The program of the Psychological Network Amsterdam Summer School is now online!
********** REGISTRATION OPENING SOON **********
Psychological Networks Amsterdam Summer School
Announcing a Five-Day Course Sequence:
First Psychological Networks Summer School
July 4 – July 8, 2016
University of Amsterdam
Nieuwe Achtergracht 166
1018 WV Amsterdam, The Netherlands
In many situations one is interested in constructing a network from different types of variables, for example age (continuous), type of medication (categorical) and number of diagnoses (count). In this blog post on R-bloggers.com, I describe how to construct such a network using the R-package “mgm”:
Our paper on comparing networks of two groups of patients with Major Depressive Disorder was published in JAMA Psychiatry (PDF).
In this paper, we investigated the association between baseline network structure of depression symptoms and the course of depression. We compared the baseline network structure of persisters (defined as patients with MDD at baseline and depressive symptomatology at 2-year follow-up) and remitters (patients with MDD at baseline without depressive symptomatology at 2-year follow-up). To compare network structures we used the first statistical test that directly compares connectivity of two networks (Network Comparison Test; NCT). While both groups have similar symptomatology at baseline, persisters have a more densely connected network compared to remitters. More specific symptom associations seem to be an important determinant of persistence of depression.
A Dutch newspaper (NRC Handelsblad, November 21st, 2015) published a piece about this paper (Link).
Our paper on the Causal Attitude Network (CAN) model was published in Psychological Review (PDF).
In the paper, we introduce the CAN model, which conceptualizes attitudes as networks consisting of interacting evaluative reactions, such as beliefs (e.g., judging a presidential candidate as competent and charismatic), feelings (e.g., feeling proudness and hope about the candidate), and behaviors (e.g., voting for the candidate). Interactions arise through direct causal connections between the evaluative reactions (e.g., feeling hopeful about the candidate because one judges her as competent and charismatic). The CAN model assumes that causal connections between evaluative reactions serve to heighten the consistency of the attitude and we argue that the Ising model’s axiom of energy expenditure reduction represents a formalized account of consistency pressure. Because individuals not only strive for consistency but also for accuracy, network representations of attitudes have to deal with the tradeoff between consistency and accuracy. This tradeoff is likely to lead to a small-world structure and we show that attitude networks indeed have a small-world structure. We also discuss the CAN model’s implication for attitude change and stability. Furthermore, we show that connectivity of attitude networks provides a formalized and parsimonious account of the dynamical differences between strong and weak attitudes.
Dalege, J., Borsboom, D., van Harreveld, F., van den Berg, H., Conner, M., & van der Maas, H. L. J. (2015). Toward a formalized account of attitudes: The Causal Attitude Network (CAN) model. Psychological Review. Advance online publication.
Our new paper “What are ‘good’ depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis” was published in the Journal of Affective Disorders (PDF).
In the paper we develop a novel theoretical and empirical framework to answer the question what a “good” symptom is. Traditionally, all depression symptoms are considered somewhat interchangeable indicators of depression, and it’s not clear what a good or clinically relevant symptom is. From the perspective of depression as a network of interacting symptoms, however, important symptoms are those with a large number of strong connections to other symptoms in the dynamic system (i.e. symptoms with a high centrality).
So we went ahead and estimated the network structure of 28 depression symptoms in about 3,500 depressed patients. We found that the 28 symptoms are intertwined with each other in complicated ways (it is not the case that all symptoms have roughly equally strong ties to each other), and symptoms differed substantially in their centrality values. Interestingly, both depression symptoms as listed in the Diagnostic and Statistical Manual of Mental Disorders (DSM) — as well as non-DSM symptoms such as anxiety — were among the most central symptoms.
When we compared the centrality of DSM and non-DSM symptoms, we found that, on average, DSM symptoms are not more central. At least from a network perspective, this raises substantial doubts about the validity of the depression symptoms featured in the DSM. Our findings suggest the value of research focusing on especially central symptoms to increase the accuracy of predicting outcomes such as the course of illness, probability of relapse, and treatment response.
Fried, E., Epskamp, S., Nesse, R. M., Tuerlinckx, F., & Borsboom, D. (in press). What are ‘good’ depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. Journal of Affective Disorders, 189, 314–320. doi:10.1016/j.jad.2015.09.005
Recently, our paper “The application of a network approach to health-related quality of life (HRQoL): introducing a new method for assessing hrqol in healthy adults and cancer patients” was published in Quality of Life Research.
The objective of this paper was to introduce a new approach for analyzing Health-Related Quality of Life (HRQoL) data, namely a network model.
The goal of this paper was to introduce the network approach in the analyzation of Health-Related Quality of Life (HRQoL) data. To show that the network approach can aid in the analysis of these kinds of data, we constructed networks of two samples: Dutch cancer patients (N = 485) and Dutch healthy adults (N = 1742). Both completed the 36-item Short Form Health Survey (SF-36), a commonly used instrument across different disease conditions and patient groups . In order to investigate the influence of diagnostic status, we added this binary variable to a third network that was constructed using both samples. The SF-36 consists of 8 sub-scales (domains). We constructed so-called “sub-scale” networks to gain more insight into the dynamics of HRQoL on domain level.
Results showed that the global structure of the SF-36 is dominant in all networks, supporting the validity of questionnaire’s subscales. Furthermore, we found that the network structure of the individual samples were similar with respect to the basic structure (item level), and that the network structure of the individual samples were highly similar not only with respect to the basic structure, but also with respect to the strength of the connections (subscale level). Lastly, centrality analyses revealed that maintaining a daily routine despite one’s physical health predicts HRQoL levels best.
We concluded that the network approach offers an alternative view on Healt-Related Quality of Life. We showed that the HRQoL network is, in its basic structure, similar across samples. Moreover, by using the network approach, we are able to identify important characteristics in the structure, which may inform treatment decisions.
Kossakowski, J. J., Epskamp, S., Kieffer, J. M., Borkulo, C. D. van, Rhemtulla, M., & Borsboom, D. (in press). The Application of a Network Approach to Health-Related Quality of Life: Introducing a New Method for Assessing HRQoL in Healthy Adults and Cancer Patients. Quality of Life Research. DOI: 10.1007/s11136-015-1127-z.
 Ware, J. E, Jr, & Sherbourne, C. D. (1992). The MOS 36-item short-form health survey (SF-36): I. Conceptual framework and item selection. Medical Care, 30, 473–483.
The European Research Council (ERC) has awarded a consolidator grant to Denny Borsboom to support the psychosystems project. The project, which is entitled “Psychosystems: Consolidating Network Approaches to Psychopathology”, is designed to further develop the theory and methodology of networks for mental disorders. The ERC will support the project for five years, allowing us to launch a number of new postdoc and PhD projects.
Our new network paper “From Loss to Loneliness: The Relationship Between Bereavement and Depressive Symptoms” was published in the Journal of Abnormal Psychology (PDF).
In the paper we examined 2 competing explanations concerning how spousal bereavement impacts on depression symptoms: a traditional latent variable explanation, in which loss triggers depression which then leads to symptoms; and a network explanation, in which bereavement directly affects particular depression symptoms which then activate other symptoms. We re-analyzed data from the CLOC study, a prospective cohort of 515 individuals, half of which would experienced spousal loss throughout the course of the study (the other half was queried as control group). We modeled the effect of partner loss on depressive symptoms either as an indirect effect through a latent variable, or as a direct effect in a network constructed through a causal search algorithm.
Overall, losing a partner impacted on very specific depression symptoms (e.g., feeling lonely and sad mood), but not on others (e.g., sleep problems). The effect of partner loss on these symptoms was not mediated by a latent variable. The network model indicated that bereavement mainly affected loneliness, which in turn activated other depressive symptoms. The findings support a growing body of literature showing that specific adverse life events differentially affect depressive symptomatology [1-3], and suggest that future studies should examine interventions that directly target such symptoms
» Fried, E. I., Bockting, C., Arjadi, R., Borsboom, D., Tuerlinckx, F., Cramer, A., Epskamp, S., Amshoff, M., Carr, D., & Stroebe, M. (2015). From Loss to Loneliness: The Relationship Between Bereavement and Depressive Symptoms. Journal of Abnormal Psychology.
 Keller, M. C., Neale, M. C., & Kendler, K. S. (2007). Association of different adverse life events with distinct patterns of depressive symptoms. The American Journal of Psychiatry, 164(10), 1521–9. doi:10.1176/appi.ajp.2007.06091564
 Cramer, A. O. J., Borsboom, D., Aggen, S. H., & Kendler, K. S. (2013). The pathoplasticity of dysphoric episodes: differential impact of stressful life events on the pattern of depressive symptom inter-correlations. Psychological Medicine, 42(5), 957–65. doi:10.1017/S003329171100211X
 Fried, E. I., Nesse, R. M., Guille, C., & Sen, S. (2015). The differential influence of life stress on individual symptoms of depression. Acta Psychiatrica Scandinavica, (6), 1–7. doi:10.1111/acps.12395
During the last couple of months, I have been working on building a web application (called NetworkApp) that enables users to upload their own data, construct networks and analyze them. Today, version 0.1 of this application has been released.
The NetworkApp has been build with Shiny: a web application framework for R. While R is used to build the web application, the user won’t see this and thus does not need to have any programming skills to use the NetworkApp.
To start using the NetworkApp, click here to access it.