and the persistence of solar flare activity through time (i.e. that the Sun will continue to do what it is doing right now: flaring or remaining calm). In this talk we present the results of applying deep Convolutional Neural Networks (CNNs) to the problem of solar flare forecasting. CNNs operate by training a set of tunable spatial filters that, in combination with neural layer interconnectivity, allow CNNs to automatically identify significant spatial structures predictive for classification and regression problems. We will start by discussing the applicability and success rate of the approach, the advantages it has over non-automated forecasts, and how mining our trained neural network provides a fresh look into the mechanisms behind magnetic energy storage and release.

An ultrastructural analysis of platelets, erythrocytes, white blood cells, and fibrin network in systemic lupus erythematosus.

PubMed

Pretorius, Etheresia; du Plooy, Jenny; Soma, Prashilla; Gasparyan, Armen Yuri

2014-07-01

The study suggests that patients with systemic lupus erythematosus (SLE) present with distinct inflammatory ultrastructural changes such as platelets blebbing, generation of platelet-derived microparticles, spontaneous formation of massive fibrin network and fusion of the erythrocytes membranes. Lupoid platelets actively interact with other inflammatory cells, particularly with white blood cells (WBCs), and the massive fibrin network facilitates such an interaction. It is possible that the concerted actions of platelets, erythrocytes and WBC, caught in the inflammatory fibrin network, predispose to pro-thrombotic states in patients with SLE.

Neural bases of prospective memory: a meta-analysis and the "Attention to Delayed Intention" (AtoDI) model.

PubMed

Cona, Giorgia; Scarpazza, Cristina; Sartori, Giuseppe; Moscovitch, Morris; Bisiacchi, Patrizia Silvia

2015-05-01

Remembering to realize delayed intentions is a multi-phase process, labelled as prospective memory (PM), and involves a plurality of neural networks. The present study utilized the activation likelihood estimation method of meta-analysis to provide a complete overview of the brain regions that are consistently activated in each PM phase. We formulated the 'Attention to Delayed Intention' (AtoDI) model to explain the neural dissociation found between intention maintenance and retrieval phases. The dorsal frontoparietal network is involved mainly in the maintenance phase and seems to mediate the strategic monitoring processes, such as the allocation of top-down attention both towards external stimuli, to monitor for the occurrence of the PM cues, and to internal memory contents, to maintain the intention active in memory. The ventral frontoparietal network is recruited in the retrieval phase and might subserve the bottom-up attention captured externally by the PM cues and, internally, by the intention stored in memory. Together with other brain regions (i.e., insula and posterior cingulate cortex), the ventral frontoparietal network would support the spontaneous retrieval processes. The functional contribution of the anterior prefrontal cortex is discussed extensively for each PM phase. Copyright © 2015 Elsevier Ltd. All rights reserved.

Impact of real-time fMRI working memory feedback training on the interactions between three core brain networks.

PubMed

Zhang, Qiushi; Zhang, Gaoyan; Yao, Li; Zhao, Xiaojie

2015-01-01

Working memory (WM) refers to the temporary holding and manipulation of information during the performance of a range of cognitive tasks, and WM training is a promising method for improving an individual's cognitive functions. Our previous work demonstrated that WM performance can be improved through self-regulation of dorsal lateral prefrontal cortex (PFC) activation using real-time functional magnetic resonance imaging (rtfMRI), which enables individuals to control local brain activities volitionally according to the neurofeedback. Furthermore, research concerning large-scale brain networks has demonstrated that WM training requires the engagement of several networks, including the central executive network (CEN), the default mode network (DMN) and the salience network (SN), and functional connectivity within the CEN and DMN can be changed by WM training. Although a switching role of the SN between the CEN and DMN has been demonstrated, it remains unclear whether WM training can affect the interactions between the three networks and whether a similar mechanism also exists during the training process. In this study, we investigated the dynamic functional connectivity between the three networks during the rtfMRI feedback training using independent component analysis (ICA) and correlation analysis. The results indicated that functional connectivity within and between the three networks were significantly enhanced by feedback training, and most of the changes were associated with the insula and correlated with behavioral improvements. These findings suggest that the insula plays a critical role in the reorganization of functional connectivity among the three networks induced by rtfMRI training and in WM performance, thus providing new insights into the mechanisms of high-level functions and the clinical treatment of related functional impairments.

The growth of partnerships to support patient safety practice adoption.

PubMed

Mendel, Peter; Damberg, Cheryl L; Sorbero, Melony E S; Varda, Danielle M; Farley, Donna O

2009-04-01

To document the numbers and types of interorganizational partnerships within the national patient safety domain, changes over time in these networks, and their potential for disseminating patient safety knowledge and practices. Self-reported information gathered from representatives of national-level organizations active in promoting patient safety. Social network analysis was used to examine the structure and composition of partnership networks and changes between 2004 and 2006. Two rounds of structured telephone interviews (n=35 organizations in 2004 and 55 in 2006). Patient safety partnerships expanded between 2004 and 2006. The average number of partnerships per interviewed organization increased 40 percent and activities per reported partnership increased over 50 percent. Partnerships increased in all activity domains, particularly dissemination and tools development. Fragmentation of the overall partnership network decreased and potential for information flow increased. Yet network centralization increased, suggesting vulnerability to partnership failure if key participants disengage. Growth in partnerships signifies growing strength in the capacity to disseminate and implement patient safety advancements in the U.S. health care system. The centrality of AHRQ in these networks of partnerships bodes well for its leadership role in disseminating information, tools, and practices generated by patient safety research projects.

The Role of Surface Water for the Branching Geometry of Mars' Channel Networks

NASA Astrophysics Data System (ADS)

Seybold, H. F.; Rothman, D.; Kirchner, J. W.

2016-12-01

The controversy over the origin of Mars' channel networks is almost as old as their discovery 150 years ago. In recent decades, new Mars probe missions have revealed detailed network structures, and new studies suggest that Mars once had an active hydrologic cycle. But how this water flowed and how it could have carved these huge channel networks remains unclear. A recent analysis of high-resolution data for the Continental United States suggests that climate leaves a characteristic imprint in the branching geometry of stream networks: arid regions dominated by overland or near-surface flows have much narrower branching angles than humid regions with greater groundwater recharge. Based on this result we analyze the channel networks of Mars, and find that their geometry resembles those created by near-surface and overland flows on Earth. This result gives additional support to the hypothesis that Mars once had a more active hydrologic cycle, with liquid water flowing over its surface.

Glycogen debranching enzyme 6 (AGL), enolase 1 (ENOSF1), ectonucleotide pyrophosphatase 2 (ENPP2_1), glutathione S-transferase 3 (GSTM3_3) and mannosidase (MAN2B2) metabolism computational network analysis between chimpanzee and human left cerebrum.

PubMed

Sun, Lingjun; Wang, Lin; Jiang, Minghu; Huang, Juxiang; Lin, Hong

2011-12-01

We identified significantly higher expression of the genes glycogen debranching enzyme 6 (AGL), enolase 1 (ENOSF1), ectonucleotide pyrophosphatase 2 (ENPP2_1), glutathione S-transferase 3 (GSTM3_3) and mannosidase (MAN2B2) from human left cerebrums versus chimpanzees. Yet the distinct low- and high-expression AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 metabolism networks between chimpanzee and human left cerebrum remain to be elucidated. Here, we constructed low- and high-expression activated and inhibited upstream and downstream AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 metabolism network between chimpanzee and human left cerebrum in GEO data set by gene regulatory network inference method based on linear programming and decomposition procedure, under covering AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 pathway and matching metabolism enrichment analysis by CapitalBio MAS 3.0 integration of public databases, including Gene Ontology, KEGG, BioCarta, GenMapp, Intact, UniGene, OMIM, etc. Our results show that the AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 metabolism network has more activated and less inhibited molecules in chimpanzee, but less activated and more inhibited in the human left cerebrum. We inferred stronger carbohydrate, glutathione and proteoglycan metabolism, ATPase activity, but weaker base excision repair, arachidonic acid and drug metabolism as a result of inducing cell growth in low-expression AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 metabolism network of chimpanzee left cerebrum; whereas stronger lipid metabolism, amino acid catabolism, DNA repair but weaker inflammatory response, cell proliferation, glutathione and carbohydrate metabolism as a result of inducing cell differentiation in high-expression AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 metabolism network of human left cerebrum. Our inferences are consistent with recent reports and computational activation and inhibition gene number patterns, respectively.

Disentangling the neural mechanisms involved in Hinduism- and Buddhism-related meditations.

PubMed

Tomasino, Barbara; Chiesa, Alberto; Fabbro, Franco

2014-10-01

The most diffuse forms of meditation derive from Hinduism and Buddhism spiritual traditions. Different cognitive processes are set in place to reach these meditation states. According to an historical-philological hypothesis (Wynne, 2009) the two forms of meditation could be disentangled. While mindfulness is the focus of Buddhist meditation reached by focusing sustained attention on the body, on breathing and on the content of the thoughts, reaching an ineffable state of nothigness accompanied by a loss of sense of self and duality (Samadhi) is the main focus of Hinduism-inspired meditation. It is possible that these different practices activate separate brain networks. We tested this hypothesis by conducting an activation likelihood estimation (ALE) meta-analysis of functional magnetic resonance imaging (fMRI) studies. The network related to Buddhism-inspired meditation (16 experiments, 263 subjects, and 96 activation foci) included activations in some frontal lobe structures associated with executive attention, possibly confirming the fundamental role of mindfulness shared by many Buddhist meditations. By contrast, the network related to Hinduism-inspired meditation (8 experiments, 54 activation foci and 66 subjects) triggered a left lateralized network of areas including the postcentral gyrus, the superior parietal lobe, the hippocampus and the right middle cingulate cortex. The dissociation between anterior and posterior networks support the notion that different meditation styles and traditions are characterized by different patterns of neural activation. Copyright © 2014. Published by Elsevier Inc.

How acute total sleep loss affects the attending brain: a meta-analysis of neuroimaging studies.

PubMed

Ma, Ning; Dinges, David F; Basner, Mathias; Rao, Hengyi

2015-02-01

Attention is a cognitive domain that can be severely affected by sleep deprivation. Previous neuroimaging studies have used different attention paradigms and reported both increased and reduced brain activation after sleep deprivation. However, due to large variability in sleep deprivation protocols, task paradigms, experimental designs, characteristics of subject populations, and imaging techniques, there is no consensus regarding the effects of sleep loss on the attending brain. The aim of this meta-analysis was to identify brain activations that are commonly altered by acute total sleep deprivation across different attention tasks. Coordinate-based meta-analysis of neuroimaging studies of performance on attention tasks during experimental sleep deprivation. The current version of the activation likelihood estimation (ALE) approach was used for meta-analysis. The authors searched published articles and identified 11 sleep deprivation neuroimaging studies using different attention tasks with a total of 185 participants, equaling 81 foci for ALE analysis. The meta-analysis revealed significantly reduced brain activation in multiple regions following sleep deprivation compared to rested wakefulness, including bilateral intraparietal sulcus, bilateral insula, right prefrontal cortex, medial frontal cortex, and right parahippocampal gyrus. Increased activation was found only in bilateral thalamus after sleep deprivation compared to rested wakefulness. Acute total sleep deprivation decreases brain activation in the fronto-parietal attention network (prefrontal cortex and intraparietal sulcus) and in the salience network (insula and medial frontal cortex). Increased thalamic activation after sleep deprivation may reflect a complex interaction between the de-arousing effects of sleep loss and the arousing effects of task performance on thalamic activity. © 2015 Associated Professional Sleep Societies, LLC.

Is there a core neural network in empathy? An fMRI based quantitative meta-analysis.

PubMed

Fan, Yan; Duncan, Niall W; de Greck, Moritz; Northoff, Georg

2011-01-01

Whilst recent neuroimaging studies have identified a series of different brain regions as being involved in empathy, it remains unclear concerning the activation consistence of these brain regions and their specific functional roles. Using MKDA, a whole-brain based quantitative meta-analysis of recent fMRI studies of empathy was performed. This analysis identified the dACC-aMCC-SMA and bilateral anterior insula as being consistently activated in empathy. Hypothesizing that what are here termed affective-perceptual and cognitive-evaluative forms of empathy might be characterized by different activity patterns, the neural activations in these forms of empathy were compared. The dorsal aMCC was demonstrated to be recruited more frequently in the cognitive-evaluative form of empathy, whilst the right anterior insula was found to be involved in the affective-perceptual form of empathy only. The left anterior insula was active in both forms of empathy. It was concluded that the dACC-aMCC-SMA and bilateral insula can be considered as forming a core network in empathy, and that cognitive-evaluative and affective-perceptual empathy can be distinguished at the level of regional activation. Copyright © 2010 Elsevier Ltd. All rights reserved.

Function and activity classification in network traffic data: existing methods, their weaknesses, and a path forward

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy

2016-05-01

The cyber spaces are increasingly becoming the battlefields between friendly and adversary forces, with normal users caught in the middle. Accordingly, planners of enterprise defensive policies and offensive cyber missions alike have an essential goal to minimize the impact of their own actions and adversaries' attacks on normal operations of the commercial and government networks. To do this, the cyber analysis need accurate "cyber battle maps", where the functions, roles, and activities of individual and groups of devices and users are accurately identified. Most of the research in cyber exploitation has focused on the identification of attacks, attackers, and their devices. Many tools exist for device profiling, malware identification, user attribution, and attack analysis. However, most of the tools are intrusive, sensitive to data obfuscation, or provide anomaly flagging and not able to correctly classify the semantics and causes of network activities. In this paper, we review existing solutions that can identify functional and social roles of entities in cyberspace, discuss their weaknesses, and propose an approach for developing functional and social layers of cyber battle maps.

Altered Network Oscillations and Functional Connectivity Dynamics in Children Born Very Preterm.

PubMed

Moiseev, Alexander; Doesburg, Sam M; Herdman, Anthony T; Ribary, Urs; Grunau, Ruth E

2015-09-01

Structural brain connections develop atypically in very preterm children, and altered functional connectivity is also evident in fMRI studies. Such alterations in brain network connectivity are associated with cognitive difficulties in this population. Little is known, however, about electrophysiological interactions among specific brain networks in children born very preterm. In the present study, we recorded magnetoencephalography while very preterm children and full-term controls performed a visual short-term memory task. Regions expressing task-dependent activity changes were identified using beamformer analysis, and inter-regional phase synchrony was calculated. Very preterm children expressed altered regional recruitment in distributed networks of brain areas, across standard physiological frequency ranges including the theta, alpha, beta and gamma bands. Reduced oscillatory synchrony was observed among task-activated brain regions in very preterm children, particularly for connections involving areas critical for executive abilities, including middle frontal gyrus. These findings suggest that inability to recruit neurophysiological activity and interactions in distributed networks including frontal regions may contribute to difficulties in cognitive development in children born very preterm.

The interaction of social networks and child obesity prevention program effects: the pathways trial.

PubMed

Shin, Hee-Sung; Valente, Thomas W; Riggs, Nathaniel R; Huh, Jimi; Spruijt-Metz, Donna; Chou, Chih-Ping; Ann Pentz, Mary

2014-06-01

Social network analysis was used to examine whether peer influence from one's social networks moderates obesity prevention program effects on obesity-related behaviors: healthful and unhealthful. Participants included 557 children residing in Southern California. The survey assessed health-promoting behaviors (i.e., physical activity at school, physical activity outside of school, and fruit and vegetable intake), as well as unhealthful behaviors (high-calorie, low-nutrient intake and sedentary activity), and peer exposure calculated from social network nominations as indicators of peer influence. Multilevel models were conducted separately on outcomes predicted by program participation, peer exposure, and program participation by peer exposure. Results indicated that peer exposure was positively associated with one's own healthful and unhealthful behaviors. Program participation effects were moderated by peer influence, but only when unhealthful peer influence was present. Results suggest that peer influence can diminish or amplify prevention programs Future interventions should consider peer-led components to promote healthful influence of peers on healthful and unhealthful behaviors, and programs should be mindful that their effects are moderated by social networks. Copyright © 2014 The Obesity Society.

Identification of a Functional Connectome for Long-Term Fear Memory in Mice

PubMed Central

Wheeler, Anne L.; Teixeira, Cátia M.; Wang, Afra H.; Xiong, Xuejian; Kovacevic, Natasa; Lerch, Jason P.; McIntosh, Anthony R.; Parkinson, John; Frankland, Paul W.

2013-01-01

Long-term memories are thought to depend upon the coordinated activation of a broad network of cortical and subcortical brain regions. However, the distributed nature of this representation has made it challenging to define the neural elements of the memory trace, and lesion and electrophysiological approaches provide only a narrow window into what is appreciated a much more global network. Here we used a global mapping approach to identify networks of brain regions activated following recall of long-term fear memories in mice. Analysis of Fos expression across 84 brain regions allowed us to identify regions that were co-active following memory recall. These analyses revealed that the functional organization of long-term fear memories depends on memory age and is altered in mutant mice that exhibit premature forgetting. Most importantly, these analyses indicate that long-term memory recall engages a network that has a distinct thalamic-hippocampal-cortical signature. This network is concurrently integrated and segregated and therefore has small-world properties, and contains hub-like regions in the prefrontal cortex and thalamus that may play privileged roles in memory expression. PMID:23300432

Generalized activity equations for spiking neural network dynamics.

PubMed

Buice, Michael A; Chow, Carson C

2013-01-01

Much progress has been made in uncovering the computational capabilities of spiking neural networks. However, spiking neurons will always be more expensive to simulate compared to rate neurons because of the inherent disparity in time scales-the spike duration time is much shorter than the inter-spike time, which is much shorter than any learning time scale. In numerical analysis, this is a classic stiff problem. Spiking neurons are also much more difficult to study analytically. One possible approach to making spiking networks more tractable is to augment mean field activity models with some information about spiking correlations. For example, such a generalized activity model could carry information about spiking rates and correlations between spikes self-consistently. Here, we will show how this can be accomplished by constructing a complete formal probabilistic description of the network and then expanding around a small parameter such as the inverse of the number of neurons in the network. The mean field theory of the system gives a rate-like description. The first order terms in the perturbation expansion keep track of covariances.

Reduced salience and default mode network activity in women with anorexia nervosa

PubMed Central

McFadden, Kristina L.; Tregellas, Jason R.; Shott, Megan E.; Frank, Guido K.W.

2014-01-01

Background The neurobiology of anorexia nervosa is poorly understood. Neuronal networks contributing to action selection, self-regulation and interoception could contribute to pathologic eating and body perception in people with anorexia nervosa. We tested the hypothesis that the salience network (SN) and default mode network (DMN) would show decreased intrinsic activity in women with anorexia nervosa and those who had recovered from the disease compared to controls. The basal ganglia (BGN) and sensorimotor networks (SMN) were also investigated. Methods Between January 2008 and January 2012, women with restricting-type anorexia nervosa, women who recovered from the disease and healthy control women completed functional magnetic resonance imaging during a conditioned stimulus task. Network activity was studied using independent component analysis. Results We studied 20 women with anorexia nervosa, 24 recovered women and 24 controls. Salience network activity in the anterior cingulate cortex was reduced in women with anorexia nervosa (p = 0.030; all results false-discovery rate–corrected) and recovered women (p = 0.039) compared to controls. Default mode network activity in the precuneus was reduced in women with anorexia compared to controls (p = 0.023). Sensorimotor network activity in the supplementary motor area (SMA; p = 0.008), and the left (p = 0.028) and right (p = 0.002) postcentral gyrus was reduced in women with anorexia compared to controls; SMN activity in the SMA (p = 0.019) and the right postcentral gyrus (p = 0.008) was reduced in women with anorexia compared to recovered women. There were no group differences in the BGN. Limitations Differences between patient and control populations (e.g., depression, anxiety, medication) are potential confounds, but were included as covariates. Conclusion Reduced SN activity in women with anorexia nervosa and recovered women could be a trait-related biomarker or illness remnant, altering the drive to approach food. The alterations in the DMN and SMN observed only in women with anorexia nervosa suggest state-dependent abnormalities that could be related to altered interoception and body image in these women when they are underweight but that remit following recovery. PMID:24280181

Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses

PubMed Central

Nickerson, Lisa D.; Smith, Stephen M.; Öngür, Döst; Beckmann, Christian F.

2017-01-01

Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state networks (RSNs) that simultaneously engage in oscillatory activity. Furthermore, typical seed-based analysis characterizes RSNs in terms of spatially distributed patterns of correlation (typically by means of simple Pearson's coefficients) and thereby confounds together amplitude information of oscillatory activity and noise. ICA and other regression techniques, on the other hand, retain magnitude information and therefore can be sensitive to both changes in the spatially distributed nature of correlations (differences in the spatial pattern or “shape”) as well as the amplitude of the network activity. Furthermore, motion can mimic amplitude effects so it is crucial to use a technique that retains such information to ensure that connectivity differences are accurately localized. In this work, we investigate the dual regression approach that is frequently applied with group ICA to assess group differences in resting state functional connectivity of brain networks. We show how ignoring amplitude effects and how excessive motion corrupts connectivity maps and results in spurious connectivity differences. We also show how to implement the dual regression to retain amplitude information and how to use dual regression outputs to identify potential motion effects. Two key findings are that using a technique that retains magnitude information, e.g., dual regression, and using strict motion criteria are crucial for controlling both network amplitude and motion-related amplitude effects, respectively, in resting state connectivity analyses. We illustrate these concepts using realistic simulated resting state FMRI data and in vivo data acquired in healthy subjects and patients with bipolar disorder and schizophrenia. PMID:28348512

Topological dimension tunes activity patterns in hierarchical modular networks

NASA Astrophysics Data System (ADS)

Safari, Ali; Moretti, Paolo; Muñoz, Miguel A.

2017-11-01

Connectivity patterns of relevance in neuroscience and systems biology can be encoded in hierarchical modular networks (HMNs). Recent studies highlight the role of hierarchical modular organization in shaping brain activity patterns, providing an excellent substrate to promote both segregation and integration of neural information. Here, we propose an extensive analysis of the critical spreading rate (or ‘epidemic’ threshold)—separating a phase with endemic persistent activity from one in which activity ceases—on diverse HMNs. By employing analytical and computational techniques we determine the nature of such a threshold and scrutinize how it depends on general structural features of the underlying HMN. We critically discuss the extent to which current graph-spectral methods can be applied to predict the onset of spreading in HMNs and, most importantly, we elucidate the role played by the network topological dimension as a relevant and unifying structural parameter, controlling the epidemic threshold.

Optimization of deformation monitoring networks using finite element strain analysis

NASA Astrophysics Data System (ADS)

Alizadeh-Khameneh, M. Amin; Eshagh, Mehdi; Jensen, Anna B. O.

2018-04-01

An optimal design of a geodetic network can fulfill the requested precision and reliability of the network, and decrease the expenses of its execution by removing unnecessary observations. The role of an optimal design is highlighted in deformation monitoring network due to the repeatability of these networks. The core design problem is how to define precision and reliability criteria. This paper proposes a solution, where the precision criterion is defined based on the precision of deformation parameters, i. e. precision of strain and differential rotations. A strain analysis can be performed to obtain some information about the possible deformation of a deformable object. In this study, we split an area into a number of three-dimensional finite elements with the help of the Delaunay triangulation and performed the strain analysis on each element. According to the obtained precision of deformation parameters in each element, the precision criterion of displacement detection at each network point is then determined. The developed criterion is implemented to optimize the observations from the Global Positioning System (GPS) in Skåne monitoring network in Sweden. The network was established in 1989 and straddled the Tornquist zone, which is one of the most active faults in southern Sweden. The numerical results show that 17 out of all 21 possible GPS baseline observations are sufficient to detect minimum 3 mm displacement at each network point.

Organisational adaptation in an activist network: social networks, leadership, and change in al-Muhajiroun.

PubMed

Kenney, Michael; Horgan, John; Horne, Cale; Vining, Peter; Carley, Kathleen M; Bigrigg, Michael W; Bloom, Mia; Braddock, Kurt

2013-09-01

Social networks are said to facilitate learning and adaptation by providing the connections through which network nodes (or agents) share information and experience. Yet, our understanding of how this process unfolds in real-world networks remains underdeveloped. This paper explores this gap through a case study of al-Muhajiroun, an activist network that continues to call for the establishment of an Islamic state in Britain despite being formally outlawed by British authorities. Drawing on organisation theory and social network analysis, we formulate three hypotheses regarding the learning capacity and social network properties of al-Muhajiroun (AM) and its successor groups. We then test these hypotheses using mixed methods. Our methods combine quantitative analysis of three agent-based networks in AM measured for structural properties that facilitate learning, including connectedness, betweenness centrality and eigenvector centrality, with qualitative analysis of interviews with AM activists focusing organisational adaptation and learning. The results of these analyses confirm that al-Muhajiroun activists respond to government pressure by changing their operations, including creating new platforms under different names and adjusting leadership roles among movement veterans to accommodate their spiritual leader's unwelcome exodus to Lebanon. Simple as they are effective, these adaptations have allowed al-Muhajiroun and its successor groups to continue their activism in an increasingly hostile environment. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Representing distributed cognition in complex systems: how a submarine returns to periscope depth.

PubMed

Stanton, Neville A

2014-01-01

This paper presents the Event Analysis of Systemic Teamwork (EAST) method as a means of modelling distributed cognition in systems. The method comprises three network models (i.e. task, social and information) and their combination. This method was applied to the interactions between the sound room and control room in a submarine, following the activities of returning the submarine to periscope depth. This paper demonstrates three main developments in EAST. First, building the network models directly, without reference to the intervening methods. Second, the application of analysis metrics to all three networks. Third, the combination of the aforementioned networks in different ways to gain a broader understanding of the distributed cognition. Analyses have shown that EAST can be used to gain both qualitative and quantitative insights into distributed cognition. Future research should focus on the analyses of network resilience and modelling alternative versions of a system.

Sparse dictionary learning for resting-state fMRI analysis

NASA Astrophysics Data System (ADS)

Lee, Kangjoo; Han, Paul Kyu; Ye, Jong Chul

2011-09-01

Recently, there has been increased interest in the usage of neuroimaging techniques to investigate what happens in the brain at rest. Functional imaging studies have revealed that the default-mode network activity is disrupted in Alzheimer's disease (AD). However, there is no consensus, as yet, on the choice of analysis method for the application of resting-state analysis for disease classification. This paper proposes a novel compressed sensing based resting-state fMRI analysis tool called Sparse-SPM. As the brain's functional systems has shown to have features of complex networks according to graph theoretical analysis, we apply a graph model to represent a sparse combination of information flows in complex network perspectives. In particular, a new concept of spatially adaptive design matrix has been proposed by implementing sparse dictionary learning based on sparsity. The proposed approach shows better performance compared to other conventional methods, such as independent component analysis (ICA) and seed-based approach, in classifying the AD patients from normal using resting-state analysis.

Interactive Network Analytical Tool for Instantaneous Bespoke Interrogation of Food Safety Notifications

PubMed Central

Nepusz, Tamás; Petróczi, Andrea; Naughton, Declan P.

2012-01-01

Background The globalization of food supply necessitates continued advances in regulatory control measures to ensure that citizens enjoy safe and adequate nutrition. The aim of this study was to extend previous reports on network analysis relating to food notifications by including an optional filter by type of notification and in cases of contamination, by type of contaminant in the notified foodstuff. Methodology/Principal Findings A filter function has been applied to enable processing of selected notifications by contaminant or type of notification to i) capture complexity, ii) analyze trends, and iii) identify patterns of reporting activities between countries. The program rapidly assesses nations' roles as transgressor and/or detector for each category of contaminant and for the key class of border rejection. In the open access demonstration version, the majority of notifications in the Rapid Alert System for Food and Feed were categorized by contaminant type as mycotoxin (50.4%), heavy metals (10.9%) or bacteria (20.3%). Examples are given demonstrating how network analytical approaches complement, and in some cases supersede, descriptive statistics such as frequency counts, which may give limited or potentially misleading information. One key feature is that network analysis takes the relationship between transgressor and detector countries, along with number of reports and impact simultaneously into consideration. Furhermore, the indices that compliment the network maps and reflect each country's transgressor and detector activities allow comparisons to be made between (transgressing vs. detecting) as well as within (e.g. transgressing) activities. Conclusions/significance This further development of the network analysis approach to food safety contributes to a better understanding of the complexity of the effort ensuring food is safe for consumption in the European Union. The unique patterns of the interplay between detectors and transgressors, instantly revealed by our approach, could supplement the intelligence gathered by regulatory authorities and inform risk based sampling protocols. PMID:22530063

Interactive network analytical tool for instantaneous bespoke interrogation of food safety notifications.

PubMed

Nepusz, Tamás; Petróczi, Andrea; Naughton, Declan P

2012-01-01

The globalization of food supply necessitates continued advances in regulatory control measures to ensure that citizens enjoy safe and adequate nutrition. The aim of this study was to extend previous reports on network analysis relating to food notifications by including an optional filter by type of notification and in cases of contamination, by type of contaminant in the notified foodstuff. A filter function has been applied to enable processing of selected notifications by contaminant or type of notification to i) capture complexity, ii) analyze trends, and iii) identify patterns of reporting activities between countries. The program rapidly assesses nations' roles as transgressor and/or detector for each category of contaminant and for the key class of border rejection. In the open access demonstration version, the majority of notifications in the Rapid Alert System for Food and Feed were categorized by contaminant type as mycotoxin (50.4%), heavy metals (10.9%) or bacteria (20.3%). Examples are given demonstrating how network analytical approaches complement, and in some cases supersede, descriptive statistics such as frequency counts, which may give limited or potentially misleading information. One key feature is that network analysis takes the relationship between transgressor and detector countries, along with number of reports and impact simultaneously into consideration. Furhermore, the indices that compliment the network maps and reflect each country's transgressor and detector activities allow comparisons to be made between (transgressing vs. detecting) as well as within (e.g. transgressing) activities. This further development of the network analysis approach to food safety contributes to a better understanding of the complexity of the effort ensuring food is safe for consumption in the European Union. The unique patterns of the interplay between detectors and transgressors, instantly revealed by our approach, could supplement the intelligence gathered by regulatory authorities and inform risk based sampling protocols.

Association between suicidal symptoms and repeat suicidal behaviour within a sample of hospital-treated suicide attempters

PubMed Central

van Borkulo, Claudia D.; O’Connor, Rory C.

2017-01-01

Background Suicidal behaviour is the end result of the complex relation between many factors which are biological, psychological and environmental in nature. Network analysis is a novel method that may help us better understand the complex association between different factors. Aims To examine the relationship between suicidal symptoms as assessed by the Beck Scale for Suicide Ideation and future suicidal behaviour in patients admitted to hospital following a suicide attempt, using network analysis. Method Secondary analysis was conducted on previously collected data from a sample of 366 patients who were admitted to a Scottish hospital following a suicide attempt. Network models were estimated to visualise and test the association between baseline symptom network structure and suicidal behaviour at 15-month follow-up. Results Network analysis showed that the desire for an active attempt was found to be the most central, strongly related suicide symptom. Of the 19 suicide symptoms that were assessed at baseline, 10 symptoms were directly related to repeat suicidal behaviour. When comparing baseline network structure of repeaters (n=94) with the network of non-repeaters (n=272), no significant differences were found. Conclusions Network analysis can help us better understand suicidal behaviour by visualising the complex relation between relevant symptoms and by indicating which symptoms are most central within the network. These insights have theoretical implications as well as informing the assessment and treatment of suicidal behaviour. Declaration of interest None. Copyright and usage © The Royal College of Psychiatrists 2017. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license. PMID:28507771

Insula Demonstrates a Non-Linear Response to Varying Demand for Cognitive Control and Weaker Resting Connectivity With the Executive Control Network in Smokers.

PubMed

Fedota, John R; Matous, Allison L; Salmeron, Betty Jo; Gu, Hong; Ross, Thomas J; Stein, Elliot A

2016-09-01

Deficits in cognitive control processes are a primary characteristic of nicotine addiction. However, while network-based connectivity measures of dysfunction have frequently been observed, empirical evidence of task-based dysfunction in these processes has been inconsistent. Here, in a sample of smokers (n=35) and non-smokers (n=21), a previously validated parametric flanker task is employed to characterize addiction-related alterations in responses to varying (ie, high, intermediate, and low) demands for cognitive control. This approach yields a demand-response curve that aims to characterize potential non-linear responses to increased demand for control, including insensitivities or lags in fully activating the cognitive control network. We further used task-based differences in activation between groups as seeds for resting-state analysis of network dysfunction in an effort to more closely link prior inconsistencies in task-related activation with evidence of impaired network connectivity in smokers. For both smokers and non-smokers, neuroimaging results showed similar increases in activation in brain areas associated with cognitive control. However, reduced activation in right insula was seen only in smokers and only when processing intermediate demand for cognitive control. Further, in smokers, this task-modulated right insula showed weaker functional connectivity with the superior frontal gyrus, a component of the task-positive executive control network. These results demonstrate that the neural instantiation of salience attribution in smokers is both more effortful to fully activate and has more difficulty communicating with the exogenous, task-positive, executive control network. Together, these findings further articulate the cognitive control dysfunction associated with smoking and illustrate a specific brain circuit potentially responsible.

Resting-state brain networks revealed by granger causal connectivity in frogs.

PubMed

Xue, Fei; Fang, Guangzhan; Yue, Xizi; Zhao, Ermi; Brauth, Steven E; Tang, Yezhong

2016-10-15

Resting-state networks (RSNs) refer to the spontaneous brain activity generated under resting conditions, which maintain the dynamic connectivity of functional brain networks for automatic perception or higher order cognitive functions. Here, Granger causal connectivity analysis (GCCA) was used to explore brain RSNs in the music frog (Babina daunchina) during different behavioral activity phases. The results reveal that a causal network in the frog brain can be identified during the resting state which reflects both brain lateralization and sexual dimorphism. Specifically (1) ascending causal connections from the left mesencephalon to both sides of the telencephalon are significantly higher than those from the right mesencephalon, while the right telencephalon gives rise to the strongest efferent projections among all brain regions; (2) causal connections from the left mesencephalon in females are significantly higher than those in males and (3) these connections are similar during both the high and low behavioral activity phases in this species although almost all electroencephalograph (EEG) spectral bands showed higher power in the high activity phase for all nodes. The functional features of this network match important characteristics of auditory perception in this species. Thus we propose that this causal network maintains auditory perception during the resting state for unexpected auditory inputs as resting-state networks do in other species. These results are also consistent with the idea that females are more sensitive to auditory stimuli than males during the reproductive season. In addition, these results imply that even when not behaviorally active, the frogs remain vigilant for detecting external stimuli. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Characterizing structure connectivity correlation with the default mode network in Alzheimer's patients and normal controls

NASA Astrophysics Data System (ADS)

Guo, Jia; Xu, Peng; Song, Chao; Yao, Li; Zhao, Xiaojie

2012-03-01

Magnetic resonance diffusion tensor imaging (DTI) is a kind of effective measure to do non-invasive investigation on brain fiber structure at present. Studies of fiber tracking based on DTI showed that there was structural connection of white matter fiber among the nodes of resting-state functional network, denoting that the connection of white matter was the basis of gray matter regions in functional network. Nevertheless, relationship between these structure connectivity regions and functional network has not been clearly indicated. Moreover, research of fMRI found that activation of default mode network (DMN) in Alzheimer's disease (AD) was significantly descended, especially in hippocampus and posterior cingulated cortex (PCC). The relationship between this change of DMN activity and structural connection among functional networks needs further research. In this study, fast marching tractography (FMT) algorithm was adopted to quantitative calculate fiber connectivity value between regions, and hippocampus and PCC which were two important regions in DMN related with AD were selected to compute white matter connection region between them in elderly normal control (NC) and AD patient. The fiber connectivity value was extracted to do the correlation analysis with activity intensity of DMN. Results showed that, between PCC and hippocampus of NC, there exited region with significant high connectivity value of white matter fiber whose performance has relatively strong correlation with the activity of DMN, while there was no significant white matter connection region between them for AD patient which might be related with reduced network activation in these two regions of AD.

Channeling the Central Dogma

PubMed Central

Calabrese, Ronald L.

2014-01-01

How do neurons and networks achieve their characteristic electrical activity, regulate this activity homeostatically, and yet show population variability in expression? O'Leary et al. address some of these thorny questions in this theoretical analysis that starts with the Central Dogma. PMID:24853932

An integrated approach to uncover quality marker underlying the effects of Alisma orientale on lipid metabolism, using chemical analysis and network pharmacology.

PubMed

Liao, Maoliang; Shang, Haihua; Li, Yazhuo; Li, Tian; Wang, Miao; Zheng, Yanan; Hou, Wenbin; Liu, Changxiao

2018-06-01

Quality control of traditional Chinese medicines is currently a great concern, due to the correlation between the quality control indicators and clinic effect is often questionable. According to the "multi-components and multi-targets" property of TCMs, a new special quality and bioactivity evaluation system is urgently needed. Present study adopted an integrated approach to provide new insights relating to uncover quality marker underlying the effects of Alisma orientale (AO) on lipid metabolism. In this paper, guided by the concept of the quality marker (Q-marker), an integrated strategies "effect-compound-target-fingerprint" was established to discovery and screen the potential quality marker of AO based on network pharmacology and chemical analysis. Firstly, a bioactivity evaluation was performed to screen the main active fractions. Then the chemical compositions were rapidly identified by chemical analysis. Next, networks were constructed to illuminate the interactions between these component and their targets for lipid metabolism, and the potential Q-marker of AO was initially screened. Finally, the activity of the Q-markers was validated in vitro. 50% ethanol extract fraction was found to have the strongest lipid-lowering activity. Then, the network pharmacology was used to clarify the unique relationship between the Q-markers and their integral pharmacological action. Combined with the results obtained, five active ingredients in the 50% ethanol extract fraction were given special considerations to be representative Q-markers: Alisol A, Alisol B, Alisol A 23-acetate, Alisol B 23-acetate and Alisol A 24-acetate, respectively. The chromatographic fingerprints based Q-marker was establishment. The integrated Q-marker screen may offer an alternative quality assessment of herbal medicines. Copyright © 2018. Published by Elsevier GmbH.

Functional Activation during the Rapid Visual Information Processing Task in a Middle Aged Cohort: An fMRI Study.

PubMed

Neale, Chris; Johnston, Patrick; Hughes, Matthew; Scholey, Andrew

2015-01-01

The Rapid Visual Information Processing (RVIP) task, a serial discrimination task where task performance believed to reflect sustained attention capabilities, is widely used in behavioural research and increasingly in neuroimaging studies. To date, functional neuroimaging research into the RVIP has been undertaken using block analyses, reflecting the sustained processing involved in the task, but not necessarily the transient processes associated with individual trial performance. Furthermore, this research has been limited to young cohorts. This study assessed the behavioural and functional magnetic resonance imaging (fMRI) outcomes of the RVIP task using both block and event-related analyses in a healthy middle aged cohort (mean age = 53.56 years, n = 16). The results show that the version of the RVIP used here is sensitive to changes in attentional demand processes with participants achieving a 43% accuracy hit rate in the experimental task compared with 96% accuracy in the control task. As shown by previous research, the block analysis revealed an increase in activation in a network of frontal, parietal, occipital and cerebellar regions. The event related analysis showed a similar network of activation, seemingly omitting regions involved in the processing of the task (as shown in the block analysis), such as occipital areas and the thalamus, providing an indication of a network of regions involved in correct trial performance. Frontal (superior and inferior frontal gryi), parietal (precuenus, inferior parietal lobe) and cerebellar regions were shown to be active in both the block and event-related analyses, suggesting their importance in sustained attention/vigilance. These networks and the differences between them are discussed in detail, as well as implications for future research in middle aged cohorts.

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants.

PubMed

Cruz-Garza, Jesus G; Hernandez, Zachery R; Tse, Teresa; Caducoy, Eunice; Abibullaev, Berdakh; Contreras-Vidal, Jose L

2015-10-04

Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental paradigms and analysis tools have been limited to constrained laboratory tasks and contexts due to technical limitations imposed by the available set of measuring and analysis techniques and the age of the subjects. These limitations severely limit the study of developmental neural dynamics and associated neural networks engaged in cognition, perception and action in infants performing "in action and in context". This protocol presents a novel approach to study infants and young children as they freely organize their own behavior, and its consequences in a complex, partly unpredictable and highly dynamic environment. The proposed methodology integrates synchronized high-density active scalp electroencephalography (EEG), inertial measurement units (IMUs), video recording and behavioral analysis to capture brain activity and movement non-invasively in freely-behaving infants. This setup allows for the study of neural network dynamics in the developing brain, in action and context, as these networks are recruited during goal-oriented, exploration and social interaction tasks.

Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity

NASA Astrophysics Data System (ADS)

Zhang, Bijun; Vogt, Martin; Maggiora, Gerald M.; Bajorath, Jürgen

2015-07-01

Chemical space networks (CSNs) have recently been introduced as a conceptual alternative to coordinate-based representations of chemical space. CSNs were initially designed as threshold networks using the Tanimoto coefficient as a continuous similarity measure. The analysis of CSNs generated from sets of bioactive compounds revealed that many statistical properties were strongly dependent on their edge density. While it was difficult to compare CSNs at pre-defined similarity threshold values, CSNs with constant edge density were directly comparable. In the current study, alternative CSN representations were constructed by applying the matched molecular pair (MMP) formalism as a substructure-based similarity criterion. For more than 150 compound activity classes, MMP-based CSNs (MMP-CSNs) were compared to corresponding threshold CSNs (THR-CSNs) at a constant edge density by applying different parameters from network science, measures of community structure distributions, and indicators of structure-activity relationship (SAR) information content. MMP-CSNs were found to be an attractive alternative to THR-CSNs, yielding low edge densities and well-resolved topologies. MMP-CSNs and corresponding THR-CSNs often had similar topology and closely corresponding community structures, although there was only limited overlap in similarity relationships. The homophily principle from network science was shown to affect MMP-CSNs and THR-CSNs in different ways, despite the presence of conserved topological features. Moreover, activity cliff distributions in alternative CSN designs markedly differed, which has important implications for SAR analysis.

Structural stability of interaction networks against negative external fields

NASA Astrophysics Data System (ADS)

Yoon, S.; Goltsev, A. V.; Mendes, J. F. F.

2018-04-01

We explore structural stability of weighted and unweighted networks of positively interacting agents against a negative external field. We study how the agents support the activity of each other to confront the negative field, which suppresses the activity of agents and can lead to collapse of the whole network. The competition between the interactions and the field shape the structure of stable states of the system. In unweighted networks (uniform interactions) the stable states have the structure of k -cores of the interaction network. The interplay between the topology and the distribution of weights (heterogeneous interactions) impacts strongly the structural stability against a negative field, especially in the case of fat-tailed distributions of weights. We show that apart from critical slowing down there is also a critical change in the system structure that precedes the network collapse. The change can serve as an early warning of the critical transition. To characterize changes of network structure we develop a method based on statistical analysis of the k -core organization and so-called "corona" clusters belonging to the k -cores.

Injection Drug Users’ Involvement In Drug Economy: Dynamics of Sociometric and Egocentric Social Networks

PubMed Central

Yang, Cui; Latkin, Carl; Muth, Stephen Q.; Rudolph, Abby

2014-01-01

The purpose of this analysis was to examine the effect of social network cohesiveness on drug economy involvement, and to test whether this relationship is mediated by drug support network size in a sample of active injection drug users. Involvement in the drug economy was defined by self-report of participation in at least one of the following activities: selling drugs, holding drugs or money for drugs, providing street security for drug sellers, cutting/packaging/cooking drugs, selling or renting drug paraphernalia (e.g., pipes, tools, rigs), and injecting drugs in others’ veins. The sample consists of 273 active injection drug users in Baltimore, Maryland who reported having injected drugs in the last 6 months and were recruited through either street outreach or by their network members. Egocentric drug support networks were assessed through a social network inventory at baseline. Sociometric networks were built upon the linkages by selected matching characteristics, and k-plex rank was used to characterize the level of cohesiveness of the individual to others in the social network. Although no direct effect was observed, structural equation modeling indicated k-plex rank was indirectly associated with drug economy involvement through drug support network size. These findings suggest the effects of large-scale sociometric networks on injectors’ drug economy involvement may occur through their immediate egocentric networks. Future harm reduction programs for injection drug users (IDUs) should consider providing programs coupled with economic opportunities to those drug users within a cohesive network subgroup. Moreover, individuals with a high connectivity to others in their network may be optimal individuals to train for diffusing HIV prevention messages. PMID:25309015

Evolving dynamics of trading behavior based on coordination game in complex networks

NASA Astrophysics Data System (ADS)

Bian, Yue-tang; Xu, Lu; Li, Jin-sheng

2016-05-01

This work concerns the modeling of evolvement of trading behavior in stock markets. Based on the assumption of the investors' limited rationality, the evolution mechanism of trading behavior is modeled according to the investment strategy of coordination game in network, that investors are prone to imitate their neighbors' activity through comprehensive analysis on the risk dominance degree of certain investment behavior, the network topology of their relationship and its heterogeneity. We investigate by mean-field analysis and extensive simulations the evolution of investors' trading behavior in various typical networks under different risk dominance degree of investment behavior. Our results indicate that the evolution of investors' behavior is affected by the network structure of stock market and the effect of risk dominance degree of investment behavior; the stability of equilibrium states of investors' behavior dynamics is directly related with the risk dominance degree of some behavior; connectivity and heterogeneity of the network plays an important role in the evolution of the investment behavior in stock market.

Meta-review of protein network regulating obesity between validated obesity candidate genes in the white adipose tissue of high-fat diet-induced obese C57BL/6J mice.

PubMed

Kim, Eunjung; Kim, Eun Jung; Seo, Seung-Won; Hur, Cheol-Goo; McGregor, Robin A; Choi, Myung-Sook

2014-01-01

Worldwide obesity and related comorbidities are increasing, but identifying new therapeutic targets remains a challenge. A plethora of microarray studies in diet-induced obesity models has provided large datasets of obesity associated genes. In this review, we describe an approach to examine the underlying molecular network regulating obesity, and we discuss interactions between obesity candidate genes. We conducted network analysis on functional protein-protein interactions associated with 25 obesity candidate genes identified in a literature-driven approach based on published microarray studies of diet-induced obesity. The obesity candidate genes were closely associated with lipid metabolism and inflammation. Peroxisome proliferator activated receptor gamma (Pparg) appeared to be a core obesity gene, and obesity candidate genes were highly interconnected, suggesting a coordinately regulated molecular network in adipose tissue. In conclusion, the current network analysis approach may help elucidate the underlying molecular network regulating obesity and identify anti-obesity targets for therapeutic intervention.

Specifying the core network supporting episodic simulation and episodic memory by activation likelihood estimation.

PubMed

Benoit, Roland G; Schacter, Daniel L

2015-08-01

It has been suggested that the simulation of hypothetical episodes and the recollection of past episodes are supported by fundamentally the same set of brain regions. The present article specifies this core network via Activation Likelihood Estimation (ALE). Specifically, a first meta-analysis revealed joint engagement of expected core-network regions during episodic memory and episodic simulation. These include parts of the medial surface, the hippocampus and parahippocampal cortex within the medial temporal lobes, and the temporal and inferior posterior parietal cortices on the lateral surface. Both capacities also jointly recruited additional regions such as parts of the bilateral dorsolateral prefrontal cortex. All of these core regions overlapped with the default network. Moreover, it has further been suggested that episodic simulation may require a stronger engagement of some of the core network's nodes as well as the recruitment of additional brain regions supporting control functions. A second ALE meta-analysis indeed identified such regions that were consistently more strongly engaged during episodic simulation than episodic memory. These comprised the core-network clusters located in the left dorsolateral prefrontal cortex and posterior inferior parietal lobe and other structures distributed broadly across the default and fronto-parietal control networks. Together, the analyses determine the set of brain regions that allow us to experience past and hypothetical episodes, thus providing an important foundation for studying the regions' specialized contributions and interactions. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Computational Drug-Target Network for Yuanhu Zhitong Prescription

PubMed Central

Lu, Peng; Zhang, Fangbo; Yuan, Yuan; Wang, Songsong

2013-01-01

Yuanhu Zhitong prescription (YZP) is a typical and relatively simple traditional Chinese medicine (TCM), widely used in the clinical treatment of headache, gastralgia, and dysmenorrhea. However, the underlying molecular mechanism of action of YZP is not clear. In this study, based on the previous chemical and metabolite analysis, a complex approach including the prediction of the structure of metabolite, high-throughput in silico screening, and network reconstruction and analysis was developed to obtain a computational drug-target network for YZP. This was followed by a functional and pathway analysis by ClueGO to determine some of the pharmacologic activities. Further, two new pharmacologic actions, antidepressant and antianxiety, of YZP were validated by animal experiments using zebrafish and mice models. The forced swimming test and the tail suspension test demonstrated that YZP at the doses of 4 mg/kg and 8 mg/kg had better antidepressive activity when compared with the control group. The anxiolytic activity experiment showed that YZP at the doses of 100 mg/L, 150 mg/L, and 200 mg/L had significant decrease in diving compared to controls. These results not only shed light on the better understanding of the molecular mechanisms of YZP for curing diseases, but also provide some evidence for exploring the classic TCM formulas for new clinical application. PMID:23762151

Analysis of critical operating conditions for LV distribution networks with microgrids

NASA Astrophysics Data System (ADS)

Zehir, M. A.; Batman, A.; Sonmez, M. A.; Font, A.; Tsiamitros, D.; Stimoniaris, D.; Kollatou, T.; Bagriyanik, M.; Ozdemir, A.; Dialynas, E.

2016-11-01

Increase in the penetration of Distributed Generation (DG) in distribution networks, raises the risk of voltage limit violations while contributing to line losses. Especially in low voltage (LV) distribution networks (secondary distribution networks), impacts of active power flows on the bus voltages and on the network losses are more dominant. As network operators must meet regulatory limitations, they have to take into account the most critical operating conditions in their systems. In this study, it is aimed to present the impact of the worst operation cases of LV distribution networks comprising microgrids. Simulation studies are performed on a field data-based virtual test-bed. The simulations are repeated for several cases consisting different microgrid points of connection with different network loading and microgrid supply/demand conditions.

Analysis of structural patterns in the brain with the complex network approach

NASA Astrophysics Data System (ADS)

Maksimenko, Vladimir A.; Makarov, Vladimir V.; Kharchenko, Alexander A.; Pavlov, Alexey N.; Khramova, Marina V.; Koronovskii, Alexey A.; Hramov, Alexander E.

2015-03-01

In this paper we study mechanisms of the phase synchronization in a model network of Van der Pol oscillators and in the neural network of the brain by consideration of macroscopic parameters of these networks. As the macroscopic characteristics of the model network we consider a summary signal produced by oscillators. Similar to the model simulations, we study EEG signals reflecting the macroscopic dynamics of neural network. We show that the appearance of the phase synchronization leads to an increased peak in the wavelet spectrum related to the dynamics of synchronized oscillators. The observed correlation between the phase relations of individual elements and the macroscopic characteristics of the whole network provides a way to detect phase synchronization in the neural networks in the cases of normal and pathological activity.

How Acute Total Sleep Loss Affects the Attending Brain: A Meta-Analysis of Neuroimaging Studies

PubMed Central

Ma, Ning; Dinges, David F.; Basner, Mathias; Rao, Hengyi

2015-01-01

Study Objectives: Attention is a cognitive domain that can be severely affected by sleep deprivation. Previous neuroimaging studies have used different attention paradigms and reported both increased and reduced brain activation after sleep deprivation. However, due to large variability in sleep deprivation protocols, task paradigms, experimental designs, characteristics of subject populations, and imaging techniques, there is no consensus regarding the effects of sleep loss on the attending brain. The aim of this meta-analysis was to identify brain activations that are commonly altered by acute total sleep deprivation across different attention tasks. Design: Coordinate-based meta-analysis of neuroimaging studies of performance on attention tasks during experimental sleep deprivation. Methods: The current version of the activation likelihood estimation (ALE) approach was used for meta-analysis. The authors searched published articles and identified 11 sleep deprivation neuroimaging studies using different attention tasks with a total of 185 participants, equaling 81 foci for ALE analysis. Results: The meta-analysis revealed significantly reduced brain activation in multiple regions following sleep deprivation compared to rested wakefulness, including bilateral intraparietal sulcus, bilateral insula, right prefrontal cortex, medial frontal cortex, and right parahippocampal gyrus. Increased activation was found only in bilateral thalamus after sleep deprivation compared to rested wakefulness. Conclusion: Acute total sleep deprivation decreases brain activation in the fronto-parietal attention network (prefrontal cortex and intraparietal sulcus) and in the salience network (insula and medial frontal cortex). Increased thalamic activation after sleep deprivation may reflect a complex interaction between the de-arousing effects of sleep loss and the arousing effects of task performance on thalamic activity. Citation: Ma N, Dinges DF, Basner M, Rao H. How acute total sleep loss affects the attending brain: a meta-analysis of neuroimaging studies. SLEEP 2015;38(2):233–240. PMID:25409102

Neural network expert system for X-ray analysis of welded joints

NASA Astrophysics Data System (ADS)

Kozlov, V. V.; Lapik, N. V.; Popova, N. V.

2018-03-01

The use of intelligent technologies for the automated analysis of product quality is one of the main trends in modern machine building. At the same time, rapid development in various spheres of human activity is experienced by methods associated with the use of artificial neural networks, as the basis for building automated intelligent diagnostic systems. Technologies of machine vision allow one to effectively detect the presence of certain regularities in the analyzed designation, including defects of welded joints according to radiography data.

Server Level Analysis of Network Operation Utilizing System Call Data

DTIC Science & Technology

2010-09-25

Server DLL Inject 6 Executable Download and Execute 7 Execute Command 8 Execute net user /ADD 9 PassiveX ActiveX Inject Meterpreter Payload...10 PassiveX ActiveX Inject VNC Server Payload 11 PassiveX ActiveX Injection Payload 12 Recv Tag Findsock Meterpreter 13 Recv Tag Findsock

Specifying the core network supporting episodic simulation and episodic memory by activation likelihood estimation

PubMed Central

Benoit, Roland G.; Schacter, Daniel L.

2015-01-01

It has been suggested that the simulation of hypothetical episodes and the recollection of past episodes are supported by fundamentally the same set of brain regions. The present article specifies this core network via Activation Likelihood Estimation (ALE). Specifically, a first meta-analysis revealed joint engagement of core network regions during episodic memory and episodic simulation. These include parts of the medial surface, the hippocampus and parahippocampal cortex within the medial temporal lobes, and the lateral temporal and inferior posterior parietal cortices on the lateral surface. Both capacities also jointly recruited additional regions such as parts of the bilateral dorsolateral prefrontal cortex. All of these core regions overlapped with the default network. Moreover, it has further been suggested that episodic simulation may require a stronger engagement of some of the core network’s nodes as wells as the recruitment of additional brain regions supporting control functions. A second ALE meta-analysis indeed identified such regions that were consistently more strongly engaged during episodic simulation than episodic memory. These comprised the core-network clusters located in the left dorsolateral prefrontal cortex and posterior inferior parietal lobe and other structures distributed broadly across the default and fronto-parietal control networks. Together, the analyses determine the set of brain regions that allow us to experience past and hypothetical episodes, thus providing an important foundation for studying the regions’ specialized contributions and interactions. PMID:26142352

Multimodal integration of fMRI and EEG data for high spatial and temporal resolution analysis of brain networks

PubMed Central

Mantini, D.; Marzetti, L.; Corbetta, M.; Romani, G.L.; Del Gratta, C.

2017-01-01

Two major non-invasive brain mapping techniques, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. We propose an approach based on the integration of EEG and fMRI, enabling the EEG temporal dynamics of information processing to be characterized within spatially well-defined fMRI large-scale networks. First, the fMRI data are decomposed into networks by means of spatial independent component analysis (sICA), and those associated with intrinsic activity and/or responding to task performance are selected using information from the related time-courses. Next, the EEG data over all sensors are averaged with respect to event timing, thus calculating event-related potentials (ERPs). The ERPs are subjected to temporal ICA (tICA), and the resulting components are localized with the weighted minimum norm (WMNLS) algorithm using the task-related fMRI networks as priors. Finally, the temporal contribution of each ERP component in the areas belonging to the fMRI large-scale networks is estimated. The proposed approach has been evaluated on visual target detection data. Our results confirm that two different components, commonly observed in EEG when presenting novel and salient stimuli respectively, are related to the neuronal activation in large-scale networks, operating at different latencies and associated with different functional processes. PMID:20052528

3D Actin Network Centerline Extraction with Multiple Active Contours

PubMed Central

Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei

2013-01-01

Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and actin cables. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we propose a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D Total Internal Reflection Fluorescence Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy. Quantitative evaluation of the method using synthetic images shows that for images with SNR above 5.0, the average vertex error measured by the distance between our result and ground truth is 1 voxel, and the average Hausdorff distance is below 10 voxels. PMID:24316442

Computer-Mediated Social Support for Physical Activity: A Content Analysis.

PubMed

Stragier, Jeroen; Mechant, Peter; De Marez, Lieven; Cardon, Greet

2018-02-01

Online fitness communities are a recent phenomenon experiencing growing user bases. They can be considered as online social networks in which recording, monitoring, and sharing of physical activity (PA) are the most prevalent practices. They have added a new dimension to the social experience of PA in which online peers function as virtual PA partners or supporters. However, research into seeking and receiving computer-mediated social support for PA is scarce. Our aim was to study to what extent using online fitness communities and sharing physical activities with online social networks results in receiving various types of online social support. Two databases, one containing physical activities logged with Strava and one containing physical activities logged with RunKeeper and shared on Twitter, were investigated for occurrence and type of social support, by means of a deductive content analysis. Results indicate that social support delivered through Twitter is not particularly extensive. On Strava, social support is significantly more prevalent. Especially esteem support, expressed as compliments for the accomplishment of an activity, is provided on both Strava and Twitter. The results demonstrate that social media have potential as a platform used for providing social support for PA, but differences among various social network sites can be substantial. Especially esteem support can be expected, in contrast to online health communities, where information support is more common.

Default mode network activation and Transcendental Meditation practice: Focused Attention or Automatic Self-transcending?

PubMed

Travis, Frederick; Parim, Niyazi

2017-02-01

This study used subjective reports and eLORETA analysis to assess to what extent Transcendental Meditation (TM) might involve focused attention-voluntary control of mental content. Eighty-seven TM subjects with one month to five years TM experience participated in this study. Regression analysis of years TM practice and self-reported transcendental experiences (lack of time, space and body sense) during meditation practice was flat (r=.07). Those practicing Transcendental Meditation for 1month reported as much transcending as those with 5years of practice. The eLORETA comparison of eyes-closed rest/task and TM practice/task identified similar areas of activation: theta and alpha activation during rest and TM in the posterior cingulate and precuneus, part of the default mode network, and beta2 and beta3 activation during the task in anterior cingulate, ventral lateral and dorsolateral prefrontal cortices, part of the central executive network. In addition, eLORETA comparison of rest and TM identified higher beta temporal activation during rest and higher theta orbitofrontal activation during TM. Thus, it does not seem accurate to include TM practice with meditations in the catgory of Focused Attention, which are characterized by gamma EEG and DMN deactivation. Mixing meditations with different procedures into a single study confounds exploration of meditation effects and confounds application of meditation practices to different subject populations. Copyright © 2016 Elsevier Inc. All rights reserved.

Ecological Network Analysis for a Low-Carbon and High-Tech Industrial Park

PubMed Central

Lu, Yi; Su, Meirong; Liu, Gengyuan; Chen, Bin; Zhou, Shiyi; Jiang, Meiming

2012-01-01

Industrial sector is one of the indispensable contributors in global warming. Even if the occurrence of ecoindustrial parks (EIPs) seems to be a good improvement in saving ecological crises, there is still a lack of definitional clarity and in-depth researches on low-carbon industrial parks. In order to reveal the processes of carbon metabolism in a low-carbon high-tech industrial park, we selected Beijing Development Area (BDA) International Business Park in Beijing, China as case study, establishing a seven-compartment- model low-carbon metabolic network based on the methodology of Ecological Network Analysis (ENA). Integrating the Network Utility Analysis (NUA), Network Control Analysis (NCA), and system-wide indicators, we compartmentalized system sectors into ecological structure and analyzed dependence and control degree based on carbon metabolism. The results suggest that indirect flows reveal more mutuality and exploitation relation between system compartments and they are prone to positive sides for the stability of the whole system. The ecological structure develops well as an approximate pyramidal structure, and the carbon metabolism of BDA proves self-mutualistic and sustainable. Construction and waste management were found to be two active sectors impacting carbon metabolism, which was mainly regulated by internal and external environment. PMID:23365516

Patient-specific connectivity pattern of epileptic network in frontal lobe epilepsy

PubMed Central

Luo, Cheng; An, Dongmei; Yao, Dezhong; Gotman, Jean

2014-01-01

There is evidence that focal epilepsy may involve the dysfunction of a brain network in addition to the focal region. To delineate the characteristics of this epileptic network, we collected EEG/fMRI data from 23 patients with frontal lobe epilepsy. For each patient, EEG/fMRI analysis was first performed to determine the BOLD response to epileptic spikes. The maximum activation cluster in the frontal lobe was then chosen as the seed to identify the epileptic network in fMRI data. Functional connectivity analysis seeded at the same region was also performed in 63 healthy control subjects. Nine features were used to evaluate the differences of epileptic network patterns in three connection levels between patients and controls. Compared with control subjects, patients showed overall more functional connections between the epileptogenic region and the rest of the brain and higher laterality. However, the significantly increased connections were located in the neighborhood of the seed, but the connections between the seed and remote regions actually decreased. Comparing fMRI runs with interictal epileptic discharges (IEDs) and without IEDs, the patient-specific connectivity pattern was not changed significantly. These findings regarding patient-specific connectivity patterns of epileptic networks in FLE reflect local high connectivity and connections with distant regions differing from those of healthy controls. Moreover, the difference between the two groups in most features was observed in the strictest of the three connection levels. The abnormally high connectivity might reflect a predominant attribute of the epileptic network, which may facilitate propagation of epileptic activity among regions in the network. PMID:24936418

Research and Application of Knowledge Resources Network for Product Innovation

PubMed Central

Li, Chuan; Li, Wen-qiang; Li, Yan; Na, Hui-zhen; Shi, Qian

2015-01-01

In order to enhance the capabilities of knowledge service in product innovation design service platform, a method of acquiring knowledge resources supporting for product innovation from the Internet and providing knowledge active push is proposed. Through knowledge modeling for product innovation based on ontology, the integrated architecture of knowledge resources network is put forward. The technology for the acquisition of network knowledge resources based on focused crawler and web services is studied. Knowledge active push is provided for users by user behavior analysis and knowledge evaluation in order to improve users' enthusiasm for participation in platform. Finally, an application example is illustrated to prove the effectiveness of the method. PMID:25884031

Social network analysis of child and adult interorganizational connections.

PubMed

Davis, Maryann; Koroloff, Nancy; Johnsen, Matthew

2012-01-01

Because most programs serve either children and their families or adults, a critical component of service and treatment continuity in mental health and related services for individuals transitioning into adulthood (ages 14-25) is coordination across programs on either side of the adult age divide. This study was conducted in Clark County, Washington, a community that had received a Partnership for Youth Transition grant from the Federal Center for Mental Health Services. Social Network Analysis methodology was used to describe the strength and direction of each organization's relationship to other organizations in the transition network. Interviews were conducted before grant implementation (n=103) and again four years later (n=99). The findings of the study revealed significant changes in the nature of relationships between organizations over time. While the overall density of the transition service network remained stable, specific ways of connecting did change. Some activities became more decentralized while others became more inclusive as evidenced by the increase in size of the largest K-core. This was particularly true for the activity of "receiving referrals." These changes reflected more direct contact between child and adult serving organizations. The two separate child and adult systems identified at baseline appeared more integrated by the end of the grant period. Having greater connectivity among all organizations regardless of ages served should benefit youth and young adults of transition age. This study provides further evidence that Social Network Analysis is a useful method for measuring change in service system integration over time.

Oscillatory activity in neocortical networks during tactile discrimination near the limit of spatial acuity.

PubMed

Adhikari, Bhim M; Sathian, K; Epstein, Charles M; Lamichhane, Bidhan; Dhamala, Mukesh

2014-05-01

Oscillatory interactions within functionally specialized but distributed brain regions are believed to be central to perceptual and cognitive functions. Here, using human scalp electroencephalography (EEG) recordings combined with source reconstruction techniques, we study how oscillatory activity functionally organizes different neocortical regions during a tactile discrimination task near the limit of spatial acuity. While undergoing EEG recordings, blindfolded participants felt a linear three-dot array presented electromechanically, under computer control, and reported whether the central dot was offset to the left or right. The average brain response differed significantly for trials with correct and incorrect perceptual responses in the timeframe approximately between 130 and 175ms. During trials with correct responses, source-level peak activity appeared in the left primary somatosensory cortex (SI) at around 45ms, in the right lateral occipital complex (LOC) at 130ms, in the right posterior intraparietal sulcus (pIPS) at 160ms, and finally in the left dorsolateral prefrontal cortex (dlPFC) at 175ms. Spectral interdependency analysis of activity in these nodes showed two distinct distributed networks, a dominantly feedforward network in the beta band (12-30Hz) that included all four nodes and a recurrent network in the gamma band (30-100Hz) that linked SI, pIPS and dlPFC. Measures of network activity in both bands were correlated with the accuracy of task performance. These findings suggest that beta and gamma band oscillatory networks coordinate activity between neocortical regions mediating sensory and cognitive processing to arrive at tactile perceptual decisions. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Evaluating conducting network based transparent electrodes from geometrical considerations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kumar, Ankush; Kulkarni, G. U., E-mail: guk@cens.res.in

2016-01-07

Conducting nanowire networks have been developed as viable alternative to existing indium tin oxide based transparent electrode (TE). The nature of electrical conduction and process optimization for electrodes have gained much from the theoretical models based on percolation transport using Monte Carlo approach and applying Kirchhoff's law on individual junctions and loops. While most of the literature work pertaining to theoretical analysis is focussed on networks obtained from conducting rods (mostly considering only junction resistance), hardly any attention has been paid to those made using template based methods, wherein the structure of network is neither similar to network obtained frommore » conducting rods nor similar to well periodic geometry. Here, we have attempted an analytical treatment based on geometrical arguments and applied image analysis on practical networks to gain deeper insight into conducting networked structure particularly in relation to sheet resistance and transmittance. Many literature examples reporting networks with straight or curvilinear wires with distributions in wire width and length have been analysed by treating the networks as two dimensional graphs and evaluating the sheet resistance based on wire density and wire width. The sheet resistance values from our analysis compare well with the experimental values. Our analysis on various examples has revealed that low sheet resistance is achieved with high wire density and compactness with straight rather than curvilinear wires and with narrower wire width distribution. Similarly, higher transmittance for given sheet resistance is possible with narrower wire width but of higher thickness, minimal curvilinearity, and maximum connectivity. For the purpose of evaluating active fraction of the network, the algorithm was made to distinguish and quantify current carrying backbone regions as against regions containing only dangling or isolated wires. The treatment can be helpful in predicting the properties of a network simply from image analysis and will be helpful in improvisation and comparison of various TEs and better understanding of electrical percolation.« less

Evaluating conducting network based transparent electrodes from geometrical considerations

NASA Astrophysics Data System (ADS)

Kumar, Ankush; Kulkarni, G. U.

2016-01-01

Conducting nanowire networks have been developed as viable alternative to existing indium tin oxide based transparent electrode (TE). The nature of electrical conduction and process optimization for electrodes have gained much from the theoretical models based on percolation transport using Monte Carlo approach and applying Kirchhoff's law on individual junctions and loops. While most of the literature work pertaining to theoretical analysis is focussed on networks obtained from conducting rods (mostly considering only junction resistance), hardly any attention has been paid to those made using template based methods, wherein the structure of network is neither similar to network obtained from conducting rods nor similar to well periodic geometry. Here, we have attempted an analytical treatment based on geometrical arguments and applied image analysis on practical networks to gain deeper insight into conducting networked structure particularly in relation to sheet resistance and transmittance. Many literature examples reporting networks with straight or curvilinear wires with distributions in wire width and length have been analysed by treating the networks as two dimensional graphs and evaluating the sheet resistance based on wire density and wire width. The sheet resistance values from our analysis compare well with the experimental values. Our analysis on various examples has revealed that low sheet resistance is achieved with high wire density and compactness with straight rather than curvilinear wires and with narrower wire width distribution. Similarly, higher transmittance for given sheet resistance is possible with narrower wire width but of higher thickness, minimal curvilinearity, and maximum connectivity. For the purpose of evaluating active fraction of the network, the algorithm was made to distinguish and quantify current carrying backbone regions as against regions containing only dangling or isolated wires. The treatment can be helpful in predicting the properties of a network simply from image analysis and will be helpful in improvisation and comparison of various TEs and better understanding of electrical percolation.

Social influence and motivation to change health behaviors among Mexican origin adults: Implications for diet and physical activity

PubMed Central

Ashida, Sato; Wilkinson, Anna V.; Koehly, Laura M.

2011-01-01

Purpose To evaluate whether influence from social network members is associated with motivation to change dietary and physical activity behaviors. Design Baseline assessment followed by mailing of family health history-based personalized messages (2 weeks) and follow-up assessment (3 months). Setting Families from an ongoing population-based cohort in Houston, TX. Subjects 475 adults from 161 Mexican origin families. Out of 347 households contacted, 162 (47%) participated. Measures Family health history, social networks, and motivation to change behaviors. Analysis Two-level logistic regression modeling. Results Having at least one network member who encourages one to eat more fruits and vegetables (p=.010) and to engage in regular physical activity (p=.046) was associated with motivation to change the relevant behavior. About 40% of the participants did not have encouragers for these behaviors. Conclusions Identification of new encouragers within networks and targeting natural encouragers (e.g., children, spouses) may increase the efficacy of interventions to motivate behavioral changes among Mexican origin adults. PMID:22208416

Burstiness and tie activation strategies in time-varying social networks

NASA Astrophysics Data System (ADS)

Ubaldi, Enrico; Vezzani, Alessandro; Karsai, Márton; Perra, Nicola; Burioni, Raffaella

2017-04-01

The recent developments in the field of social networks shifted the focus from static to dynamical representations, calling for new methods for their analysis and modelling. Observations in real social systems identified two main mechanisms that play a primary role in networks’ evolution and influence ongoing spreading processes: the strategies individuals adopt when selecting between new or old social ties, and the bursty nature of the social activity setting the pace of these choices. We introduce a time-varying network model accounting both for ties selection and burstiness and we analytically study its phase diagram. The interplay of the two effects is non trivial and, interestingly, the effects of burstiness might be suppressed in regimes where individuals exhibit a strong preference towards previously activated ties. The results are tested against numerical simulations and compared with two empirical datasets with very good agreement. Consequently, the framework provides a principled method to classify the temporal features of real networks, and thus yields new insights to elucidate the effects of social dynamics on spreading processes.

Meditation experience is associated with differences in default mode network activity and connectivity

PubMed Central

Brewer, Judson A.; Worhunsky, Patrick D.; Gray, Jeremy R.; Tang, Yi-Yuan; Weber, Jochen; Kober, Hedy

2011-01-01

Many philosophical and contemplative traditions teach that “living in the moment” increases happiness. However, the default mode of humans appears to be that of mind-wandering, which correlates with unhappiness, and with activation in a network of brain areas associated with self-referential processing. We investigated brain activity in experienced meditators and matched meditation-naive controls as they performed several different meditations (Concentration, Loving-Kindness, Choiceless Awareness). We found that the main nodes of the default-mode network (medial prefrontal and posterior cingulate cortices) were relatively deactivated in experienced meditators across all meditation types. Furthermore, functional connectivity analysis revealed stronger coupling in experienced meditators between the posterior cingulate, dorsal anterior cingulate, and dorsolateral prefrontal cortices (regions previously implicated in self-monitoring and cognitive control), both at baseline and during meditation. Our findings demonstrate differences in the default-mode network that are consistent with decreased mind-wandering. As such, these provide a unique understanding of possible neural mechanisms of meditation. PMID:22114193

Analyzing psychotherapy process as intersubjective sensemaking: an approach based on discourse analysis and neural networks.

PubMed

Nitti, Mariangela; Ciavolino, Enrico; Salvatore, Sergio; Gennaro, Alessandro

2010-09-01

The authors propose a method for analyzing the psychotherapy process: discourse flow analysis (DFA). DFA is a technique representing the verbal interaction between therapist and patient as a discourse network, aimed at measuring the therapist-patient discourse ability to generate new meanings through time. DFA assumes that the main function of psychotherapy is to produce semiotic novelty. DFA is applied to the verbatim transcript of the psychotherapy. It defines the main meanings active within the therapeutic discourse by means of the combined use of text analysis and statistical techniques. Subsequently, it represents the dynamic interconnections among these meanings in terms of a "discursive network." The dynamic and structural indexes of the discursive network have been shown to provide a valid representation of the patient-therapist communicative flow as well as an estimation of its clinical quality. Finally, a neural network is designed specifically to identify patterns of functioning of the discursive network and to verify the clinical validity of these patterns in terms of their association with specific phases of the psychotherapy process. An application of the DFA to a case of psychotherapy is provided to illustrate the method and the kinds of results it produces.

A novel information cascade model in online social networks

NASA Astrophysics Data System (ADS)

Tong, Chao; He, Wenbo; Niu, Jianwei; Xie, Zhongyu

2016-02-01

The spread and diffusion of information has become one of the hot issues in today's social network analysis. To analyze the spread of online social network information and the attribute of cascade, in this paper, we discuss the spread of two kinds of users' decisions for city-wide activities, namely the "want to take part in the activity" and "be interested in the activity", based on the users' attention in "DouBan" and the data of the city-wide activities. We analyze the characteristics of the activity-decision's spread in these aspects: the scale and scope of the cascade subgraph, the structure characteristic of the cascade subgraph, the topological attribute of spread tree, and the occurrence frequency of cascade subgraph. On this basis, we propose a new information spread model. Based on the classical independent diffusion model, we introduce three mechanisms, equal probability, similarity of nodes, and popularity of nodes, which can generate and affect the spread of information. Besides, by conducting the experiments in six different kinds of network data set, we compare the effects of three mechanisms above mentioned, totally six specific factors, on the spread of information, and put forward that the node's popularity plays an important role in the information spread.

Characterizing individual differences in reward sensitivity from the brain networks involved in response inhibition.

PubMed

Fuentes-Claramonte, Paola; Ávila, César; Rodríguez-Pujadas, Aina; Costumero, Víctor; Ventura-Campos, Noelia; Bustamante, Juan Carlos; Rosell-Negre, Patricia; Barrós-Loscertales, Alfonso

2016-01-01

A "disinhibited" cognitive profile has been proposed for individuals with high reward sensitivity, characterized by increased engagement in goal-directed responses and reduced processing of negative or unexpected cues, which impairs adequate behavioral regulation after feedback in these individuals. This pattern is manifested through deficits in inhibitory control and/or increases in RT variability. In the present work, we aimed to test whether this profile is associated with the activity of functional networks during a stop-signal task using independent component analysis (ICA). Sixty-one participants underwent fMRI while performing a stop-signal task, during which a manual response had to be inhibited. ICA was used to mainly replicate the functional networks involved in the task (Zhang and Li, 2012): two motor networks involved in the go response, the left and right fronto-parietal networks for stopping, a midline error-processing network, and the default-mode network (DMN), which was further subdivided into its anterior and posterior parts. Reward sensitivity was mainly associated with greater activity of motor networks, reduced activity in the midline network during correct stop trials and, behaviorally, increased RT variability. All these variables explained 36% of variance of the SR scores. This pattern of associations suggests that reward sensitivity involves greater motor engagement in the dominant response, more distractibility and reduced processing of salient or unexpected events, which may lead to disinhibited behavior. Copyright © 2015 Elsevier Inc. All rights reserved.

[Efficacy of racecadotril vs. smectite, probiotics or zinc as an integral part of treatment of acute diarrhea in children under five years: A meta-analysis of multiple treatments].

PubMed

Gutiérrez-Castrellón, Pedro; Ortíz-Hernández, Anna Alejandra; Llamosas-Gallardo, Beatriz; Acosta-Bastidas, Mario A; Jiménez-Gutiérrez, Carlos; Diaz-García, Luisa; Anzo-Osorio, Anahí; Estevez-Jiménez, Juliana; Jiménez-Escobar, Irma; Vidal-Vázquez, Rosa Patricia

2015-01-01

Despite major advances in treatment, acute diarrhea continues to be a public health problem in children under five years. There is no systematic approach to treatment and most evidence is assembled comparing active treatment vs. placebo. Systematic review of evidence on efficacy of adjuvants for treatment of acute diarrhea through a network meta-analysis. A systematic search of multiple databases searching clinical trials related to the use of racecadotril, smectite, Lactobacillus GG, Lactobacillus reuteri, Saccharomyces boulardii and zinc as adjuvants in acute diarrhea was done. The primary endpoint was duration of diarrhea. Information is displayed through network meta-analysis.The superiority of each coadjutant was analyzed by Sucra approach. Network meta-analysis showed race cadotril was better when compared with placebo and other adjuvants. Sucra analysis showed racecadotril as the first option followed by smectite and Lactobacillus reuteri. Considering a strategic decision making approach, network meta-analysis allows us to establish the therapeutic superiority of racecadotril as an adjunct for the comprehensive management of acute diarrhea in children aged less than five years.

Characterization of Early Cortical Neural Network ...

EPA Pesticide Factsheets

We examined the development of neural network activity using microelectrode array (MEA) recordings made in multi-well MEA plates (mwMEAs) over the first 12 days in vitro (DIV). In primary cortical cultures made from postnatal rats, action potential spiking activity was essentially absent on DIV 2 and developed rapidly between DIV 5 and 12. Spiking activity was primarily sporadic and unorganized at early DIV, and became progressively more organized with time in culture, with bursting parameters, synchrony and network bursting increasing between DIV 5 and 12. We selected 12 features to describe network activity and principal components analysis using these features demonstrated a general segregation of data by age at both the well and plate levels. Using a combination of random forest classifiers and Support Vector Machines, we demonstrated that 4 features (CV of within burst ISI, CV of IBI, network spike rate and burst rate) were sufficient to predict the age (either DIV 5, 7, 9 or 12) of each well recording with >65% accuracy. When restricting the classification problem to a binary decision, we found that classification improved dramatically, e.g. 95% accuracy for discriminating DIV 5 vs DIV 12 wells. Further, we present a novel resampling approach to determine the number of wells that might be needed for conducting comparisons of different treatments using mwMEA plates. Overall, these results demonstrate that network development on mwMEA plates is similar to

Comparison of Functional Network Connectivity for Passive-Listening and Active-Response Narrative Comprehension in Adolescents

PubMed Central

Holland, Scott K.

2014-01-01

Abstract Comprehension of narrative stories plays an important role in the development of language skills. In this study, we compared brain activity elicited by a passive-listening version and an active-response (AR) version of a narrative comprehension task by using independent component (IC) analysis on functional magnetic resonance imaging data from 21 adolescents (ages 14–18 years). Furthermore, we explored differences in functional network connectivity engaged by two versions of the task and investigated the relationship between the online response time and the strength of connectivity between each pair of ICs. Despite similar brain region involvements in auditory, temporoparietal, and frontoparietal language networks for both versions, the AR version engages some additional network elements including the left dorsolateral prefrontal, anterior cingulate, and sensorimotor networks. These additional involvements are likely associated with working memory and maintenance of attention, which can be attributed to the differences in cognitive strategic aspects of the two versions. We found significant positive correlation between the online response time and the strength of connectivity between an IC in left inferior frontal region and an IC in sensorimotor region. An explanation for this finding is that longer reaction time indicates stronger connection between the frontal and sensorimotor networks caused by increased activation in adolescents who require more effort to complete the task. PMID:24689887

Comparative Analysis of University-Government-Enterprise Co-Authorship Networks in Three Scientific Domains in the Region of Madrid

ERIC Educational Resources Information Center

Olmeda-Gomez, Carlos; Perianes-Rodriguez, Antonio; Ovalle-Perandones, Maria Antonia; Moya-Anegon, Felix

2008-01-01

Introduction: In an economy geared to innovation and competitiveness in research and development activities, inter-relationships between the university, private enterprise and government are of considerable interest. Networking constitutes a priority strategy to attain this strategic objective and a tool in knowledge-based economies. Method:…

The Multilingual Education (MLE) Network Phenomenon: Advocacy and Action for Minoritized Language Communities

ERIC Educational Resources Information Center

Trudell, Barbara

2014-01-01

This article examines a new phenomenon in language activism variously called the multilingual education working group or the multilingual education network, and abbreviated as MLEN. After an analysis of the conceptual and organizational contexts for these activist groups, the six MLENs in existence as of 2013 are described. The groups are then…

Extracting Intrinsic Functional Networks with Feature-Based Group Independent Component Analysis

ERIC Educational Resources Information Center

Calhoun, Vince D.; Allen, Elena

2013-01-01

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in…

Synaptic activity induces input-specific rearrangements in a targeted synaptic protein interaction network.

PubMed

Lautz, Jonathan D; Brown, Emily A; VanSchoiack, Alison A Williams; Smith, Stephen E P

2018-05-27

Cells utilize dynamic, network level rearrangements in highly interconnected protein interaction networks to transmit and integrate information from distinct signaling inputs. Despite the importance of protein interaction network dynamics, the organizational logic underlying information flow through these networks is not well understood. Previously, we developed the quantitative multiplex co-immunoprecipitation platform, which allows for the simultaneous and quantitative measurement of the amount of co-association between large numbers of proteins in shared complexes. Here, we adapt quantitative multiplex co-immunoprecipitation to define the activity dependent dynamics of an 18-member protein interaction network in order to better understand the underlying principles governing glutamatergic signal transduction. We first establish that immunoprecipitation detected by flow cytometry can detect activity dependent changes in two known protein-protein interactions (Homer1-mGluR5 and PSD-95-SynGAP). We next demonstrate that neuronal stimulation elicits a coordinated change in our targeted protein interaction network, characterized by the initial dissociation of Homer1 and SynGAP-containing complexes followed by increased associations among glutamate receptors and PSD-95. Finally, we show that stimulation of distinct glutamate receptor types results in different modular sets of protein interaction network rearrangements, and that cells activate both modules in order to integrate complex inputs. This analysis demonstrates that cells respond to distinct types of glutamatergic input by modulating different combinations of protein co-associations among a targeted network of proteins. Our data support a model of synaptic plasticity in which synaptic stimulation elicits dissociation of preexisting multiprotein complexes, opening binding slots in scaffold proteins and allowing for the recruitment of additional glutamatergic receptors. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Characterization of functional brain activity and connectivity using EEG and fMRI in patients with sickle cell disease.

PubMed

Case, Michelle; Zhang, Huishi; Mundahl, John; Datta, Yvonne; Nelson, Stephen; Gupta, Kalpna; He, Bin

2017-01-01

Sickle cell disease (SCD) is a red blood cell disorder that causes many complications including life-long pain. Treatment of pain remains challenging due to a poor understanding of the mechanisms and limitations to characterize and quantify pain. In the present study, we examined simultaneously recording functional MRI (fMRI) and electroencephalogram (EEG) to better understand neural connectivity as a consequence of chronic pain in SCD patients. We performed independent component analysis and seed-based connectivity on fMRI data. Spontaneous power and microstate analysis was performed on EEG-fMRI data. ICA analysis showed that patients lacked activity in the default mode network (DMN) and executive control network compared to controls. EEG-fMRI data revealed that the insula cortex's role in salience increases with age in patients. EEG microstate analysis showed patients had increased activity in pain processing regions. The cerebellum in patients showed a stronger connection to the periaqueductal gray matter (involved in pain inhibition), and negative connections to pain processing areas. These results suggest that patients have reduced activity of DMN and increased activity in pain processing regions during rest. The present findings suggest resting state connectivity differences between patients and controls can be used as novel biomarkers of SCD pain.

Functional organization of human subgenual cortical areas: Relationship between architectonical segregation and connectional heterogeneity.

PubMed

Palomero-Gallagher, Nicola; Eickhoff, Simon B; Hoffstaedter, Felix; Schleicher, Axel; Mohlberg, Hartmut; Vogt, Brent A; Amunts, Katrin; Zilles, Karl

2015-07-15

Human subgenual anterior cingulate cortex (sACC) is involved in affective experiences and fear processing. Functional neuroimaging studies view it as a homogeneous cortical entity. However, sACC comprises several distinct cyto- and receptorarchitectonical areas: 25, s24, s32, and the ventral portion of area 33. Thus, we hypothesized that the areas may also be connectionally and functionally distinct. We performed structural post mortem and functional in vivo analyses. We computed probabilistic maps of each area based on cytoarchitectonical analysis of ten post mortem brains. Maps, publicly available via the JuBrain atlas and the Anatomy Toolbox, were used to define seed regions of task-dependent functional connectivity profiles and quantitative functional decoding. sACC areas presented distinct co-activation patterns within widespread networks encompassing cortical and subcortical regions. They shared common functional domains related to emotion, perception and cognition. A more specific analysis of these domains revealed an association of s24 with sadness, and of s32 with fear processing. Both areas were activated during taste evaluation, and co-activated with the amygdala, a key node of the affective network. s32 co-activated with areas of the executive control network, and was associated with tasks probing cognition in which stimuli did not have an emotional component. Area 33 was activated by painful stimuli, and co-activated with areas of the sensorimotor network. These results support the concept of a connectional and functional specificity of the cyto- and receptorarchitectonically defined areas within the sACC, which can no longer be seen as a structurally and functionally homogeneous brain region. Copyright © 2015 Elsevier Inc. All rights reserved.

Preliminary evidence of reduced brain network activation in patients with post-traumatic migraine following concussion.

PubMed

Kontos, Anthony P; Reches, Amit; Elbin, R J; Dickman, Dalia; Laufer, Ilan; Geva, Amir B; Shacham, Galit; DeWolf, Ryan; Collins, Michael W

2016-06-01

Post-traumatic migraine (PTM) (i.e., headache, nausea, light and/or noise sensitivity) is an emerging risk factor for prolonged recovery following concussion. Concussions and migraine share similar pathophysiology characterized by specific ionic imbalances in the brain. Given these similarities, patients with PTM following concussion may exhibit distinct electrophysiological patterns, although researchers have yet to examine the electrophysiological brain activation in patients with PTM following concussion. A novel approach that may help differentiate brain activation in patients with and without PTM is brain network activation (BNA) analysis. BNA involves an algorithmic analysis applied to multichannel EEG-ERP data that provides a network map of cortical activity and quantitative data during specific tasks. A prospective, repeated measures design was used to evaluate BNA (during Go/NoGo task), EEG-ERP, cognitive performance, and concussion related symptoms at 1, 2, 3, and 4 weeks post-injury intervals among athletes with a medically diagnosed concussion with PTM (n = 15) and without (NO-PTM) (n = 22); and age, sex, and concussion history matched controls without concussion (CONTROL) (n = 20). Participants with PTM had significantly reduced BNA compared to NO-PTM and CONTROLS for Go and NoGo components at 3 weeks and for NoGo component at 4 weeks post-injury. The PTM group also demonstrated a more prominent deviation of network activity compared to the other two groups over a longer period of time. The composite BNA algorithm may be a more sensitive measure of electrophysiological change in the brain that can augment established cognitive assessment tools for detecting impairment in individuals with PTM.

Aberrant gene expression in mucosa adjacent to tumor reveals a molecular crosstalk in colon cancer

PubMed Central

2014-01-01

Background A colorectal tumor is not an isolated entity growing in a restricted location of the body. The patient’s gut environment constitutes the framework where the tumor evolves and this relationship promotes and includes a complex and tight correlation of the tumor with inflammation, blood vessels formation, nutrition, and gut microbiome composition. The tumor influence in the environment could both promote an anti-tumor or a pro-tumor response. Methods A set of 98 paired adjacent mucosa and tumor tissues from colorectal cancer (CRC) patients and 50 colon mucosa from healthy donors (246 samples in total) were included in this work. RNA extracted from each sample was hybridized in Affymetrix chips Human Genome U219. Functional relationships between genes were inferred by means of systems biology using both transcriptional regulation networks (ARACNe algorithm) and protein-protein interaction networks (BIANA software). Results Here we report a transcriptomic analysis revealing a number of genes activated in adjacent mucosa from CRC patients, not activated in mucosa from healthy donors. A functional analysis of these genes suggested that this active reaction of the adjacent mucosa was related to the presence of the tumor. Transcriptional and protein-interaction networks were used to further elucidate this response of normal gut in front of the tumor, revealing a crosstalk between proteins secreted by the tumor and receptors activated in the adjacent colon tissue; and vice versa. Remarkably, Slit family of proteins activated ROBO receptors in tumor whereas tumor-secreted proteins transduced a cellular signal finally activating AP-1 in adjacent tissue. Conclusions The systems-level approach provides new insights into the micro-ecology of colorectal tumorogenesis. Disrupting this intricate molecular network of cell-cell communication and pro-inflammatory microenvironment could be a therapeutic target in CRC patients. PMID:24597571

Network meta-analysis: application and practice using Stata

PubMed Central

2017-01-01

This review aimed to arrange the concepts of a network meta-analysis (NMA) and to demonstrate the analytical process of NMA using Stata software under frequentist framework. The NMA tries to synthesize evidences for a decision making by evaluating the comparative effectiveness of more than two alternative interventions for the same condition. Before conducting a NMA, 3 major assumptions—similarity, transitivity, and consistency—should be checked. The statistical analysis consists of 5 steps. The first step is to draw a network geometry to provide an overview of the network relationship. The second step checks the assumption of consistency. The third step is to make the network forest plot or interval plot in order to illustrate the summary size of comparative effectiveness among various interventions. The fourth step calculates cumulative rankings for identifying superiority among interventions. The last step evaluates publication bias or effect modifiers for a valid inference from results. The synthesized evidences through five steps would be very useful to evidence-based decision-making in healthcare. Thus, NMA should be activated in order to guarantee the quality of healthcare system. PMID:29092392

Network meta-analysis: application and practice using Stata.

PubMed

Shim, Sungryul; Yoon, Byung-Ho; Shin, In-Soo; Bae, Jong-Myon

2017-01-01

This review aimed to arrange the concepts of a network meta-analysis (NMA) and to demonstrate the analytical process of NMA using Stata software under frequentist framework. The NMA tries to synthesize evidences for a decision making by evaluating the comparative effectiveness of more than two alternative interventions for the same condition. Before conducting a NMA, 3 major assumptions-similarity, transitivity, and consistency-should be checked. The statistical analysis consists of 5 steps. The first step is to draw a network geometry to provide an overview of the network relationship. The second step checks the assumption of consistency. The third step is to make the network forest plot or interval plot in order to illustrate the summary size of comparative effectiveness among various interventions. The fourth step calculates cumulative rankings for identifying superiority among interventions. The last step evaluates publication bias or effect modifiers for a valid inference from results. The synthesized evidences through five steps would be very useful to evidence-based decision-making in healthcare. Thus, NMA should be activated in order to guarantee the quality of healthcare system.

Glucocorticoid Administration Improves Aberrant Fear-Processing Networks in Spider Phobia

PubMed Central

Nakataki, Masahito; Soravia, Leila M; Schwab, Simon; Horn, Helge; Dierks, Thomas; Strik, Werner; Wiest, Roland; Heinrichs, Markus; de Quervain, Dominique J-F; Federspiel, Andrea; Morishima, Yosuke

2017-01-01

Glucocorticoids reduce phobic fear in patients with anxiety disorders. Previous studies have shown that fear-related activation of the amygdala can be mediated through the visual cortical pathway, which includes the fusiform gyrus, or through other pathways. However, it is not clear which of the pathways that activate the amygdala is responsible for the pathophysiology of a specific phobia and how glucocorticoid treatment alleviates fear processing in these neural networks. We recorded the brain activity with functional magnetic resonance imaging in patients with spider phobia, who received either 20 mg of cortisol or a placebo while viewing pictures of spiders. We also tested healthy participants who did not receive any medication during the same task. We performed dynamic causal modelling (DCM), a connectivity analysis, to examine the effects of cortisol on the networks involved in processing fear and to examine if there was an association between these networks and the symptoms of the phobia. Cortisol administration suppressed the phobic stimuli-related amygdala activity to levels comparable to the healthy participants and reduced subjective phobic fear. The DCM analysis revealed that cortisol administration suppressed the aberrant inputs into the amygdala that did not originate from the visual cortical pathway, but rather from a fast subcortical pathway mediated by the pulvinar nucleus, and suppressed the interactions between the amygdala and fusiform gyrus. This network changes were distinguishable from healthy participants and considered the residual changes under cortisol administration. We also found that the strengths of the aberrant inputs into the amygdala were positively correlated with the severity of spider phobia. This study demonstrates that patients with spider phobia show an aberrant functional connectivity of the amygdala when they are exposed to phobia-related stimuli and that cortisol administration can alleviate this fear-specific neural connectivity. PMID:27644128

Glucocorticoid Administration Improves Aberrant Fear-Processing Networks in Spider Phobia.

PubMed

Nakataki, Masahito; Soravia, Leila M; Schwab, Simon; Horn, Helge; Dierks, Thomas; Strik, Werner; Wiest, Roland; Heinrichs, Markus; de Quervain, Dominique J-F; Federspiel, Andrea; Morishima, Yosuke

2017-01-01

Glucocorticoids reduce phobic fear in patients with anxiety disorders. Previous studies have shown that fear-related activation of the amygdala can be mediated through the visual cortical pathway, which includes the fusiform gyrus, or through other pathways. However, it is not clear which of the pathways that activate the amygdala is responsible for the pathophysiology of a specific phobia and how glucocorticoid treatment alleviates fear processing in these neural networks. We recorded the brain activity with functional magnetic resonance imaging in patients with spider phobia, who received either 20 mg of cortisol or a placebo while viewing pictures of spiders. We also tested healthy participants who did not receive any medication during the same task. We performed dynamic causal modelling (DCM), a connectivity analysis, to examine the effects of cortisol on the networks involved in processing fear and to examine if there was an association between these networks and the symptoms of the phobia. Cortisol administration suppressed the phobic stimuli-related amygdala activity to levels comparable to the healthy participants and reduced subjective phobic fear. The DCM analysis revealed that cortisol administration suppressed the aberrant inputs into the amygdala that did not originate from the visual cortical pathway, but rather from a fast subcortical pathway mediated by the pulvinar nucleus, and suppressed the interactions between the amygdala and fusiform gyrus. This network changes were distinguishable from healthy participants and considered the residual changes under cortisol administration. We also found that the strengths of the aberrant inputs into the amygdala were positively correlated with the severity of spider phobia. This study demonstrates that patients with spider phobia show an aberrant functional connectivity of the amygdala when they are exposed to phobia-related stimuli and that cortisol administration can alleviate this fear-specific neural connectivity.

EGRINs (Environmental Gene Regulatory Influence Networks) in Rice That Function in the Response to Water Deficit, High Temperature, and Agricultural Environments[OPEN

PubMed Central

Hafemeister, Christoph; Nicotra, Adrienne B.; Jagadish, S.V. Krishna; Bonneau, Richard; Purugganan, Michael

2016-01-01

Environmental gene regulatory influence networks (EGRINs) coordinate the timing and rate of gene expression in response to environmental signals. EGRINs encompass many layers of regulation, which culminate in changes in accumulated transcript levels. Here, we inferred EGRINs for the response of five tropical Asian rice (Oryza sativa) cultivars to high temperatures, water deficit, and agricultural field conditions by systematically integrating time-series transcriptome data, patterns of nucleosome-free chromatin, and the occurrence of known cis-regulatory elements. First, we identified 5447 putative target genes for 445 transcription factors (TFs) by connecting TFs with genes harboring known cis-regulatory motifs in nucleosome-free regions proximal to their transcriptional start sites. We then used network component analysis to estimate the regulatory activity for each TF based on the expression of its putative target genes. Finally, we inferred an EGRIN using the estimated transcription factor activity (TFA) as the regulator. The EGRINs include regulatory interactions between 4052 target genes regulated by 113 TFs. We resolved distinct regulatory roles for members of the heat shock factor family, including a putative regulatory connection between abiotic stress and the circadian clock. TFA estimation using network component analysis is an effective way of incorporating multiple genome-scale measurements into network inference. PMID:27655842

Bulgarian Seismological and GPS/GNSS networks-current status and practical implementation

NASA Astrophysics Data System (ADS)

Solakov, Dimcho; Simeonova, Stela; Georgiev, Ivan; Dimitrova, Lilia; Slavcheva, Krasimira; Raykova, Plamena

2016-04-01

The scientific information is the latest and one of the best bedrock on which effective policy to combat and cope with natural disasters have to be built. Understanding, monitoring and information for future natural disasters are the way to assist the government and society. Different types of networks provide reliable information on various natural disasters. For example, one of the main priorities of the networks are directed to study seismicity of the Earth, its physical phenomena and fields - with an emphasis on tectonic movements and related risk processes, global changes, rotation and position of the Earth in space. Therefore seismological network using advanced electronic systems and digital seismographs transmission of signals from seismic stations to the centres and the registration, processing and archiving of information is carried out by a specialized computer system. Thus improve the monitoring and analysis of seismicity in the whole plan. Another type networks as permanent GPS/GNSS networks are associated with processing and data analysis, as well as monitoring of recent movements of the earth crust. In this study we focus on Seismological and GPS/GNSS networks on the territory in Bulgaria. At present NIGGG-BAS runs both Bulgarian seismological and GPS/GNSS networks. The Bulgarian seismological network - NOTSSI (National Operative Telemetric System for Seismological Information) was founded at the end of 1980. The network comprises today 15 permanent seismic stations spanning the entire territory of the country and two local net works that are deployed around the town of Provadia and Kozloduy Nuclear Power Plant in Bulgaria. Since 2005-2006, real-time data exchange between Bulgaria and Greece, Romania, Serbia, Macedonia, Slovakia, Slovenia, Austria and other regional and national seismological data centers was implemented. NIGGG, respectively NOTSSI, is responsible for rapid earthquake determination, public information trough media, and information of responsible governmental authorities if necessary urgent activities to be undertaken. The available infrastructure - permanent GNSS stations, spread all over the country allow performing permanent monitoring of the Earth's crust movements on the basis of the obtained velocities of the permanent stations and the time series with their coordinates. Additional information for the current movements is obtained by the processing and analysis of the regular GNSS measurements of geodynamic network. In the GNSS Analysis Center are acquired, processed and analyzed data from more than 70 permanent stations on Bulgarian territory. In the analysis are included also data from permanent stations on the Balkan Peninsula and from the European Permanent Network. Along with the seismological and geological information, the quantitative assessment of the movements of the Earth's crust is of the substantial importance for monitoring of the active tectonic structures and is the base for the seismic hazard assessment.

Concurrent enterprise: a conceptual framework for enterprise supply-chain network activities

NASA Astrophysics Data System (ADS)

Addo-Tenkorang, Richard; Helo, Petri T.; Kantola, Jussi

2017-04-01

Supply-chain management (SCM) in manufacturing industries has evolved significantly over the years. Recently, a lot more relevant research has picked up on the development of integrated solutions. Thus, seeking a collaborative optimisation of geographical, just-in-time (JIT), quality (customer demand/satisfaction) and return-on-investment (profits), aspects of organisational management and planning through 'best practice' business-process management - concepts and application; employing system tools such as certain applications/aspects of enterprise resource planning (ERP) - SCM systems information technology (IT) enablers to enhance enterprise integrated product development/concurrent engineering principles. This article assumed three main organisation theory applications in positioning its assumptions. Thus, proposing a feasible industry-specific framework not currently included within the SCOR model's level four (4) implementation level, as well as other existing SCM integration reference models such as in the MIT process handbook's - Process Interchange Format (PIF), the TOVE project, etc. which could also be replicated in other SCs. However, the wider focus of this paper's contribution will be concentrated on a complimentary proposed framework to the SCC's SCOR reference model. Quantitative empirical closed-ended questionnaires in addition to the main data collected from a qualitative empirical real-life industrial-based pilot case study were used: To propose a conceptual concurrent enterprise framework for SCM network activities. This research adopts a design structure matrix simulation approach analysis to propose an optimal enterprise SCM-networked value-adding, customised master data-management platform/portal for efficient SCM network information exchange and an effective supply-chain (SC) network systems-design teams' structure. Furthermore, social network theory analysis will be employed in a triangulation approach with statistical correlation analysis to assess the scale/level of frequency, importance, level of collaborative-ness, mutual trust as well as roles and responsibility among the enterprise SCM network for systems product development (PD) design teams' technical communication network as well as extensive literature reviews.

High solar activity predictions through an artificial neural network

NASA Astrophysics Data System (ADS)

Orozco-Del-Castillo, M. G.; Ortiz-Alemán, J. C.; Couder-Castañeda, C.; Hernández-Gómez, J. J.; Solís-Santomé, A.

The effects of high-energy particles coming from the Sun on human health as well as in the integrity of outer space electronics make the prediction of periods of high solar activity (HSA) a task of significant importance. Since periodicities in solar indexes have been identified, long-term predictions can be achieved. In this paper, we present a method based on an artificial neural network to find a pattern in some harmonics which represent such periodicities. We used data from 1973 to 2010 to train the neural network, and different historical data for its validation. We also used the neural network along with a statistical analysis of its performance with known data to predict periods of HSA with different confidence intervals according to the three-sigma rule associated with solar cycles 24-26, which we found to occur before 2040.

Reconstructing genome-wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities

PubMed Central

2011-01-01

Background Gene regulatory networks play essential roles in living organisms to control growth, keep internal metabolism running and respond to external environmental changes. Understanding the connections and the activity levels of regulators is important for the research of gene regulatory networks. While relevance score based algorithms that reconstruct gene regulatory networks from transcriptome data can infer genome-wide gene regulatory networks, they are unfortunately prone to false positive results. Transcription factor activities (TFAs) quantitatively reflect the ability of the transcription factor to regulate target genes. However, classic relevance score based gene regulatory network reconstruction algorithms use models do not include the TFA layer, thus missing a key regulatory element. Results This work integrates TFA prediction algorithms with relevance score based network reconstruction algorithms to reconstruct gene regulatory networks with improved accuracy over classic relevance score based algorithms. This method is called Gene expression and Transcription factor activity based Relevance Network (GTRNetwork). Different combinations of TFA prediction algorithms and relevance score functions have been applied to find the most efficient combination. When the integrated GTRNetwork method was applied to E. coli data, the reconstructed genome-wide gene regulatory network predicted 381 new regulatory links. This reconstructed gene regulatory network including the predicted new regulatory links show promising biological significances. Many of the new links are verified by known TF binding site information, and many other links can be verified from the literature and databases such as EcoCyc. The reconstructed gene regulatory network is applied to a recent transcriptome analysis of E. coli during isobutanol stress. In addition to the 16 significantly changed TFAs detected in the original paper, another 7 significantly changed TFAs have been detected by using our reconstructed network. Conclusions The GTRNetwork algorithm introduces the hidden layer TFA into classic relevance score-based gene regulatory network reconstruction processes. Integrating the TFA biological information with regulatory network reconstruction algorithms significantly improves both detection of new links and reduces that rate of false positives. The application of GTRNetwork on E. coli gene transcriptome data gives a set of potential regulatory links with promising biological significance for isobutanol stress and other conditions. PMID:21668997

An Exploratory Investigation of Functional Network Connectivity of Empathy and Default Mode Networks in a Free-Viewing Task.

PubMed

Vemuri, Kavita; Surampudi, Bapi Raju

2015-08-01

This study reports dynamic functional network connectivity (dFNC) analysis on time courses of putative empathy networks-cognitive, emotional, and motor-and the default mode network (DMN) identified from independent components (ICs) derived by the group independent component analysis (ICA) method. The functional magnetic resonance imaging (fMRI) data were collected from 15 subjects watching movies of three genres, an animation (S1), Indian Hindi (S2), and a Hollywood English (S3) movie. The hypothesis of the study is that empathic engagement in a movie narrative would modulate the activation with the DMN. The clippings were individually rated for emotional expressions, context, and empathy self-response by the fMRI subjects post scanning and by 40 participants in an independent survey who rated at four time intervals in each clipping. The analysis illustrates the following: (a) the ICA method separated ICs with areas reported for empathy response and anterior/posterior DMNs. An IC indicating insula region activation reported to be crucial for the emotional empathy network was separated for S2 and S3 movies only, but not for S1, (b) the dFNC between DMN and ICs corresponding to cognitive empathy network showed higher positive periodical fluctuating correlations for all three movies, while ICs with areas crucial to motor or emotional empathy display lower positive or negative correlation values with no distinct periodicity. A possible explanation for the lower values and anticorrelation between the DMN and emotional empathy networks could possibly be inhibition due to internal self-reflections, attributed to DMN, while processing and preparing a response to external emotional content. The positive higher correlation values for cognitive empathy networks may reflect a functional overlap with DMN for enhanced internal self-reflections, inferring beliefs and intentions about the 'other', all triggered by the external stimuli. The findings are useful in the study of deviations in functional synergies of large complex networks associated with empathy responses and DMN in clinical applications like autism and schizophrenia.

Monitoring Climate Variability and Change in Northern Alaska: Updates to the U.S. Geological Survey (USGS) Climate and Permafrost Monitoring Network

NASA Astrophysics Data System (ADS)

Urban, F. E.; Clow, G. D.; Meares, D. C.

2004-12-01

Observations of long-term climate and surficial geological processes are sparse in most of the Arctic, despite the fact that this region is highly sensitive to climate change. Instrumental networks that monitor the interplay of climatic variability and geological/cryospheric processes are a necessity for documenting and understanding climate change. Improvements to the spatial coverage and temporal scale of Arctic climate data are in progress. The USGS, in collaboration with The Bureau of Land Management (BLM) and The Fish and Wildlife Service (FWS) currently maintains two types of monitoring networks in northern Alaska: (1) A 15 site network of continuously operating active-layer and climate monitoring stations, and (2) a 21 element array of deep bore-holes in which the thermal state of deep permafrost is monitored. Here, we focus on the USGS Alaska Active Layer and Climate Monitoring Network (AK-CLIM). These 15 stations are deployed in longitudinal transects that span Alaska north of the Brooks Range, (11 in The National Petroleum Reserve Alaska, (NPRA), and 4 in The Arctic National Wildlife Refuge (ANWR)). An informative overview and update of the USGS AK-CLIM network is presented, including insight to current data, processing and analysis software, and plans for data telemetry. Data collection began in 1998 and parameters currently measured include air temperature, soil temperatures (5-120 cm), snow depth, incoming and reflected short-wave radiation, soil moisture (15 cm), wind speed and direction. Custom processing and analysis software has been written that calculates additional parameters such as active layer thaw depth, thawing-degree-days, albedo, cloudiness, and duration of seasonal snow cover. Data from selected AK-CLIM stations are now temporally sufficient to begin identifying trends, anomalies, and inter-annual variability in the climate of northern Alaska.

Macromolecular networks and intelligence in microorganisms

PubMed Central

Westerhoff, Hans V.; Brooks, Aaron N.; Simeonidis, Evangelos; García-Contreras, Rodolfo; He, Fei; Boogerd, Fred C.; Jackson, Victoria J.; Goncharuk, Valeri; Kolodkin, Alexey

2014-01-01

Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of information and communication technology (ICT): they receive information from the outside and inside of cells, integrate and interpret this information, and then activate a response. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. We have become accustomed to associating brain activity – particularly activity of the human brain – with a phenomenon we call “intelligence.” Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks of the brain. Here, we explore how macromolecular networks in microbes confer intelligent characteristics, such as memory, anticipation, adaptation and reflection and we review current understanding of how network organization reflects the type of intelligence required for the environments in which they were selected. We propose that, if we were to leave terms such as “human” and “brain” out of the defining features of “intelligence,” all forms of life – from microbes to humans – exhibit some or all characteristics consistent with “intelligence.” We then review advances in genome-wide data production and analysis, especially in microbes, that provide a lens into microbial intelligence and propose how the insights derived from quantitatively characterizing biomolecular networks may enable synthetic biologists to create intelligent molecular networks for biotechnology, possibly generating new forms of intelligence, first in silico and then in vivo. PMID:25101076

Functional connectivity analysis in resting state fMRI with echo-state networks and non-metric clustering for network structure recovery

NASA Astrophysics Data System (ADS)

Wismüller, Axel; DSouza, Adora M.; Abidin, Anas Z.; Wang, Xixi; Hobbs, Susan K.; Nagarajan, Mahesh B.

2015-03-01

Echo state networks (ESN) are recurrent neural networks where the hidden layer is replaced with a fixed reservoir of neurons. Unlike feed-forward networks, neuron training in ESN is restricted to the output neurons alone thereby providing a computational advantage. We demonstrate the use of such ESNs in our mutual connectivity analysis (MCA) framework for recovering the primary motor cortex network associated with hand movement from resting state functional MRI (fMRI) data. Such a framework consists of two steps - (1) defining a pair-wise affinity matrix between different pixel time series within the brain to characterize network activity and (2) recovering network components from the affinity matrix with non-metric clustering. Here, ESNs are used to evaluate pair-wise cross-estimation performance between pixel time series to create the affinity matrix, which is subsequently subject to non-metric clustering with the Louvain method. For comparison, the ground truth of the motor cortex network structure is established with a task-based fMRI sequence. Overlap between the primary motor cortex network recovered with our model free MCA approach and the ground truth was measured with the Dice coefficient. Our results show that network recovery with our proposed MCA approach is in close agreement with the ground truth. Such network recovery is achieved without requiring low-pass filtering of the time series ensembles prior to analysis, an fMRI preprocessing step that has courted controversy in recent years. Thus, we conclude our MCA framework can allow recovery and visualization of the underlying functionally connected networks in the brain on resting state fMRI.

A novel role for WAVE1 in controlling actin network growth rate and architecture

PubMed Central

Sweeney, Meredith O.; Collins, Agnieszka; Padrick, Shae B.; Goode, Bruce L.

2015-01-01

Branched actin filament networks in cells are assembled through the combined activities of Arp2/3 complex and different WASP/WAVE proteins. Here we used TIRF and electron microscopy to directly compare for the first time the assembly kinetics and architectures of actin filament networks produced by Arp2/3 complex and dimerized VCA regions of WAVE1, WAVE2, or N-WASP. WAVE1 produced strikingly different networks from WAVE2 or N-WASP, which comprised unexpectedly short filaments. Further analysis showed that the WAVE1-specific activity stemmed from an inhibitory effect on filament elongation both in the presence and absence of Arp2/3 complex, which was observed even at low stoichiometries of WAVE1 to actin monomers, precluding an effect from monomer sequestration. Using a series of VCA chimeras, we mapped the elongation inhibitory effects of WAVE1 to its WH2 (“V”) domain. Further, mutating a single conserved lysine residue potently disrupted WAVE1's inhibitory effects. Taken together, our results show that WAVE1 has unique activities independent of Arp2/3 complex that can govern both the growth rates and architectures of actin filament networks. Such activities may underlie previously observed differences between the cellular functions of WAVE1 and WAVE2. PMID:25473116

Task-dependent individual differences in prefrontal connectivity.

PubMed

Biswal, Bharat B; Eldreth, Dana A; Motes, Michael A; Rypma, Bart

2010-09-01

Recent advances in neuroimaging have permitted testing of hypotheses regarding the neural bases of individual differences, but this burgeoning literature has been characterized by inconsistent results. To test the hypothesis that differences in task demands could contribute to between-study variability in brain-behavior relationships, we had participants perform 2 tasks that varied in the extent of cognitive involvement. We examined connectivity between brain regions during a low-demand vigilance task and a higher-demand digit-symbol visual search task using Granger causality analysis (GCA). Our results showed 1) Significant differences in numbers of frontoparietal connections between low- and high-demand tasks 2) that GCA can detect activity changes that correspond with task-demand changes, and 3) faster participants showed more vigilance-related activity than slower participants, but less visual-search activity. These results suggest that relatively low-demand cognitive performance depends on spontaneous bidirectionally fluctuating network activity, whereas high-demand performance depends on a limited, unidirectional network. The nature of brain-behavior relationships may vary depending on the extent of cognitive demand. High-demand network activity may reflect the extent to which individuals require top-down executive guidance of behavior for successful task performance. Low-demand network activity may reflect task- and performance monitoring that minimizes executive requirements for guidance of behavior.

Task-Dependent Individual Differences in Prefrontal Connectivity

PubMed Central

Biswal, Bharat B.; Eldreth, Dana A.; Motes, Michael A.

2010-01-01

Recent advances in neuroimaging have permitted testing of hypotheses regarding the neural bases of individual differences, but this burgeoning literature has been characterized by inconsistent results. To test the hypothesis that differences in task demands could contribute to between-study variability in brain-behavior relationships, we had participants perform 2 tasks that varied in the extent of cognitive involvement. We examined connectivity between brain regions during a low-demand vigilance task and a higher-demand digit–symbol visual search task using Granger causality analysis (GCA). Our results showed 1) Significant differences in numbers of frontoparietal connections between low- and high-demand tasks 2) that GCA can detect activity changes that correspond with task-demand changes, and 3) faster participants showed more vigilance-related activity than slower participants, but less visual-search activity. These results suggest that relatively low-demand cognitive performance depends on spontaneous bidirectionally fluctuating network activity, whereas high-demand performance depends on a limited, unidirectional network. The nature of brain-behavior relationships may vary depending on the extent of cognitive demand. High-demand network activity may reflect the extent to which individuals require top-down executive guidance of behavior for successful task performance. Low-demand network activity may reflect task- and performance monitoring that minimizes executive requirements for guidance of behavior. PMID:20064942

Functional Proteomic Analysis of Signaling Networks and Response to Targeted Therapy

DTIC Science & Technology

2008-03-01

of biochemical networks. Trends Biochemical Sci 31: 284–291. 56. Blinov ML, Faeder JR, Goldstein B , Hlavacek WS (2006) A network model of early events...activation is dependent on the nature of connectivity of the two receptors to B -Raf and C-Raf, which form a partially incoherent bifan. The incoherent bifan...Wooster, R., Stratton, M. R., and Futreal, P. A. (2002) Mutations of the BRaf gene in human cancer. Nature 417, 949–954 11. Goydos, J. S., Mann, B

Analysis of Time-Dependent Brain Network on Active and MI Tasks for Chronic Stroke Patients

PubMed Central

Chang, Won Hyuk; Kim, Yun-Hee; Lee, Seong-Whan; Kwon, Gyu Hyun

2015-01-01

Several researchers have analyzed brain activities by investigating brain networks. However, there is a lack of the research on the temporal characteristics of the brain network during a stroke by EEG and the comparative studies between motor execution and imagery, which became known to have similar motor functions and pathways. In this study, we proposed the possibility of temporal characteristics on the brain networks of a stroke. We analyzed the temporal properties of the brain networks for nine chronic stroke patients by the active and motor imagery tasks by EEG. High beta band has a specific role in the brain network during motor tasks. In the high beta band, for the active task, there were significant characteristics of centrality and small-worldness on bilateral primary motor cortices at the initial motor execution. The degree centrality significantly increased on the contralateral primary motor cortex, and local efficiency increased on the ipsilateral primary motor cortex. These results indicate that the ipsilateral primary motor cortex constructed a powerful subnetwork by influencing the linked channels as compensatory effect, although the contralateral primary motor cortex organized an inefficient network by using the connected channels due to lesions. For the MI task, degree centrality and local efficiency significantly decreased on the somatosensory area at the initial motor imagery. Then, there were significant correlations between the properties of brain networks and motor function on the contralateral primary motor cortex and somatosensory area for each motor execution/imagery task. Our results represented that the active and MI tasks have different mechanisms of motor acts. Based on these results, we indicated the possibility of customized rehabilitation according to different motor tasks. We expect these results to help in the construction of the customized rehabilitation system depending on motor tasks by understanding temporal functional characteristics on brain network for a stroke. PMID:26656269

Visualization techniques for computer network defense

NASA Astrophysics Data System (ADS)

Beaver, Justin M.; Steed, Chad A.; Patton, Robert M.; Cui, Xiaohui; Schultz, Matthew

2011-06-01

Effective visual analysis of computer network defense (CND) information is challenging due to the volume and complexity of both the raw and analyzed network data. A typical CND is comprised of multiple niche intrusion detection tools, each of which performs network data analysis and produces a unique alerting output. The state-of-the-practice in the situational awareness of CND data is the prevalent use of custom-developed scripts by Information Technology (IT) professionals to retrieve, organize, and understand potential threat events. We propose a new visual analytics framework, called the Oak Ridge Cyber Analytics (ORCA) system, for CND data that allows an operator to interact with all detection tool outputs simultaneously. Aggregated alert events are presented in multiple coordinated views with timeline, cluster, and swarm model analysis displays. These displays are complemented with both supervised and semi-supervised machine learning classifiers. The intent of the visual analytics framework is to improve CND situational awareness, to enable an analyst to quickly navigate and analyze thousands of detected events, and to combine sophisticated data analysis techniques with interactive visualization such that patterns of anomalous activities may be more easily identified and investigated.

Treatment effect of methylphenidate on intrinsic functional brain network in medication-naïve ADHD children: A multivariate analysis.

PubMed

Yoo, Jae Hyun; Kim, Dohyun; Choi, Jeewook; Jeong, Bumseok

2018-04-01

Methylphenidate is a first-line therapeutic option for treating attention-deficit/hyperactivity disorder (ADHD); however, elicited changes on resting-state functional networks (RSFNs) are not well understood. This study investigated the treatment effect of methylphenidate using a variety of RSFN analyses and explored the collaborative influences of treatment-relevant RSFN changes in children with ADHD. Resting-state functional magnetic resonance imaging was acquired from 20 medication-naïve ADHD children before methylphenidate treatment and twelve weeks later. Changes in large-scale functional connectivity were defined using independent component analysis with dual regression and graph theoretical analysis. The amplitude of low frequency fluctuation (ALFF) was measured to investigate local spontaneous activity alteration. Finally, significant findings were recruited to random forest regression to identify the feature subset that best explains symptom improvement. After twelve weeks of methylphenidate administration, large-scale connectivity was increased between the left fronto-parietal RSFN and the left insula cortex and the right fronto-parietal and the brainstem, while the clustering coefficient (CC) of the global network and nodes, the left fronto-parietal, cerebellum, and occipital pole-visual network, were decreased. ALFF was increased in the bilateral superior parietal cortex and decreased in the right inferior fronto-temporal area. The subset of the local and large-scale RSFN changes, including widespread ALFF changes, the CC of the global network and the cerebellum, could explain the 27.1% variance of the ADHD Rating Scale and 13.72% of the Conner's Parent Rating Scale. Our multivariate approach suggests that the neural mechanism of methylphenidate treatment could be associated with alteration of spontaneous activity in the superior parietal cortex or widespread brain regions as well as functional segregation of the large-scale intrinsic functional network.

Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of Escherichia coli metabolism.

PubMed

Tabe-Bordbar, Shayan; Marashi, Sayed-Amir

2013-12-01

Elementary modes (EMs) are steady-state metabolic flux vectors with minimal set of active reactions. Each EM corresponds to a metabolic pathway. Therefore, studying EMs is helpful for analyzing the production of biotechnologically important metabolites. However, memory requirements for computing EMs may hamper their applicability as, in most genome-scale metabolic models, no EM can be computed due to running out of memory. In this study, we present a method for computing randomly sampled EMs. In this approach, a network reduction algorithm is used for EM computation, which is based on flux balance-based methods. We show that this approach can be used to recover the EMs in the medium- and genome-scale metabolic network models, while the EMs are sampled in an unbiased way. The applicability of such results is shown by computing “estimated” control-effective flux values in Escherichia coli metabolic network.

Review and Analysis of the EU Teacher-Related Policies and Activities

ERIC Educational Resources Information Center

Stéger, Csilla

2014-01-01

This article aims at raising awareness of the key role the EU already plays in matters of teacher policy. It takes stock of European teacher policy related documents and activities, such as relevant strategies, presidency priorities, Council Conclusions, Commission working documents, the activities of thematic working groups, of networks, of data…

Within-subject joint independent component analysis of simultaneous fMRI/ERP in an auditory oddball paradigm

PubMed Central

MANGALATHU-ARUMANA, J.; BEARDSLEY, S. A.; LIEBENTHAL, E.

2012-01-01

The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. This research aimed to determine the sensitivity and limitations of applying joint independent component analysis (jICA) within-subjects, for ERP and fMRI data collected simultaneously in a parametric auditory frequency oddball paradigm. In a group of 20 subjects, an increase in ERP peak amplitude ranging 1–8 μV in the time window of the P300 (350–700ms), and a correlated increase in fMRI signal in a network of regions including the right superior temporal and supramarginal gyri, was observed with the increase in deviant frequency difference. JICA of the same ERP and fMRI group data revealed activity in a similar network, albeit with stronger amplitude and larger extent. In addition, activity in the left pre- and post- central gyri, likely associated with right hand somato-motor response, was observed only with the jICA approach. Within-subject, the jICA approach revealed significantly stronger and more extensive activity in the brain regions associated with the auditory P300 than the P300 linear regression analysis. The results suggest that with the incorporation of spatial and temporal information from both imaging modalities, jICA may be a more sensitive method for extracting common sources of activity between ERP and fMRI. PMID:22377443

Development and psychometric testing of the clinical networks engagement tool

PubMed Central

Hecker, Kent G.; Rabatach, Leora; Noseworthy, Tom W.; White, Deborah E.

2017-01-01

Background Clinical networks are being used widely to facilitate large system transformation in healthcare, by engagement of stakeholders throughout the health system. However, there are no available instruments that measure engagement in these networks. Methods The study purpose was to develop and assess the measurement properties of a multiprofessional tool to measure engagement in clinical network initiatives. Based on components of the International Association of Public Participation Spectrum and expert panel review, we developed 40 items for testing. The draft instrument was distributed to 1,668 network stakeholders across different governance levels (leaders, members, support, frontline stakeholders) in 9 strategic clinical networks in Alberta (January to July 2014). With data from 424 completed surveys (25.4% response rate), descriptive statistics, exploratory and confirmatory factor analysis, Pearson correlations, linear regression, multivariate analysis, and Cronbach alpha were conducted to assess reliability and validity of the scores. Results Sixteen items were retained in the instrument. Exploratory factor analysis indicated a four-factor solution and accounted for 85.7% of the total variance in engagement with clinical network initiatives: global engagement, inform (provided with information), involve (worked together to address concerns), and empower (given final decision-making authority). All subscales demonstrated acceptable reliability (Cronbach alpha 0.87 to 0.99). Both the confirmatory factor analysis and regression analysis confirmed that inform, involve, and empower were all significant predictors of global engagement, with involve as the strongest predictor. Leaders had higher mean scores than frontline stakeholders, while members and support staff did not differ in mean scores. Conclusions This study provided foundational evidence for the use of this tool for assessing engagement in clinical networks. Further work is necessary to evaluate engagement in broader network functions and activities; to assess barriers and facilitators of engagement; and, to elucidate how the maturity of networks and other factors influence engagement. PMID:28350834

Active distribution network planning considering linearized system loss

NASA Astrophysics Data System (ADS)

Li, Xiao; Wang, Mingqiang; Xu, Hao

2018-02-01

In this paper, various distribution network planning techniques with DGs are reviewed, and a new distribution network planning method is proposed. It assumes that the location of DGs and the topology of the network are fixed. The proposed model optimizes the capacities of DG and the optimal distribution line capacity simultaneously by a cost/benefit analysis and the benefit is quantified by the reduction of the expected interruption cost. Besides, the network loss is explicitly analyzed in the paper. For simplicity, the network loss is appropriately simplified as a quadratic function of difference of voltage phase angle. Then it is further piecewise linearized. In this paper, a piecewise linearization technique with different segment lengths is proposed. To validate its effectiveness and superiority, the proposed distribution network planning model with elaborate linearization technique is tested on the IEEE 33-bus distribution network system.

Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines

NASA Astrophysics Data System (ADS)

Hirst, Jonathan D.; King, Ross D.; Sternberg, Michael J. E.

1994-08-01

Neural networks and inductive logic programming (ILP) have been compared to linear regression for modelling the QSAR of the inhibition of E. coli dihydrofolate reductase (DHFR) by 2,4-diamino-5-(substitured benzyl)pyrimidines, and, in the subsequent paper [Hirst, J.D., King, R.D. and Sternberg, M.J.E., J. Comput.-Aided Mol. Design, 8 (1994) 421], the inhibition of rodent DHFR by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazines. Cross-validation trials provide a statistically rigorous assessment of the predictive capabilities of the methods, with training and testing data selected randomly and all the methods developed using identical training data. For the ILP analysis, molecules are represented by attributes other than Hansch parameters. Neural networks and ILP perform better than linear regression using the attribute representation, but the difference is not statistically significant. The major benefit from the ILP analysis is the formulation of understandable rules relating the activity of the inhibitors to their chemical structure.

Genomic analysis of regulatory network dynamics reveals large topological changes

NASA Astrophysics Data System (ADS)

Luscombe, Nicholas M.; Madan Babu, M.; Yu, Haiyuan; Snyder, Michael; Teichmann, Sarah A.; Gerstein, Mark

2004-09-01

Network analysis has been applied widely, providing a unifying language to describe disparate systems ranging from social interactions to power grids. It has recently been used in molecular biology, but so far the resulting networks have only been analysed statically. Here we present the dynamics of a biological network on a genomic scale, by integrating transcriptional regulatory information and gene-expression data for multiple conditions in Saccharomyces cerevisiae. We develop an approach for the statistical analysis of network dynamics, called SANDY, combining well-known global topological measures, local motifs and newly derived statistics. We uncover large changes in underlying network architecture that are unexpected given current viewpoints and random simulations. In response to diverse stimuli, transcription factors alter their interactions to varying degrees, thereby rewiring the network. A few transcription factors serve as permanent hubs, but most act transiently only during certain conditions. By studying sub-network structures, we show that environmental responses facilitate fast signal propagation (for example, with short regulatory cascades), whereas the cell cycle and sporulation direct temporal progression through multiple stages (for example, with highly inter-connected transcription factors). Indeed, to drive the latter processes forward, phase-specific transcription factors inter-regulate serially, and ubiquitously active transcription factors layer above them in a two-tiered hierarchy. We anticipate that many of the concepts presented here-particularly the large-scale topological changes and hub transience-will apply to other biological networks, including complex sub-systems in higher eukaryotes.

Flexible modulation of network connectivity related to cognition in Alzheimer's disease.

PubMed

McLaren, Donald G; Sperling, Reisa A; Atri, Alireza

2014-10-15

Functional neuroimaging tools, such as fMRI methods, may elucidate the neural correlates of clinical, behavioral, and cognitive performance. Most functional imaging studies focus on regional task-related activity or resting state connectivity rather than how changes in functional connectivity across conditions and tasks are related to cognitive and behavioral performance. To investigate the promise of characterizing context-dependent connectivity-behavior relationships, this study applies the method of generalized psychophysiological interactions (gPPI) to assess the patterns of associative-memory-related fMRI hippocampal functional connectivity in Alzheimer's disease (AD) associated with performance on memory and other cognitively demanding neuropsychological tests and clinical measures. Twenty-four subjects with mild AD dementia (ages 54-82, nine females) participated in a face-name paired-associate encoding memory study. Generalized PPI analysis was used to estimate the connectivity between the hippocampus and the whole brain during encoding. The difference in hippocampal-whole brain connectivity between encoding novel and encoding repeated face-name pairs was used in multiple-regression analyses as an independent predictor for 10 behavioral, neuropsychological and clinical tests. The analysis revealed connectivity-behavior relationships that were distributed, dynamically overlapping, and task-specific within and across intrinsic networks; hippocampal-whole brain connectivity-behavior relationships were not isolated to single networks, but spanned multiple brain networks. Importantly, these spatially distributed performance patterns were unique for each measure. In general, out-of-network behavioral associations with encoding novel greater than repeated face-name pairs hippocampal-connectivity were observed in the default-mode network, while correlations with encoding repeated greater than novel face-name pairs hippocampal-connectivity were observed in the executive control network (p<0.05, cluster corrected). Psychophysiological interactions revealed significantly more extensive and robust associations between paired-associate encoding task-dependent hippocampal-whole brain connectivity and performance on memory and behavioral/clinical measures than previously revealed by standard activity-behavior analysis. Compared to resting state and task-activation methods, gPPI analyses may be more sensitive to reveal additional complementary information regarding subtle within- and between-network relations. The patterns of robust correlations between hippocampal-whole brain connectivity and behavioral measures identified here suggest that there are 'coordinated states' in the brain; that the dynamic range of these states is related to behavior and cognition; and that these states can be observed and quantified, even in individuals with mild AD. Copyright © 2014 Elsevier Inc. All rights reserved.

HRD Motif as the Central Hub of the Signaling Network for Activation Loop Autophosphorylation in Abl Kinase.

PubMed

La Sala, Giuseppina; Riccardi, Laura; Gaspari, Roberto; Cavalli, Andrea; Hantschel, Oliver; De Vivo, Marco

2016-11-08

A number of structural factors modulate the activity of Abelson (Abl) tyrosine kinase, whose deregulation is often related to oncogenic processes. First, only the open conformation of the Abl kinase domain's activation loop (A-loop) favors ATP binding to the catalytic cleft. In this regard, the trans-autophosphorylation of the Y412 residue, which is located along the A-loop, favors the stability of the open conformation, in turn enhancing Abl activity. Another key factor for full Abl activity is the formation of active conformations of the catalytic DFG motif in the Abl kinase domain. Furthermore, binding of the SH2 domain to the N-lobe of the Abl kinase was recently demonstrated to have a long-range allosteric effect on the stabilization of the A-loop open state. Intriguingly, these distinct structural factors imply a complex signal transmission network for controlling the A-loop's flexibility and conformational preference for optimal Abl function. However, the exact dynamical features of this signal transmission network structure remain unclear. Here, we report on microsecond-long molecular dynamics coupled with enhanced sampling simulations of multiple Abl model systems, in the presence or absence of the SH2 domain and with the DFG motif flipped in two ways (in or out conformation). Through comparative analysis, our simulations augment the interpretation of the existing Abl experimental data, revealing a dynamical network of interactions that interconnect SH2 domain binding with A-loop plasticity and Y412 autophosphorylation in Abl. This signaling network engages the DFG motif and, importantly, other conserved structural elements of the kinase domain, namely, the EPK-ELK H-bond network and the HRD motif. Our results show that the signal propagation for modulating the A-loop spatial localization is highly dependent on the HRD motif conformation, which thus acts as the central hub of this (allosteric) signaling network controlling Abl activation and function.

Combining Community Engagement and Scientific Approaches in Next-Generation Monitor Siting: The Case of the Imperial County Community Air Network.

PubMed

Wong, Michelle; Bejarano, Esther; Carvlin, Graeme; Fellows, Katie; King, Galatea; Lugo, Humberto; Jerrett, Michael; Meltzer, Dan; Northcross, Amanda; Olmedo, Luis; Seto, Edmund; Wilkie, Alexa; English, Paul

2018-03-15

Air pollution continues to be a global public health threat, and the expanding availability of small, low-cost air sensors has led to increased interest in both personal and crowd-sourced air monitoring. However, to date, few low-cost air monitoring networks have been developed with the scientific rigor or continuity needed to conduct public health surveillance and inform policy. In Imperial County, California, near the U.S./Mexico border, we used a collaborative, community-engaged process to develop a community air monitoring network that attains the scientific rigor required for research, while also achieving community priorities. By engaging community residents in the project design, monitor siting processes, data dissemination, and other key activities, the resulting air monitoring network data are relevant, trusted, understandable, and used by community residents. Integration of spatial analysis and air monitoring best practices into the network development process ensures that the data are reliable and appropriate for use in research activities. This combined approach results in a community air monitoring network that is better able to inform community residents, support research activities, guide public policy, and improve public health. Here we detail the monitor siting process and outline the advantages and challenges of this approach.

Combining Community Engagement and Scientific Approaches in Next-Generation Monitor Siting: The Case of the Imperial County Community Air Network

PubMed Central

Wong, Michelle; Bejarano, Esther; Carvlin, Graeme; King, Galatea; Lugo, Humberto; Jerrett, Michael; Northcross, Amanda; Olmedo, Luis; Seto, Edmund; Wilkie, Alexa; English, Paul

2018-01-01

Air pollution continues to be a global public health threat, and the expanding availability of small, low-cost air sensors has led to increased interest in both personal and crowd-sourced air monitoring. However, to date, few low-cost air monitoring networks have been developed with the scientific rigor or continuity needed to conduct public health surveillance and inform policy. In Imperial County, California, near the U.S./Mexico border, we used a collaborative, community-engaged process to develop a community air monitoring network that attains the scientific rigor required for research, while also achieving community priorities. By engaging community residents in the project design, monitor siting processes, data dissemination, and other key activities, the resulting air monitoring network data are relevant, trusted, understandable, and used by community residents. Integration of spatial analysis and air monitoring best practices into the network development process ensures that the data are reliable and appropriate for use in research activities. This combined approach results in a community air monitoring network that is better able to inform community residents, support research activities, guide public policy, and improve public health. Here we detail the monitor siting process and outline the advantages and challenges of this approach. PMID:29543726

Systems view of adipogenesis via novel omics-driven and tissue-specific activity scoring of network functional modules

NASA Astrophysics Data System (ADS)

Nassiri, Isar; Lombardo, Rosario; Lauria, Mario; Morine, Melissa J.; Moyseos, Petros; Varma, Vijayalakshmi; Nolen, Greg T.; Knox, Bridgett; Sloper, Daniel; Kaput, Jim; Priami, Corrado

2016-07-01

The investigation of the complex processes involved in cellular differentiation must be based on unbiased, high throughput data processing methods to identify relevant biological pathways. A number of bioinformatics tools are available that can generate lists of pathways ranked by statistical significance (i.e. by p-value), while ideally it would be desirable to functionally score the pathways relative to each other or to other interacting parts of the system or process. We describe a new computational method (Network Activity Score Finder - NASFinder) to identify tissue-specific, omics-determined sub-networks and the connections with their upstream regulator receptors to obtain a systems view of the differentiation of human adipocytes. Adipogenesis of human SBGS pre-adipocyte cells in vitro was monitored with a transcriptomic data set comprising six time points (0, 6, 48, 96, 192, 384 hours). To elucidate the mechanisms of adipogenesis, NASFinder was used to perform time-point analysis by comparing each time point against the control (0 h) and time-lapse analysis by comparing each time point with the previous one. NASFinder identified the coordinated activity of seemingly unrelated processes between each comparison, providing the first systems view of adipogenesis in culture. NASFinder has been implemented into a web-based, freely available resource associated with novel, easy to read visualization of omics data sets and network modules.

Variations in solar Lyman alpha irradiance on short time scales

NASA Astrophysics Data System (ADS)

Pap, J. M.

1992-10-01

Variations in solar UV irradiance at Lyman alpha are studied on short time scales (from days to months) after removing the long-term changes over the solar cycle. The SME/Lyman alpha irradiance is estimated from various solar indices using linear regression analysis. In order to study the nonlinear effects, Lyman alpha irradiance is modeled with a 5th-degree polynomial as well. It is shown that the full-disk equivalent width of the He line at 1083 nm, which is used as a proxy for the plages and active network, can best reproduce the changes observed in Lyman alpha. Approximately 72 percent of the solar-activity-related changes in Lyman alpha irradiance arise from plages and the network. The network contribution is estimated by the correlation analysis to be about 19 percent. It is shown that significant variability remains in Lyman alpha irradiance, with periods around 300, 27, and 13.5d, which is not explained by the solar activity indices. It is shown that the nonlinear effects cannot account for a significant part of the unexplained variation in Lyman alpha irradiance. Therefore, additional events (e.g., large-scale motions and/or a systematic difference in the area and intensity of the plages and network observed in the lines of Ca-K, He 1083, and Lyman alpha) may explain the discrepancies found between the observed and estimated irradiance values.

Variations in solar Lyman alpha irradiance on short time scales

NASA Technical Reports Server (NTRS)

Pap, J. M.

1992-01-01

Variations in solar UV irradiance at Lyman alpha are studied on short time scales (from days to months) after removing the long-term changes over the solar cycle. The SME/Lyman alpha irradiance is estimated from various solar indices using linear regression analysis. In order to study the nonlinear effects, Lyman alpha irradiance is modeled with a 5th-degree polynomial as well. It is shown that the full-disk equivalent width of the He line at 1083 nm, which is used as a proxy for the plages and active network, can best reproduce the changes observed in Lyman alpha. Approximately 72 percent of the solar-activity-related changes in Lyman alpha irradiance arise from plages and the network. The network contribution is estimated by the correlation analysis to be about 19 percent. It is shown that significant variability remains in Lyman alpha irradiance, with periods around 300, 27, and 13.5d, which is not explained by the solar activity indices. It is shown that the nonlinear effects cannot account for a significant part of the unexplained variation in Lyman alpha irradiance. Therefore, additional events (e.g., large-scale motions and/or a systematic difference in the area and intensity of the plages and network observed in the lines of Ca-K, He 1083, and Lyman alpha) may explain the discrepancies found between the observed and estimated irradiance values.

Delay-slope-dependent stability results of recurrent neural networks.

PubMed

Li, Tao; Zheng, Wei Xing; Lin, Chong

2011-12-01

By using the fact that the neuron activation functions are sector bounded and nondecreasing, this brief presents a new method, named the delay-slope-dependent method, for stability analysis of a class of recurrent neural networks with time-varying delays. This method includes more information on the slope of neuron activation functions and fewer matrix variables in the constructed Lyapunov-Krasovskii functional. Then some improved delay-dependent stability criteria with less computational burden and conservatism are obtained. Numerical examples are given to illustrate the effectiveness and the benefits of the proposed method.

Poly-dimensional network comparative analysis reveals the pure pharmacological mechanism of baicalin in the targeted network of mouse cerebral ischemia.

PubMed

Liu, Qiong; Liu, Jun; Wang, Pengqian; Zhang, Yingying; Li, Bing; Yu, Yanan; Dang, Haixia; Li, Haixia; Zhang, Xiaoxu; Wang, Zhong

2017-07-01

This study aimed to investigate the pure pharmacological mechanisms of baicalin/baicalein (BA) in the targeted network of mouse cerebral ischemia using a poly-dimensional network comparative analysis. Eighty mice with induced focal cerebral ischemia were randomly divided into four groups: BA, Concha Margaritifera (CM), vehicle and sham group. A poly-dimensional comparative analysis of the expression levels of 374 stroke-related genes in each of the four groups was performed using MetaCore. BA significantly reduced the ischemic infarct volume (P<0.05), whereas CM was ineffective. Two processes and 10 network nodes were shared between "BA vs CM" and vehicle, but there were no overlapping pathways. Two pathways, three processes and 12 network nodes overlapped in "BA vs CM" and BA. The pure pharmacological mechanism of BA resulted in targeting of pathways related to development, G-protein signaling, apoptosis, signal transduction and immunity. The biological processes affected by BA were primarily found to correlate with apoptotic, anti-apoptotic and neurophysiological processes. Three network nodes changed from up-regulation to down-regulation, while mitogen-activated protein kinase kinase 6 (MAP2K6, also known as MEK6) changed from down-regulation to up-regulation in "BA vs CM" and vehicle. The changed nodes were all related to cell death and development. The pure pharmacological mechanism of BA is related to immunity, apoptosis, development, cytoskeletal remodeling, transduction and neurophysiology, as ascertained using a poly-dimensional network comparative analysis. Copyright © 2017. Published by Elsevier B.V.

Social networks help to infer causality in the tumor microenvironment.

PubMed

Crespo, Isaac; Doucey, Marie-Agnès; Xenarios, Ioannis

2016-03-15

Networks have become a popular way to conceptualize a system of interacting elements, such as electronic circuits, social communication, metabolism or gene regulation. Network inference, analysis, and modeling techniques have been developed in different areas of science and technology, such as computer science, mathematics, physics, and biology, with an active interdisciplinary exchange of concepts and approaches. However, some concepts seem to belong to a specific field without a clear transferability to other domains. At the same time, it is increasingly recognized that within some biological systems--such as the tumor microenvironment--where different types of resident and infiltrating cells interact to carry out their functions, the complexity of the system demands a theoretical framework, such as statistical inference, graph analysis and dynamical models, in order to asses and study the information derived from high-throughput experimental technologies. In this article we propose to adopt and adapt the concepts of influence and investment from the world of social network analysis to biological problems, and in particular to apply this approach to infer causality in the tumor microenvironment. We showed that constructing a bidirectional network of influence between cell and cell communication molecules allowed us to determine the direction of inferred regulations at the expression level and correctly recapitulate cause-effect relationships described in literature. This work constitutes an example of a transfer of knowledge and concepts from the world of social network analysis to biomedical research, in particular to infer network causality in biological networks. This causality elucidation is essential to model the homeostatic response of biological systems to internal and external factors, such as environmental conditions, pathogens or treatments.

Modalities of Thinking: State and Trait Effects on Cross-Frequency Functional Independent Brain Networks.

PubMed

Milz, Patricia; Pascual-Marqui, Roberto D; Lehmann, Dietrich; Faber, Pascal L

2016-05-01

Functional states of the brain are constituted by the temporally attuned activity of spatially distributed neural networks. Such networks can be identified by independent component analysis (ICA) applied to frequency-dependent source-localized EEG data. This methodology allows the identification of networks at high temporal resolution in frequency bands of established location-specific physiological functions. EEG measurements are sensitive to neural activity changes in cortical areas of modality-specific processing. We tested effects of modality-specific processing on functional brain networks. Phasic modality-specific processing was induced via tasks (state effects) and tonic processing was assessed via modality-specific person parameters (trait effects). Modality-specific person parameters and 64-channel EEG were obtained from 70 male, right-handed students. Person parameters were obtained using cognitive style questionnaires, cognitive tests, and thinking modality self-reports. EEG was recorded during four conditions: spatial visualization, object visualization, verbalization, and resting. Twelve cross-frequency networks were extracted from source-localized EEG across six frequency bands using ICA. RMANOVAs, Pearson correlations, and path modelling examined effects of tasks and person parameters on networks. Results identified distinct state- and trait-dependent functional networks. State-dependent networks were characterized by decreased, trait-dependent networks by increased alpha activity in sub-regions of modality-specific pathways. Pathways of competing modalities showed opposing alpha changes. State- and trait-dependent alpha were associated with inhibitory and automated processing, respectively. Antagonistic alpha modulations in areas of competing modalities likely prevent intruding effects of modality-irrelevant processing. Considerable research suggested alpha modulations related to modality-specific states and traits. This study identified the distinct electrophysiological cortical frequency-dependent networks within which they operate.

Effective connectivity of facial expression network by using Granger causality analysis

NASA Astrophysics Data System (ADS)

Zhang, Hui; Li, Xiaoting

2013-10-01

Functional magnetic resonance imaging (fMRI) is an advanced non-invasive data acquisition technique to investigate the neural activity in human brain. In addition to localize the functional brain regions that is activated by specific cognitive task, fMRI can also be utilized to measure the task-related functional interactions among the active regions of interest (ROI) in the brain. Among the variety of analysis tools proposed for modeling the connectivity of brain regions, Granger causality analysis (GCA) measure the directions of information interactions by looking for the lagged effect among the brain regions. In this study, we use fMRI and Granger Causality analysis to investigate the effective connectivity of brain network induced by viewing several kinds of expressional faces. We focus on four kinds of facial expression stimuli: fearful, angry, happy and neutral faces. Five face selective regions of interest are localized and the effective connectivity within these regions is measured for the expressional faces. Our result based on 8 subjects showed that there is significant effective connectivity from STS to amygdala, from amygdala to OFA, aFFA and pFFA, from STS to aFFA and from pFFA to aFFA. This result suggested that there is an information flow from the STS to the amygdala when perusing expressional faces. This emotional expressional information flow that is conveyed by STS and amygdala, flow back to the face selective regions in occipital-temporal lobes, which constructed a emotional face processing network.

Visual target modulation of functional connectivity networks revealed by self-organizing group ICA.

PubMed

van de Ven, Vincent; Bledowski, Christoph; Prvulovic, David; Goebel, Rainer; Formisano, Elia; Di Salle, Francesco; Linden, David E J; Esposito, Fabrizio

2008-12-01

We applied a data-driven analysis based on self-organizing group independent component analysis (sogICA) to fMRI data from a three-stimulus visual oddball task. SogICA is particularly suited to the investigation of the underlying functional connectivity and does not rely on a predefined model of the experiment, which overcomes some of the limitations of hypothesis-driven analysis. Unlike most previous applications of ICA in functional imaging, our approach allows the analysis of the data at the group level, which is of particular interest in high order cognitive studies. SogICA is based on the hierarchical clustering of spatially similar independent components, derived from single subject decompositions. We identified four main clusters of components, centered on the posterior cingulate, bilateral insula, bilateral prefrontal cortex, and right posterior parietal and prefrontal cortex, consistently across all participants. Post hoc comparison of time courses revealed that insula, prefrontal cortex and right fronto-parietal components showed higher activity for targets than for distractors. Activation for distractors was higher in the posterior cingulate cortex, where deactivation was observed for targets. While our results conform to previous neuroimaging studies, they also complement conventional results by showing functional connectivity networks with unique contributions to the task that were consistent across subjects. SogICA can thus be used to probe functional networks of active cognitive tasks at the group-level and can provide additional insights to generate new hypotheses for further study. Copyright 2007 Wiley-Liss, Inc.