Laura F. Bringmann

A Network Approach to Psychopathology: New Insights into Clinical Longitudinal Data

In the network approach to psychopathology, disorders are conceptualized as networks of mutually interacting symptoms (e.g., depressed mood) and transdiagnostic factors (e.g., rumination). This suggests that it is necessary to study how symptoms dynamically interact over time in a network architecture. In the present paper, we show how such an architecture can be constructed on the basis of time-series data obtained through Experience Sampling Methodology (ESM). The proposed methodology determines the parameters for the interaction between nodes in the network by estimating a multilevel vector autoregression (VAR) model on the data. The methodology allows combining between-subject and within-subject information in a multilevel framework. The resulting network architecture can subsequently be analyzed through network analysis techniques. In the present study, we apply the method to a set of items that assess mood-related factors. We show that the analysis generates a plausible and replicable network architecture, the structure of which is related to variables such as neuroticism; that is, for subjects who score high on neuroticism, worrying plays a more central role in the network. Implications and extensions of the methodology are discussed.

Emotion-Network Density in Major Depressive Disorder

Major depressive disorder (MDD) is a prevalent disorder involving disturbances in mood. There is still much to understand regarding precisely how emotions are disrupted in individuals with MDD. In this study, we used a network approach to examine the emotional disturbances underlying MDD. We hypothesized that compared with healthy control individuals, individuals diagnosed with MDD would be characterized by a denser emotion network, thereby indicating that their emotion system is more resistant to change. Indeed, results from a 7-day experience sampling study revealed that individuals with MDD had a denser overall emotion network than did healthy control individuals. Moreover, this difference was driven primarily by a denser negative, but not positive, network in MDD participants. These findings suggest that the disruption in emotions that characterizes depressed individuals stems from a negative emotion system that is resistant to change.

Perceived causal relations between anxiety, posttraumatic stress and depression: extension to moderation, mediation, and network analysis

Background Previous research demonstrates that posttraumatic memory reexperiencing, depression, anxiety, and guilt-shame are frequently co-occurring problems that may be causally related. Objectives The present study utilized Perceived Causal Relations (PCR) scaling in order to assess participants’ own attributions concerning whether and to what degree these co-occurring problems may be causally interrelated. Methods 288 young adults rated the frequency and respective PCR scores associating their symptoms of posttraumatic reexperiencing, depression, anxiety, and guilt-shame. Results PCR scores were found to moderate associations between the frequency of posttraumatic memory reexperiencing, depression, anxiety, and guilt-shame. Network analyses showed that the number of feedback loops between PCR scores was positively associated with symptom frequencies. Conclusion Results tentatively support the interpretation of PCR scores as moderators of the association between different psychological problems, and lend support to the hypothesis that increased symptom frequencies are observed in the presence of an increased number of causal feedback loops between symptoms. Additionally, a perceived causal role for the reexperiencing of traumatic memories in exacerbating emotional disturbance was identified.

Assessing Temporal Emotion Dynamics Using Networks

Multivariate psychological processes have recently been studied, visualized, and analyzed as networks. In this network approach, psychological constructs are represented as complex systems of interacting components. In addition to insightful visualization of dynamics, a network perspective leads to a new way of thinking about the nature of psychological phenomena by offering new tools for studying dynamical processes in psychology. In this article, we explain the rationale of the network approach, the associated methods and visualization, and illustrate it using an empirical example focusing on the relation between the daily fluctuations of emotions and neuroticism. The results suggest that individuals with high levels of neuroticism had a denser emotion network compared with their less neurotic peers. This effect is especially pronounced for the negative emotion network, which is in line with previous studies that found a denser network in depressed subjects than in healthy subjects. In sum, we show how the network approach may offer new tools for studying dynamical processes in psychology.

Changing Dynamics: Time-Varying Autoregressive Models Using Generalized Additive Modeling.

In psychology, the use of intensive longitudinal data has steeply increased during the past decade. As a result, studying temporal dependencies in such data with autoregressive modeling is becoming common practice. However, standard autoregressive models are often suboptimal as they assume that parameters are time-invariant. This is problematic if changing dynamics (e.g., changes in the temporal dependency of a process) govern the time series. Often a change in the process, such as emotional well-being during therapy, is the very reason why it is interesting and important to study psychological dynamics. As a result, there is a need for an easily applicable method for studying such nonstationary processes that result from changing dynamics. In this article we present such a tool: the semiparametric TV-AR model. We show with a simulation study and an empirical application that the TV-AR model can approximate nonstationary processes well if there are at least 100 time points available and no unknown abrupt changes in the data. Notably, no prior knowledge of the processes that drive change in the dynamic structure is necessary. We conclude that the TV-AR model has significant potential for studying changing dynamics in psychology.

Heating up the measurement debate: What psychologists can learn from the history of physics

Discussions of psychological measurement are largely disconnected from issues of measurement in the natural sciences. We show that there are interesting parallels and connections between the two, by focusing on a real and detailed example (temperature) from the history of science. More specifically, our novel approach is to study the issue of validity based on the history of measurement in physics, which will lead to three concrete points that are relevant for the validity debate in psychology. First of all, studying the causal mechanisms underlying the measurements can be crucial for evaluating whether the measurements are valid. Secondly, psychologists would benefit from focusing more on the robustness of measurements. Finally, we argue that it is possible to make good science based on (relatively) bad measurements, and that the explanatory success of science can contribute to justifying the validity of measurements.

Mental disorders as networks: some cautionary reflections on a promising approach

network perspective has helped the field to become aware of novel scientific approaches and tools and is stimulating a philosophical discussion on the matter of psychopathology: what is it and what should we look for in the search for the smallest elements that contribute to the development of psychopathology. As the popularity of the network approach grows and many researchers have started applying network techniques to their data, the question arises: where do we go from here? Below, we put forward some considerations for research in this field and we start with some notes of caution. As with all things that become popular, it is tempting to indiscriminately apply network techniques to data available in the field, simply because these are new and exciting techniques in the field. However, we should not give in to this temptation, but use network techniques only if they fit the specific research question that we have in mind. Questions for which one may want to use network techniques are, for example, those where one is specifically interested in assessing direct connections between variables, but only if that assumption is a valid one. Social networks with connections between people, are a good example hereof. Other examples are questions regarding the centrality of certain variables, or regarding causal dynamics between variables. Thus, many different sorts of research questions can be answered using network analytic techniques. However, not all of those research questions have relevance to the proposed theoretical ideas behind the network perspective as formulated earlier. Vice versa, there have been empirical studies that did not use specific network techniques, but, nevertheless, provided support for the network theory [10]. Therefore, it is important for this field to clearly distinguish between studies that support the network theory on the one Fried and colleagues [6] provided a clear overview regarding the theoretical background of the network perspective to psychopathology and empirical studies that use network techniques in the field. According to the network perspective, mental disorders arise as a result of a complex network of interacting symptoms and mental states. As summarized by Fried and colleagues [6], this perspective has gained popularity in the past years and opens new opportunities for understanding the concept and development of comorbidity and particularly for predicting the future course of symptoms. Furthermore, it has led to the hypothesis that such networks will provide insight into patient-specific psychological mechanisms underlying the development of mental disorders. The network perspective may, therefore, hold great promises for use in clinical practice [7]. For example, personal network structures could be used as an add-on diagnostic tool, which may optimize personalized targets for intervention. In short, the

Matching structural, effective, and functional connectivity: A comparison between structural equation modeling and ancestral graphs

In this study, we examined the accuracy of ancestral graphs (AGs) to study effective connectivity in the brain. Unlike most other methods that estimate effective connectivity, an AG is able to explicitly model missing brain regions in a network model. We compared AGs with the conventional structural equation models (SEM). We used both methods to estimate connection strengths between six regions of interest of the visual cortex based on functional magnetic resonance imaging data of a motion perception task. In order to examine which method is more accurate to estimate effective connectivity, we compared the connection strengths of the AG and SEM models with connection probabilities resulting from probabilistic tractography obtained from diffusion tensor images. This was done by correlating the connection strengths of the best fitting AG and SEM models with the connection probabilities of the probabilistic tractography models. We show that, in general, AGs result in more accurate models to estimate effective connectivity than SEM. The reason for this is that missing regions are taken into account when modeling with AG but not when modeling with SEM: AG can be used to explicitly test the assumption of missing regions. If the set of regions is complete, SEM and AG perform about equally well.

Don't blame the model : Reconsidering the network approach to psychopathology

The network approach to psychopathology is becoming increasingly popular. The motivation for this approach is to provide a replacement for the problematic common cause perspective and the associated latent variable model, where symptoms are taken to be mere effects of a common cause (the disorder itself). The idea is that the latent variable model is plausible for medical diseases, but unrealistic for mental disorders, which should rather be conceptualized as networks of directly interacting symptoms. We argue that this rationale for the network approach is misguided. Latent variable (or common cause) models are not inherently problematic, and there is not even a clear boundary where network models end and latent variable (or common cause) models begin. We also argue that focusing on this contrast has led to an unrealistic view of testing and finding support for the network approach, as well as an oversimplified picture of the relationship between medical diseases and mental disorders. As an alternative, we point out more essential contrasts, such as the contrast between dynamic and static modeling approaches that can provide a better framework for conceptualizing mental disorders. Finally, we discuss several topics and open problems that need to be addressed in order to make the network approach more concrete and to move the field of psychological network research forward. (PsycINFO Database Record

Modeling Nonstationary Emotion Dynamics in Dyads Using a Semiparametric Time-Varying Vector Autoregressive Model

Emotion dynamics often arise in an interpersonal context, such as a romantic relationship. As time passes, emotion dynamics are prone to change, leading to nonstationarity (e.g., time-lagged relations that differ across periods). Several models have been developed to account for nonstationarity in dynamics; for example, a time-varying dynamic factor model (Chow, Zu, Shifren, & Zhang, 2011). The use of such models, however, is limited because they are not implemented in standard software. We present a statistical data-driven model, the semiparametric time-varying vector autoregressive (TV-VAR) model. The TV-VAR model is based on wellstudied generalized additive models (GAM; Wood, 2006), implemented in the software R, and has well-functioning default settings, making it very user friendly. The TV-VAR can explicitly model changes in temporal dependency—for