A statistical model for brain networks inferred from large-scale electrophysiological signals

Network science has been extensively developed to characterize structural properties of complex systems, including brain networks inferred from neuroimaging data. As a result of the inference process, networks estimated from experimentally obtained biological data, represent one instance of a larger number of realizations with similar intrinsic topology. A modeling approach is therefore needed to support statistical inference on the bottom-up local connectivity mechanisms influencing the formation of the estimated brain networks. We adopted a statistical model based on exponential random graphs (ERGM) to reproduce brain networks, or connectomes, estimated by spectral coherence between high-density electroencephalographic (EEG) signals. We validated this approach in a dataset of 108 healthy subjects during eyes-open (EO) and eyes-closed (EC) resting-state conditions. Results showed that the tendency to form triangles and stars, reflecting clustering and node centrality, better explained the global properties of the EEG connectomes as compared to other combinations of graph metrics. Synthetic networks generated by this model configuration replicated the characteristic differences found in brain networks, with EO eliciting significantly higher segregation in the alpha frequency band (8-13 Hz) as compared to EC. Furthermore, the fitted ERGM parameter values provided complementary information showing that clustering connections are significantly more represented from EC to EO in the alpha range, but also in the beta band (14-29 Hz), which is known to play a crucial role in cortical processing of visual input and externally oriented attention. These findings support the current view of the brain functional segregation and integration in terms of modules and hubs, and provide a statistical approach to extract new information on the (re)organizational mechanisms in healthy and diseased brains.Comment: Due to the limitation "The abstract field cannot be longer than 1,920 characters", the abstract appearing here is slightly shorter than that in the PDF fil

backbone: An R Package for extracting the backbone of bipartite projections

Bipartite projections are used in a wide range of network contexts including politics (bill co-sponsorship), genetics (gene co-expression), economics (executive board co-membership), and innovation (patent co-authorship). However, because bipartite projections are always weighted graphs, which are inherently challenging to analyze and visualize, it is often useful to examine the 'backbone', an unweighted subgraph containing only the most significant edges. In this paper, we introduce the R package backbone for extracting the backbone of weighted bipartite projections, and use bill sponsorship data from the 114th session of the United States Senate to demonstrate its functionality

Using Affiliation Networks to Study the Determinants of Multilateral Research Cooperation Some empirical evidence from EU Framework Programs in biotechnology

This paper studies multilateral cooperation networks among organizations and work on a two-mode representation to study the decision to participate in a consortium. Our objective is to explain the underlying processes that give rise to multilateral collaboration networks. Particularly, we are interested in how heterogeneity in organizations' attributes plays a part and in the geographical dimension of this formation process. We use the data on project proposals submitted to the 7th Framework Program (FP) in the area of Life sciences, Biotechnology and Biochemistry for Sustainable Non-Food. We employ exponential random graph models (p* models) (Frank and Strauss, 1986 ; Wasserman and Pattison, 1996) with node attributes (Agneessens et al., 2004), and we make use of extensions for affiliation networks (Wang et al., 2009). These models do not only enable handling variability in consortium sizes but also relax the assumption on tie/triad independence. We obtained some preliminary results indicating institutional types as a source of heterogeneity affecting participation decisions. Also, these initial results point out that organizations take their potential partners' participations in other projects into account in giving their decision ; organizations located in the core European countries tend to participate in the same project ; the tendency to preserve the composition of a consortium across projects and the tendency of organizations with the same institutional type to co-participate are not significant

Key aspects of covert networks data collection:Problems, challenges, and opportunities

Data quality is considered to be among the greatest challenges in research on covert networks. This study identifies six aspects of network data collection, namely nodes, ties, attributes, levels, dynamics, and context. Addressing these aspects presents challenges, but also opens theoretical and methodological opportunities. Furthermore, specific issues arise in this research context, stemming from the use of secondary data and the problem of missing data. While each of the issues and challenges has some specific solution in the literature on organized crime and social networks, the main argument of this paper is to try and follow a more systematic and general solution to deal with these issues. To this end, three potentially synergistic and combinable techniques for data collection are proposed for each stage of data collection – biographies for data extraction, graph databases for data storage, and checklists for data reporting. The paper concludes with discussing the use of statistical models to analyse covert networks and the cultivation of relations within the research community and between researchers and practitioners

From local to central: a network analysis of who manages plant pest and disease outbreaks across scales

One of the key determinants of success in managing natural resources is “institutional fit,” i.e., how well the suite of required actions collectively match the scale of the environmental problem. The effective management of pest and pathogen threats to plants is a natural resource problem of particular economic, social, and environmental importance. Responses to incursions are managed by a network of decision makers and managers acting at different spatial and temporal scales. We applied novel network theoretical methods to assess the propensity of growers, local industry, local state government, and state and national government head offices to foster either within- or across-scale coordination during the successful 2001 Australian response to the outbreak of the fungal pathogen black sigatoka (Mycosphaerella fijiensis). We also reconstructed the response network to proxy what that network would look like today under the Australian government’s revised response system. We illustrate a structural move in the plant biosecurity response system from one that was locally driven to the current top-down system, in which the national government leads coordination of a highly partitioned engagement process. For biological incursions that spread widely across regions, nationally rather than locally managed responses may improve coordination of diverse tasks. However, in dealing with such challenges of institutional fit, local engagement will always be critical in deploying flexible and adaptive local responses based on a national system. The methods we propose detect where and how network structures foster cross-scale interactions, which will contribute to stronger empirical studies of cross-scale environmental governance

Exponential random graph model specifications for bipartite networks: a dependence hierarchy

In this paper, we review the development of dependence structures for exponential random graph models for bipartite networks, and propose a hierarchy of dependence structures within which different dependence assumptions may be located. Based on this hierarchy, we propose a new set of model specifications by including bipartite graph configurations involving more than four nodes. We discuss the theoretical significance of the various effects that the extended models afford, and illustrate application of this hierarchy of models to several bipartite networks related to the political mobilization in Brazil in the early 1990s (Mische, 2007)

New approaches in network data analysis

This thesis introduces two extensions to statistical approaches improving modeling and estimation in the field of network data analysis. The first contributing publication focuses on cross-sectional networks based on Markov graphs, whereas the second takes the evolution of networks with dynamical structure into account. Analyzing network data is challenging in terms of modeling and computation due to large and dependent data sets. The dissertation starts with an overview of network data in general and gives an introduction to the well-known model framework of exponential random graphs models with its dependence assumptions, estimation routines, challenges, and solution approaches. At the end of the introduction, main ideas of dynamic network models, the profile likelihood approach for multivariate counting processes for network data, and the analogy of the Cox proportional hazards and Poisson model with semiparametric estimation are presented. The first part of this work proposes an extension for sampling Markov graphs as a subclass of exponential random graph models in parallel to accelerate computation time in simulation-based routines. The estimation of network models, especially of large networks, is demanding and requires Markov chain Monte Carlo simulations. This publication recommends to exploit the conditional independence structure in networks to make use of parallel draws. This idea is applied to a large ego network of Facebook friendships, where an additional log transformation of network statistics accounts for degeneracy problems. This extension is implemented in the open source R package pergm, available on GitHub and a short introduction to the main functionalities is elaborated on in the thesis. The second part of this work focuses on dynamic networks. In comparison to cross-sectional networks from the first part, the development and application of longitudinal network data concentrates on modeling changes of relations. Therefore, a profile likelihood approach to model time-stamped event data is combined with a semiparametric approach including covariates built from network history. This flexible semiparametric approach is applicable to large networks because standard software can be used for estimation due to the analogy of the Cox proportional hazards and Poisson model with artificial data structure. This extended method is applied to patent collaboration data of patents submitted jointly by inventors with German residency between 2000 and 2013. Based on penalized smoothing techniques, we include time dependent network statistics and exogenous covariates to capture internal and external effects