A network theory of mental disorders.
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TLDR
The network theory has direct implications for how to understand diagnosis and treatment, and suggests a clear agenda for future research in psychiatry and associated disciplines.About:
This article is published in World Psychiatry.The article was published on 2017-02-01 and is currently open access. It has received 1311 citations till now. The article focuses on the topics: Mind-blindness & Psychological intervention.read more
Citations
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A Tutorial on Regularized Partial Correlation Networks
Sacha Epskamp,Eiko I. Fried +1 more
TL;DR: In this article, the authors describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data, and demonstrate the method in an empirical example on post-traumatic stress disorder data.
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Moving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology:
TL;DR: Challenges to network theory may propel the network approach from its adolescence into adulthood and promises advances in understanding psychopathology both at the nomothetic and idiographic level.
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A tutorial on regularized partial correlation networks.
Sacha Epskamp,Eiko I. Fried +1 more
TL;DR: This tutorial introduces the reader to estimating the most popular network model for psychological data: the partial correlation network and describes how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data.
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What Do Centrality Measures Measure in Psychological Networks
Laura F. Bringmann,Timon Elmer,Sacha Epskamp,Robert Krause,David Schoch,Marieke Wichers,Johanna T. W. Wigman,Evelien Snippe +7 more
TL;DR: Critically examine several issues with the use of the most popular centrality indices in psychological networks: degree, betweenness, and closeness centrality, and conclude that betweenness and closness centrality seem especially unsuitable as measures of node importance.
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Bridge Centrality: A Network Approach to Understanding Comorbidity.
TL;DR: Four network statistics to identify bridge symptoms are developed: bridge strength, bridge betweenness, bridge closeness, and bridge expected influence, which are nonspecific to the type of network estimated, making them potentially useful in individual-level psychometric networks, group-level psychology networks, and networks outside the field of psychopathology such as social networks.
References
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The small world of psychopathology.
Denny Borsboom,Angélique O. J. Cramer,Verena D. Schmittmann,Sacha Epskamp,Lourens J. Waldorp +4 more
TL;DR: In the network model, mental disorders are inherently complex, which explains the limited successes of genetic, neuroscientific, and etiological approaches to unravel their causes.
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A new method for constructing networks from binary data
Claudia D. van Borkulo,Denny Borsboom,Sacha Epskamp,Tessa F. Blanken,Lynn Boschloo,Robert A. Schoevers,Lourens J. Waldorp +6 more
TL;DR: A method for assessing network structures from binary data based on Ising models, which combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network is presented.
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What kinds of things are psychiatric disorders
TL;DR: It is argued that psychiatric disorders are objectively grounded features of the causal structure of the mind/brain and a model first proposed for biological species, mechanistic property cluster (MPC) kinds, can provide a useful framework.
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What are 'good' depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis
TL;DR: The network perspective neither supports the standard psychometric notion that depression symptoms are equivalent indicators of MD, nor the common assumption that DSM symptoms of depression are of higher clinical relevance than non-DSM depression symptoms, which suggest the value of research focusing on especially central symptoms to increase the accuracy of predicting outcomes.
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Psychometric perspectives on diagnostic systems
TL;DR: The author concludes that the Psychometric analysis of diagnostic systems is not settled, and that these systems require deeper psychometric analysis than they currently receive.