107,964 research outputs found
A complex network perspective on clinical science
Contemporary classification systems for mental disorders assume that abnormal behaviors are expressions of latent disease entities. An alternative to the latent disease model is the complex network approach. Instead of assuming that symptoms arise from an underlying disease entity, the complex network approach holds that disorders exist as systems of interrelated elements of a network. This approach also provides a framework for the understanding of therapeutic change. Depending on the structure of the network, change can occur abruptly once the network reaches a critical threshold (the tipping point). Homogeneous and highly connected networks often recover more slowly from local perturbations when the network approaches the tipping point, potentially making it possible to predict treatment change, relapse, and recovery. In this article, we discuss the complex network approach as an alternative to the latent disease model and its implications for classification, therapy, relapse, and recovery.R34 MH086668 - NIMH NIH HHS; R01 AT007257 - NCCIH NIH HHS; R21 MH101567 - NIMH NIH HHS; R34 MH099311 - NIMH NIH HHS; R21 MH102646 - NIMH NIH HHS; K23 MH100259 - NIMH NIH HHS; R01 MH099021 - NIMH NIH HH
Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: a methodological proof-of-concept study
There are large health, societal, and economic costs associated with attrition from psychological services. The recently emerged, innovative statistical tool of complex network analysis was used in the present proof-of-concept study to improve the prediction of attrition. Fifty-eight patients undergoing psychological treatment for mood or anxiety disorders were assessed using Ecological Momentary Assessments four times a day for two weeks before treatment (3,248 measurements). Multilevel vector autoregressive models were employed to compute dynamic symptom networks. Intake variables and network parameters (centrality measures) were used as predictors for dropout using machine-learning algorithms. Networks for patients differed significantly between completers and dropouts. Among intake variables, initial impairment and sex predicted dropout explaining 6% of the variance. The network analysis identified four additional predictors: Expected force of being excited, outstrength of experiencing social support, betweenness of feeling nervous, and instrength of being active. The final model with the two intake and four network variables explained 32% of variance in dropout and identified 47 out of 58 patients correctly. The findings indicate that patients’ dynamic network structures may improve the prediction of dropout. When implemented in routine care, such prediction models could identify patients at risk for attrition and inform personalized treatment recommendations.This work was supported by the German Research Foundation National Institute (DFG, Grant nos. LU 660/8-1 and LU 660/10-1 to W. Lutz). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. The corresponding author had access to all data in the study and had final responsibility for the decision to submit for publication. Dr. Hofmann receives financial support from the Alexander von Humboldt Foundation (as part of the Humboldt Prize), NIH/NCCIH (R01AT007257), NIH/NIMH (R01MH099021, U01MH108168), and the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative. (LU 660/8-1 - German Research Foundation National Institute (DFG); LU 660/10-1 - German Research Foundation National Institute (DFG); Alexander von Humboldt Foundation; R01AT007257 - NIH/NCCIH; R01MH099021 - NIH/NIMH; U01MH108168 - NIH/NIMH; James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative)Accepted manuscrip
The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations
In recent years scholars have built maps of science by connecting the
academic fields that cite each other, are cited together, or that cite a
similar literature. But since scholars cannot always publish in the fields they
cite, or that cite them, these science maps are only rough proxies for the
potential of a scholar, organization, or country, to enter a new academic
field. Here we use a large dataset of scholarly publications disambiguated at
the individual level to create a map of science-or research space-where links
connect pairs of fields based on the probability that an individual has
published in both of them. We find that the research space is a significantly
more accurate predictor of the fields that individuals and organizations will
enter in the future than citation based science maps. At the country level,
however, the research space and citations based science maps are equally
accurate. These findings show that data on career trajectories-the set of
fields that individuals have previously published in-provide more accurate
predictors of future research output for more focalized units-such as
individuals or organizations-than citation based science maps
The co-evolution of emotional well-being with weak and strong friendship ties
Social ties are strongly related to well-being. But what characterizes this
relationship? This study investigates social mechanisms explaining how social
ties affect well-being through social integration and social influence, and how
well-being affects social ties through social selection. We hypothesize that
highly integrated individuals - those with more extensive and dense friendship
networks - report higher emotional well-being than others. Moreover, emotional
well-being should be influenced by the well-being of close friends. Finally,
well-being should affect friendship selection when individuals prefer others
with higher levels of well-being, and others whose well-being is similar to
theirs. We test our hypotheses using longitudinal social network and well-being
data of 117 individuals living in a graduate housing community. The application
of a novel extension of Stochastic Actor-Oriented Models for ordered networks
(ordered SAOMs) allows us to detail and test our hypotheses for weak- and
strong-tied friendship networks simultaneously. Results do not support our
social integration and social influence hypotheses but provide evidence for
selection: individuals with higher emotional well-being tend to have more
strong-tied friends, and there are homophily processes regarding emotional
well-being in strong-tied networks. Our study highlights the two-directional
relationship between social ties and well-being, and demonstrates the
importance of considering different tie strengths for various social processes
Consensus Emerging from the Bottom-up: the Role of Cognitive Variables in Opinion Dynamics
The study of opinions e.g., their formation and change, and their effects
on our society by means of theoretical and numerical models has been one of
the main goals of sociophysics until now, but it is one of the defining topics
addressed by social psychology and complexity science. Despite the flourishing
of different models and theories, several key questions still remain
unanswered. The aim of this paper is to provide a cognitively grounded
computational model of opinions in which they are described as mental
representations and defined in terms of distinctive mental features. We also
define how these representations change dynamically through different
processes, describing the interplay between mental and social dynamics of
opinions. We present two versions of the model, one with discrete opinions
(voter model-like), and one with continuous ones (Deffuant-like). By means of
numerical simulations, we compare the behaviour of our cognitive model with the
classical sociophysical models, and we identify interesting differences in the
dynamics of consensus for each of the models considered.Comment: 14 pages, 8 figure
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
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