43 research outputs found
Exponential Random Graph Modeling for Complex Brain Networks
Exponential random graph models (ERGMs), also known as p* models, have been
utilized extensively in the social science literature to study complex networks
and how their global structure depends on underlying structural components.
However, the literature on their use in biological networks (especially brain
networks) has remained sparse. Descriptive models based on a specific feature
of the graph (clustering coefficient, degree distribution, etc.) have dominated
connectivity research in neuroscience. Corresponding generative models have
been developed to reproduce one of these features. However, the complexity
inherent in whole-brain network data necessitates the development and use of
tools that allow the systematic exploration of several features simultaneously
and how they interact to form the global network architecture. ERGMs provide a
statistically principled approach to the assessment of how a set of interacting
local brain network features gives rise to the global structure. We illustrate
the utility of ERGMs for modeling, analyzing, and simulating complex
whole-brain networks with network data from normal subjects. We also provide a
foundation for the selection of important local features through the
implementation and assessment of three selection approaches: a traditional
p-value based backward selection approach, an information criterion approach
(AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF
approach serves as the best method given the scientific interest in being able
to capture and reproduce the structure of fitted brain networks
Hierarchy measure for complex networks
Nature, technology and society are full of complexity arising from the
intricate web of the interactions among the units of the related systems (e.g.,
proteins, computers, people). Consequently, one of the most successful recent
approaches to capturing the fundamental features of the structure and dynamics
of complex systems has been the investigation of the networks associated with
the above units (nodes) together with their relations (edges). Most complex
systems have an inherently hierarchical organization and, correspondingly, the
networks behind them also exhibit hierarchical features. Indeed, several papers
have been devoted to describing this essential aspect of networks, however,
without resulting in a widely accepted, converging concept concerning the
quantitative characterization of the level of their hierarchy. Here we develop
an approach and propose a quantity (measure) which is simple enough to be
widely applicable, reveals a number of universal features of the organization
of real-world networks and, as we demonstrate, is capable of capturing the
essential features of the structure and the degree of hierarchy in a complex
network. The measure we introduce is based on a generalization of the m-reach
centrality, which we first extend to directed/partially directed graphs. Then,
we define the global reaching centrality (GRC), which is the difference between
the maximum and the average value of the generalized reach centralities over
the network. We investigate the behavior of the GRC considering both a
synthetic model with an adjustable level of hierarchy and real networks.
Results for real networks show that our hierarchy measure is related to the
controllability of the given system. We also propose a visualization procedure
for large complex networks that can be used to obtain an overall qualitative
picture about the nature of their hierarchical structure.Comment: 29 pages, 9 figures, 4 table
Managing Relationship Decay Network, Gender, and Contextual Effects
Relationships are central to human life strategies and have crucial fitness consequences. Yet, at the same time, they incur significant maintenance costs that are rarely considered in either social psychological or evolutionary studies. Although many social psychological studies have explored their dynamics, these studies have typically focused on a small number of emotionally intense ties, whereas social networks in fact consist of a large number of ties that serve a variety of different functions. In this study, we examined how entire active personal networks changed over 18 months across a major life transition. Family relationships and friendships differed strikingly in this respect. The decline in friendship quality was mitigated by increased effort invested in the relationship, but with a striking gender difference: relationship decline was prevented most by increased contact frequency (talking together) for females but by doing more activities together in the case of males
The impact of social networks on knowledge transfer in long-term care facilities: Protocol for a study
<p>Abstract</p> <p>Background</p> <p>Social networks are theorized as significant influences in the innovation adoption and behavior change processes. Our understanding of how social networks operate within healthcare settings is limited. As a result, our ability to design optimal interventions that employ social networks as a method of fostering planned behavior change is also limited. Through this proposed project, we expect to contribute new knowledge about factors influencing uptake of knowledge translation interventions.</p> <p>Objectives</p> <p>Our specific aims include: To collect social network data among staff in two long-term care (LTC) facilities; to characterize social networks in these units; and to describe how social networks influence uptake and use of feedback reports.</p> <p>Methods and design</p> <p>In this prospective study, we will collect data on social networks in nursing units in two LTC facilities, and use social network analysis techniques to characterize and describe the networks. These data will be combined with data from a funded project to explore the impact of social networks on uptake and use of feedback reports. In this parent study, feedback reports using standardized resident assessment data are distributed on a monthly basis. Surveys are administered to assess report uptake. In the proposed project, we will collect data on social networks, analyzing the data using graphical and quantitative techniques. We will combine the social network data with survey data to assess the influence of social networks on uptake of feedback reports.</p> <p>Discussion</p> <p>This study will contribute to understanding mechanisms for knowledge sharing among staff on units to permit more efficient and effective intervention design. A growing number of studies in the social network literature suggest that social networks can be studied not only as influences on knowledge translation, but also as possible mechanisms for fostering knowledge translation. This study will contribute to building theory to design such interventions.</p
Exploration of Shared Genetic Architecture Between Subcortical Brain Volumes and Anorexia Nervosa
In MRI scans of patients with anorexia nervosa (AN), reductions in brain volume are often apparent. However, it is unknown whether such brain abnormalities are influenced by genetic determinants that partially overlap with those underlying AN. Here, we used a battery of methods (LD score regression, genetic risk scores, sign test, SNP effect concordance analysis, and Mendelian randomization) to investigate the genetic covariation between subcortical brain volumes and risk for AN based on summary measures retrieved from genome-wide association studies of regional brain volumes (ENIGMA consortium, n = 13,170) and genetic risk for AN (PGC-ED consortium, n = 14,477). Genetic correlations ranged from − 0.10 to 0.23 (all p > 0.05). There were some signs of an inverse concordance between greater thalamus volume and risk for AN (permuted p = 0.009, 95% CI: [0.005, 0.017]). A genetic variant in the vicinity of ZW10, a gene involved in cell division, and neurotransmitter and immune system relevant genes, in particular DRD2, was significantly associated with AN only after conditioning on its association with caudate volume (pFDR = 0.025). Another genetic variant linked to LRRC4C, important in axonal and synaptic development, reached significance after conditioning on hippocampal volume (pFDR = 0.021). In this comprehensive set of analyses and based on the largest available sample sizes to date, there was weak evidence for associations between risk for AN and risk for abnormal subcortical brain volumes at a global level (that is, common variant genetic architecture), but suggestive evidence for effects of single genetic markers. Highly powered multimodal brain- and disorder-related genome-wide studies are needed to further dissect the shared genetic influences on brain structure and risk for AN
FRAME DECAY, INFORMAL POWER, AND THE ESCALATION OF SOCIAL CONTROL IN A MANAGEMENT TEAM: A RELATIONAL SIGNALING PERSPECTIVE
In a study of conflict in organizations, Lindenberg's relational signaling theory is used to develop hypotheses on the impact of relationship strength, network embeddedness, and organizational change on social escalation. Social escalation is defined as the involvement of one or more third parties in a conflict. An empirical test is conducted with data on 67 conflicts involving 22 managers, gathered during three years of ethnographic fieldwork and a longitudinal network study in a management team of a German paper factory. Multilevel analysis indicates that strong ties between conflicting parties decrease the level of social escalation, whereas informal power advantage of one party increases the chances for social escalation. Both effects disappear over time. It is argued that the dissolving impact of relationships and networks is due to the disappearance of so-called solidarity frame-stabilizing activities in the firm. The results highlight the context-dependence of network effects and escalation processes. © 2003 Elsevier Science Ltd. All rights reserved
Lonely but not alone: Emotional isolation and social isolation as two distinct dimensions of loneliness in older people
The unidimensional nature of the De Jong-Gierveld Loneliness Scale is investigated. The internal properties of the scale scores were studied using item response theory, supplemented by an external validity study. In line with the theory of relational loneliness, the results stress the significance of distinguishing between emotional loneliness and social loneliness