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 circ*mstances in psychology and psychiatry research. Also, NCT can detect differences under various circ*mstances.
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.
| Original language | English |
|---|---|
| Qualification | Doctor of Philosophy |
| Awarding Institution |
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| Supervisors/Advisors |
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| Award date | 17-Jan-2018 |
| Place of Publication | [Groningen] |
| Publisher |
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| Print ISBNs | 978-94-034-0379-3 |
| Electronic ISBNs | 978-94-034-0378-6 |
| Publication status | Published - 2018 |
Access to Document
Title and contentsFinal publisher's version, 356 KB
Chapter 1Final publisher's version, 111 KB
Chapter 2Final publisher's version, 1.38 MB
Chapter 3Final publisher's version, 5.57 MB
Chapter 4Final publisher's version, 2.47 MB
Chapter 5Final publisher's version, 668 KB
Chapter 6Final publisher's version, 816 KB
Chapter 7Final publisher's version, 93.1 KB
Chapter 8Final publisher's version, 776 KB
Chapter 9Final publisher's version, 635 KB
Chapter 10Final publisher's version, 458 KB
Chapter 11Final publisher's version, 245 KB
Appendix AFinal publisher's version, 1.02 MB
Appendix BFinal publisher's version, 5.78 MB
Appendix CFinal publisher's version, 636 KB
Appendix DFinal publisher's version, 269 KB
Appendix EFinal publisher's version, 220 KB
BibliographyFinal publisher's version, 229 KB
Nederlandse samenvattingFinal publisher's version, 90.4 KB
Curriculum VitaeFinal publisher's version, 77.3 KB
List of publicationsFinal publisher's version, 137 KB
Dankwoord (acknowledgements)Final publisher's version, 89.2 KB
Complete thesisFinal publisher's version, 19.3 MB
PropositionsFinal publisher's version, 38.2 KB
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van Borkulo, C. D. (2018). Symptom network models in depression research: From methodological exploration to clinical application. [Thesis fully internal (DIV), University of Groningen]. University of Groningen.
van Borkulo, Claudia Debora. / Symptom network models in depression research : From methodological exploration to clinical application. [Groningen] : University of Groningen, 2018. 316 p.
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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{\textquoteright}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 circ*mstances in psychology and psychiatry research. Also, NCT can detect differences under various circ*mstances. Subsequently, she applied the methods to empirical data. She showed that the density of patients{\textquoteright} 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",
}
van Borkulo, CD 2018, 'Symptom network models in depression research: From methodological exploration to clinical application', Doctor of Philosophy, University of Groningen, [Groningen].
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: Thesis › Thesis fully internal (DIV)
TY - BOOK
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 circ*mstances in psychology and psychiatry research. Also, NCT can detect differences under various circ*mstances. 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 circ*mstances in psychology and psychiatry research. Also, NCT can detect differences under various circ*mstances. 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 -
van Borkulo CD. Symptom network models in depression research: From methodological exploration to clinical application. [Groningen]: University of Groningen, 2018. 316 p.
