Publication

Symptom network models in depression research: From methodological exploration to clinical application

van Borkulo, C. D., 2018, [Groningen]: University of Groningen. 316 p.

Research output: ThesisThesis fully internal (DIV)Academic

APA

van Borkulo, C. D. (2018). Symptom network models in depression research: From methodological exploration to clinical application. [Groningen]: University of Groningen.

Author

van Borkulo, Claudia Debora. / Symptom network models in depression research : From methodological exploration to clinical application. [Groningen] : University of Groningen, 2018. 316 p.

Harvard

van Borkulo, CD 2018, 'Symptom network models in depression research: From methodological exploration to clinical application', Doctor of Philosophy, University of Groningen, [Groningen].

Standard

Symptom network models in depression research : From methodological exploration to clinical application. / van Borkulo, Claudia Debora.

[Groningen] : University of Groningen, 2018. 316 p.

Research output: ThesisThesis fully internal (DIV)Academic

Vancouver

van Borkulo CD. Symptom network models in depression research: From methodological exploration to clinical application. [Groningen]: University of Groningen, 2018. 316 p.


BibTeX

@phdthesis{763caf88b1b043bbbccdbcc16e1ba7fa,
title = "Symptom network models in depression research: From methodological exploration to clinical application",
abstract = "According to the network perspective on psychopathology, mental disorders can be viewed as a network of causally interacting symptoms. With the network approach in mind, hypotheses can be formulated about psychopathology and treatment.The starting point of Claudia van Borkulo’s thesis is based on two central questions: “Why do some people develop a depressive episode, while others do not?” and “Why do some patients recover, while others do not?” She investigated these questions from a network perspective. To be able to do that, she first developed the required methodology: eLasso (implemented in R-package IsingFit) to infer the network structure from binary data and the Network Comparison Test (NCT; implemented in R-package NetworkComparisonTest) to statistically compare networks. In several validation studies, she showed that eLasso is a computational efficient method that performs well under various circumstances in psychology and psychiatry research. Also, NCT can detect differences under various circumstances. Subsequently, she applied the methods to empirical data. She showed that the density of patients’ symptom network was associated with the course of depression. Also, centrality of the depression symptoms of healthy individuals seems to have a predictive value for developing depression. Although these results pertain to group-level networks – thereby making it unclear what the results mean to an individual – they provide interesting starting points for future research.",
author = "{van Borkulo}, {Claudia Debora}",
year = "2018",
language = "English",
isbn = "978-94-034-0379-3",
publisher = "University of Groningen",
school = "University of Groningen",

}

RIS

TY - THES

T1 - Symptom network models in depression research

T2 - From methodological exploration to clinical application

AU - van Borkulo, Claudia Debora

PY - 2018

Y1 - 2018

N2 - According to the network perspective on psychopathology, mental disorders can be viewed as a network of causally interacting symptoms. With the network approach in mind, hypotheses can be formulated about psychopathology and treatment.The starting point of Claudia van Borkulo’s thesis is based on two central questions: “Why do some people develop a depressive episode, while others do not?” and “Why do some patients recover, while others do not?” She investigated these questions from a network perspective. To be able to do that, she first developed the required methodology: eLasso (implemented in R-package IsingFit) to infer the network structure from binary data and the Network Comparison Test (NCT; implemented in R-package NetworkComparisonTest) to statistically compare networks. In several validation studies, she showed that eLasso is a computational efficient method that performs well under various circumstances in psychology and psychiatry research. Also, NCT can detect differences under various circumstances. Subsequently, she applied the methods to empirical data. She showed that the density of patients’ symptom network was associated with the course of depression. Also, centrality of the depression symptoms of healthy individuals seems to have a predictive value for developing depression. Although these results pertain to group-level networks – thereby making it unclear what the results mean to an individual – they provide interesting starting points for future research.

AB - According to the network perspective on psychopathology, mental disorders can be viewed as a network of causally interacting symptoms. With the network approach in mind, hypotheses can be formulated about psychopathology and treatment.The starting point of Claudia van Borkulo’s thesis is based on two central questions: “Why do some people develop a depressive episode, while others do not?” and “Why do some patients recover, while others do not?” She investigated these questions from a network perspective. To be able to do that, she first developed the required methodology: eLasso (implemented in R-package IsingFit) to infer the network structure from binary data and the Network Comparison Test (NCT; implemented in R-package NetworkComparisonTest) to statistically compare networks. In several validation studies, she showed that eLasso is a computational efficient method that performs well under various circumstances in psychology and psychiatry research. Also, NCT can detect differences under various circumstances. Subsequently, she applied the methods to empirical data. She showed that the density of patients’ symptom network was associated with the course of depression. Also, centrality of the depression symptoms of healthy individuals seems to have a predictive value for developing depression. Although these results pertain to group-level networks – thereby making it unclear what the results mean to an individual – they provide interesting starting points for future research.

M3 - Thesis fully internal (DIV)

SN - 978-94-034-0379-3

PB - University of Groningen

CY - [Groningen]

ER -

ID: 52735610