• No results found

University of Groningen Symptom network models in depression research van Borkulo, Claudia Debora

N/A
N/A
Protected

Academic year: 2021

Share "University of Groningen Symptom network models in depression research van Borkulo, Claudia Debora"

Copied!
3
0
0

Bezig met laden.... (Bekijk nu de volledige tekst)

Hele tekst

(1)

University of Groningen

Symptom network models in depression research

van Borkulo, Claudia Debora

IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from

it. Please check the document version below.

Document Version

Publisher's PDF, also known as Version of record

Publication date:

2018

Link to publication in University of Groningen/UMCG research database

Citation for published version (APA):

van Borkulo, C. D. (2018). Symptom network models in depression research: From methodological

exploration to clinical application. University of Groningen.

Copyright

Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).

Take-down policy

If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum.

(2)

C

URRICULUM

V

ITAE

C

laudia Debora van Borkulo was born on March 16, 1971 in Amsterdam, the Netherlands. She graduated in 1989 from high school (VWO) at the Waterlant College in Amsterdam. Subsequently, she studied Chemistry at the VU University Amsterdam, with a specialization in Biochemistry and Molecu-lar Biology (with merit). After several years of working, she made a career switch and started studying Psychology in 2007 at the University of Amsterdam with a specialization in Psychological Methods. After completing the study (2012, cum

laude), she started her PhD research which is part of a broader collaboration

between the University Medical Center Groningen (Department of Psychiatry), the GGZ Friesland, and the Psychological Methods department of the University of Amsterdam under the supervision of Robert Schoevers and Denny Borsboom, and Lynn Boschloo and Lourens Waldorp as co-supervisors. During her PhD, she visited Prof. Richard McNally at Harvard Medical School in Boston and started several collaborations. Currently, Claudia works as a post-doctoral researcher at the Psychosystems group led by Denny Borsboom.

(3)

Referenties

GERELATEERDE DOCUMENTEN

Third, recent research, which used intraindividual analyses for network estimation, showed that patients with depression had a more densely connected intraindividual network of

Table 8.1 displays the results from univariable logistic regression analyses which showed that loss of interest/pleasure, depressed mood, fatigue and concentration problems (i.e.,

The contact process model can be viewed as an undirected network (see Figure 9.1 for a graphical representation) and is characterized by two independent Poisson processes: one

That is, for weakly connected symptom networks, negative external conditions (i.e., stressful events) lead to a gradual increase in symptoms, whereas for strongly connected

Methodological development can range from building on existing methods (e.g., including symptom thresholds in comparing network structures), to developing new estimation methods

To establish which of the variables in the data are neighbors of a given variable, and which are not, we used ` 1 - regularized logistic regression (Mein- shausen & Bühlmann,

It follows from Figure C.2, that the Fisher information variance is not a good estimate or the variance across all conditions (results for networks with 50% and 100% replacement

The connections in the latter network have the highest probability of being true positives: they are still present, while being estimated with a high value of γ (i.e., a high