A Review of Explicit and Implicit Assumptions When Providing Personalized Feedback Based on Self-Report EMA Data

Ecological Momentary Assessment (EMA) in which participants report on their moment-to-moment experiences in their natural environment, is a hot topic. An emerging field in clinical psychology based on either EMA, or what we term Ecological Retrospective Assessment (ERA) as it requires retrospectivity, is the field of personalized feedback. In this field, EMA/ERA-data-driven summaries are presented to participants with the goal of promoting their insight in their experiences. Underlying this procedure are some fundamental assumptions about (i) the relation between true moment-to-moment experiences and retrospective evaluations of those experiences, (ii) the translation of these experiences and evaluations to different types of data, (iii) the comparison of these different types of data, and (iv) the impact of a summary of moment-to-moment experiences on retrospective evaluations of those experiences. We argue that these assumptions deserve further exploration, in order to create a strong evidence-based foundation for the personalized feedback procedure.

Investigating the Utility of Fixed-margin Sampling in Network Psychometrics

Steinley, Hoffman, Brusco, and Sher (2017) proposed a new method for evaluating the performance of psychological network models: fixed-margin sampling. The authors investigated LASSO regularized Ising models (eLasso) by generating random datasets with the same margins as the original binary dataset, and concluded that many estimated eLasso parameters are not distinguishable from those that would be expected if the data were generated by chance. We argue that fixed-margin sampling cannot be used for this purpose, as it generates data under a particular null-hypothesis: a unidimensional factor model with interchangeable indicators (i.e., the Rasch model). We show this by discussing relevant psychometric literature and by performing simulation studies. Results indicate that while eLasso correctly estimated network models and estimated almost no edges due to chance, fixed-margin sampling performed poorly in classifying true effects as "interesting" (Steinley et al. 2017, p. 1004). Further simulation studies indicate that fixed-margin sampling offers a powerful method for highlighting local misfit from the Rasch model, but performs only moderately in identifying global departures from the Rasch model. We conclude that fixed-margin sampling is not up to the task of assessing if results from estimated Ising models or other multivariate psychometric models are due to chance.

Exploring the interconnectedness of fatigue, depression, anxiety and potential risk and protective factors in cancer patients: a network approach

Researchers have extensively studied fatigue, depression and anxiety in cancer patients. Several risk and protective factors have been identified for these symptoms. As most studies address these constructs, independently from other symptoms and potential risk and protective factors, more insight into the complex relationships among these constructs is needed. This study used the multivariate network approach to gain a better understanding of how patients' symptoms and risk and protective factors (i.e. physical symptoms, social withdrawal, illness cognitions, goal adjustment and partner support) are interconnected. We used cross-sectional data from a sample of cancer patients seeking psychological care (n = 342). Using network modelling, the relationships among symptoms of fatigue, depression and anxiety, and potential risk and protective factors were explored. Additionally, centrality (i.e. the number and strength of connections of a construct) and stability of the network were explored. Among risk factors, the relationship of helplessness and physical symptoms with fatigue stood out as they were stronger than most other connections in the network. Among protective factors, illness acceptance was most centrally embedded within the network, indicating it had more and stronger connections than most other variables in the network. The network identified key connections with risk factors (helplessness, physical symptoms) and a key protective factor (acceptance) at the group level. Longitudinal studies should explore these risk and protective factors in individual dynamic networks to further investigate their causal role and the extent to which such networks can inform us on what treatment would be most suitable for the individual cancer patient.

The Network Structure of Personality Pathology in Adolescence With the 100-Item Personality Inventory for DSM-5 Short-Form (PID-5-SF)

There is currently a lack of understanding of the structure of personality disorder (PD) trait facets. The network approach may be useful in providing additional insights, uncovering the unique association of each PD trait facet with every other facet. A unique feature of network analysis is centrality, which indicates the importance of the role a trait facet plays in the context of other trait facets. Using data from 1,940 community Dutch adolescents, we applied network analysis to the 25 trait facets from the 100-item Personality Inventory for DSM-5 Short-Form (PID-5-SF) to explore their associations. We found that some trait facets only seem to be core indicators of their pre-ordained domains, whereas we observed that other trait facets were strongly associated with trait facets outside of their hypothesized domains. Importantly, anxiousness and callousness were identified as highly central facets, being uniquely associated with many other trait facets. Future longitudinal network studies could therefore further examine the possibility of anxiousness and callousness as risk marker trait facets among other PD trait facets.

A Network Approach to Studying the Associations Between Posttraumatic Stress Disorder Symptoms and Dissociative Experiences

In recent years, there has been a growing recognition of a dissociative subtype of posttraumatic stress disorder (D-PTSD), characterized by experiences of depersonalization (DP) and derealization (DR), among individuals with PTSD. Little is known, however, about how experiences of DP and/or DR are associated with the experience of other PTSD symptoms. The central aim of the present paper was to explore the associations among DP, DR, and other PTSD symptoms by means of a network analysis of cross-sectional data for 557 participants whose overall self-reported PTSD symptom severity warranted a probable PTSD diagnosis. Three notable findings emerged: (a) a strong association between DP and DR, (b) the identification of DP as the most central symptom in the network, and (c) the discovery that clusters of symptoms in the network were roughly consistent with DSM-5 PTSD criteria. We discuss these findings in light of some considerations, including the nature of our sample and the limits of interpreting cross-sectional network models.

The antidepressant standoff: why it continues and how to resolve it

BACKGROUND

Antidepressant medications (ADMs) are widely used and long-term use is increasing. Given this extensive use and recommendation of ADMs in guidelines, one would expect ADMs to be universally considered effective. Surprisingly, that is not the case; fierce debate on their benefits and harms continues. This editorial seeks to understand why the controversy continues and how consensus can be achieved.

METHODS

'Position' paper. Critical analysis and synthesis of relevant literature.

RESULTS

Advocates point at ADMs impressive effect size (number needed to treat, NNT = 6-8) in acute phase treatment and continuation/maintenance ADM treatment prevention relapse/recurrence in acute phase ADM responders (NNT = 3-4). Critics point at the limited clinically significant surplus value of ADMs relative to placebo and argue that effectiveness is overstated. We identified multiple factors that fuel the controversy: certainty of evidence is low to moderate; modest efficacy on top of strong placebo effects allows critics to focus on small net efficacy and advocates on large gross efficacy; ADM withdrawal symptoms masquerade as relapse/recurrence; lack of association between ADM treatment and long-term outcome in observational databases. Similar problems affect psychological treatments as well, but less so. We recommend four approaches to resolve the controversy: (1) placebo-controlled trials with relevant long-term outcome assessments, (2) inventive analyses of observational databases, (3) patient cohort studies including effect moderators to improve personalized treatment, and (4) psychological treatments as universal first-line treatment step.

CONCLUSIONS

Given the public health significance of depression and increased long-term ADM usage, new approaches are needed to resolve the controversy.

[Antidepressants are frequently prescribed but still critized; a perspective on causes and solutions]

From around 1980, antidepressants (ad) have increasingly been prescribed, for longer periods of time, especially selective serotonin reuptake inhibitors (ssris). Paradoxically, their effectiveness is still doubted, especially outside the psychiatric profession.<br/> AIM: To explain increase and offer a perspective on causes and solutions, and to indicate how to reach consensus.<br/> METHOD: Position paper with critical analysis and synthesis of relevant literature.<br/> RESULTS: The rise in AD prescriptions results from: 1. increased safety and ease of prescribing, 2. increased presentation and recognition of depression in primary care, 3. extension of indication criteria, 4. effective marketing strategies, and 5. effectiveness in acute phase (aad) and of relapse/recurrence prevention in continuation/maintenance phases (coad).Critics point to: 1. low added value of aad relative to placebo, 2. many drop-outs and non-responders, 3. relapse/recurrence prevention with coad works only for responders to aad, 4. relapse/recurrence after AD discontinuation often involves withdrawal symptoms, and 5. publication bias, selective reporting, selective patient selection, and suboptimal blinding, resulting in overestimated effectiveness and underestimated disadvantages.Factors that keep fueling the controversy are: 1. critics stress the net effectiveness of AD whereas proponents point at gross effectiveness which includes spontaneous recovery and placebo effect; 2. persistence of distrust in industry-funded rcts; 3. ideological positions, reinforced by conflicts of interest and selective citations; 4. lack of rcts with relevant long-term outcome measurements.<br/> CONCLUSION: Although consensus is difficult to achieve given the ideological component, there are options. Three factors are critically important: confer to establish which data convince the opposition, response prediction (what works for whom), and rcts with long-term functional outcomes.

Where are the breaks in translation from theory to clinical practice (and back) in addressing depression? An empirical graph-theoretic approach

BACKGROUND

Research in depression has progressed rapidly over the past four decades. Yet depression rates are not subsiding and treatment success is not improving. We examine the extent to which the gap between science and practice is associated with the level of integration in how depression is considered in research and stakeholder-relevant documents.

METHODS

We used a network-science perspective to analyze similar uses of depression relevant terms in the Google News corpus (approximately 1 billion words) and the Web of Science database (120 000 documents).

RESULTS

These analyses yielded consistent pictures of insular modules associated with: (1) patient/providers, (2) academics, and (3) industry. Within academia insular modules associated with psychology, general medical, and psychiatry/neuroscience/biology were also detected.

CONCLUSIONS

These analyses suggest that the domain of depression is fragmented, and that advancements of relevance to one stakeholder group (academics, industry, or patients) may not translate to the others. We consider potential causes and associated responses to this fragmentation that could help to unify and advance translation from research on depression to the clinic, largely involving harmonizing employed language, bridging conceptual domains, and increasing communication across stakeholder groups.

A cross-sectional and longitudinal network analysis approach to understanding connections among social anxiety components in youth

As proposed in a prominent developmental model, social anxiety has different manifestations: social fear, shy temperament, anxious cognitions, and avoidance of social situations. Drawing from this model, we used the network approach to psychopathology to gain a detailed understanding of specific social anxiety components and their associations. The current article investigated (a) how social anxiety components are interconnected within a network, and (b) the consistency of the network over time, in a community sample of children and adolescents. Data from 3 waves of a longitudinal study were used. At Time 1 (T1) the total sample comprised 331 participants (M_age = 13.34 years); at Time 3 (T3) there were 236 participants (M_age = 17.48 years). Social anxiety components were assessed with self-report questionnaires. Networks of 15 nodes (i.e., components) were estimated. Network analysis of T1 components revealed 4 communities: cognitive, social-emotional, avoidance of performance, and avoidance of interaction situations. There were no direct connections between the cognitive and behavioral communities; social-emotional nodes appeared to act as bridge components between the 2 communities. A similar pattern of component associations and communities was found in the T2 and T3 networks, and the longitudinal network incorporating node change trajectories. Networks were estimated on group-level observational data and conclusions about cause-effect relationships are tentative. Although the sample size decreased across the 3 waves, the reliability of parameter estimates were minimally affected. Findings attest to the potential value of applying the network approach to investigate the pattern of associations among social anxiety components in youth. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Deconstructing and Reconstructing Resilience: A Dynamic Network Approach

Resilience is still often viewed as a unitary personality construct that, as a kind of antinosological entity, protects individuals against stress-related mental problems. However, increasing evidence indicates that maintaining mental health in the face of adversity results from complex and dynamic processes of adaptation to stressors that involve the activation of several separable protective factors. Such resilience factors can reside at biological, psychological, and social levels and may include stable predispositions (such as genotype or personality traits) and malleable properties, skills, capacities, or external circumstances (such as gene-expression patterns, emotion-regulation abilities, appraisal styles, or social support). We abandon the notion of resilience as an entity here. Starting from a conceptualization of psychiatric disorders as dynamic networks of interacting symptoms that may be driven by stressors into stable maladaptive states of disease, we deconstruct the maintenance of mental health during stressor exposure into time-variant dampening influences of resilience factors onto these symptom networks. Resilience factors are separate additional network nodes that weaken symptom-symptom interconnections or symptom autoconnections, thereby preventing maladaptive system transitions. We argue that these hybrid symptom-and-resilience-factor networks provide a promising new way of unraveling the complex dynamics of mental health.

Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections

Recent literature has introduced (a) the network perspective to psychology and (b) collection of time series data to capture symptom fluctuations and other time varying factors in daily life. Combining these trends allows for the estimation of intraindividual network structures. We argue that these networks can be directly applied in clinical research and practice as hypothesis generating structures. Two networks can be computed: a temporal network, in which one investigates if symptoms (or other relevant variables) predict one another over time, and a contemporaneous network, in which one investigates if symptoms predict one another in the same window of measurement. The contemporaneous network is a partial correlation network, which is emerging in the analysis of cross-sectional data but is not yet utilized in the analysis of time series data. We explain the importance of partial correlation networks and exemplify the network structures on time series data of a psychiatric patient.

The role of stabilizing and communicating symptoms given overlapping communities in psychopathology networks

Network theory, as a theoretical and methodological framework, is energizing many research fields, among which clinical psychology and psychiatry. Fundamental to the network theory of psychopathology is the role of specific symptoms and their interactions. Current statistical tools, however, fail to fully capture this constitutional property. We propose community detection tools as a means to evaluate the complex network structure of psychopathology, free from its original boundaries of distinct disorders. Unique to this approach is that symptoms can belong to multiple communities. Using a large community sample and spanning a broad range of symptoms (Symptom Checklist-90-Revised), we identified 18 communities of interconnected symptoms. The differential role of symptoms within and between communities offers a framework to study the clinical concepts of comorbidity, heterogeneity and hallmark symptoms. Symptoms with many and strong connections within a community, defined as stabilizing symptoms, could be thought of as the core of a community, whereas symptoms that belong to multiple communities, defined as communicating symptoms, facilitate the communication between problem areas. We propose that defining symptoms on their stabilizing and/or communicating role within and across communities accelerates our understanding of these clinical phenomena, central to research and treatment of psychopathology.

False alarm? A comprehensive reanalysis of "Evidence that psychopathology symptom networks have limited replicability" by Forbes, Wright, Markon, and Krueger (2017)

Forbes, Wright, Markon, and Krueger (2017) stated that "psychopathology networks have limited replicability" (p. 1011) and that "popular network analysis methods produce unreliable results" (p. 1011). These conclusions are based on an assessment of the replicability of four different network models for symptoms of major depression and generalized anxiety across two samples; in addition, Forbes et al. analyzed the stability of the network models within the samples using split-halves. Our reanalysis of the same data with the same methods led to results directly opposed to theirs: All network models replicated very well across the two data sets and across the split-halves. We trace the differences between Forbes et al.'s results and our own to the fact that they did not appear to accurately implement all network models and used debatable metrics to assess replicability. In particular, they deviated from existing estimation routines for relative importance networks, did not acknowledge the fact that the skip structure used in the interviews strongly distorted correlations between symptoms, and incorrectly assumed that network structures and metrics should be the same not only across the different samples but also across the different network models used. In addition to a comprehensive reanalysis of the data, we end with a discussion of best practices concerning future research into the replicability of psychometric networks. (PsycINFO Database Record

Moving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology

Since the introduction of mental disorders as networks of causally interacting symptoms, this novel framework has received considerable attention. The past years have resulted in over 40 scientific publications and numerous conference symposia and workshops. Now is an excellent moment to take stock of the network approach: What are its most fundamental challenges, and what are potential ways forward in addressing them? After a brief conceptual introduction, we first discuss challenges to network theory: (1) What is the validity of the network approach beyond some commonly investigated disorders such as major depression? (2) How do we best define psychopathological networks and their constituent elements? And (3) how can we gain a better understanding of the causal nature and real-life underpinnings of associations among symptoms? Next, after a short technical introduction to network modeling, we discuss challenges to network methodology: (4) heterogeneity of samples studied with network analytic models, and (5) a lurking replicability crisis in this strongly data-driven and exploratory field. Addressing these challenges may propel the network approach from its adolescence into adulthood and promises advances in understanding psychopathology both at the nomothetic and idiographic level.

Mental disorders as networks of problems: a review of recent insights

PURPOSE

The network perspective on psychopathology understands mental disorders as complex networks of interacting symptoms. Despite its recent debut, with conceptual foundations in 2008 and empirical foundations in 2010, the framework has received considerable attention and recognition in the last years.

METHODS

This paper provides a review of all empirical network studies published between 2010 and 2016 and discusses them according to three main themes: comorbidity, prediction, and clinical intervention.

RESULTS

Pertaining to comorbidity, the network approach provides a powerful new framework to explain why certain disorders may co-occur more often than others. For prediction, studies have consistently found that symptom networks of people with mental disorders show different characteristics than that of healthy individuals, and preliminary evidence suggests that networks of healthy people show early warning signals before shifting into disordered states. For intervention, centrality-a metric that measures how connected and clinically relevant a symptom is in a network-is the most commonly studied topic, and numerous studies have suggested that targeting the most central symptoms may offer novel therapeutic strategies.

CONCLUSIONS

We sketch future directions for the network approach pertaining to both clinical and methodological research, and conclude that network analysis has yielded important insights and may provide an important inroad towards personalized medicine by investigating the network structures of individual patients.

Major Depression as a Complex Dynamic System

In this paper, we characterize major depression (MD) as a complex dynamic system in which symptoms (e.g., insomnia and fatigue) are directly connected to one another in a network structure. We hypothesize that individuals can be characterized by their own network with unique architecture and resulting dynamics. With respect to architecture, we show that individuals vulnerable to developing MD are those with strong connections between symptoms: e.g., only one night of poor sleep suffices to make a particular person feel tired. Such vulnerable networks, when pushed by forces external to the system such as stress, are more likely to end up in a depressed state; whereas networks with weaker connections tend to remain in or return to a non-depressed state. We show this with a simulation in which we model the probability of a symptom becoming 'active' as a logistic function of the activity of its neighboring symptoms. Additionally, we show that this model potentially explains some well-known empirical phenomena such as spontaneous recovery as well as accommodates existing theories about the various subtypes of MD. To our knowledge, we offer the first intra-individual, symptom-based, process model with the potential to explain the pathogenesis and maintenance of major depression.

Kinds versus continua: a review of psychometric approaches to uncover the structure of psychiatric constructs

The question of whether psychopathology constructs are discrete kinds or continuous dimensions represents an important issue in clinical psychology and psychiatry. The present paper reviews psychometric modelling approaches that can be used to investigate this question through the application of statistical models. The relation between constructs and indicator variables in models with categorical and continuous latent variables is discussed, as are techniques specifically designed to address the distinction between latent categories as opposed to continua (taxometrics). In addition, we examine latent variable models that allow latent structures to have both continuous and categorical characteristics, such as factor mixture models and grade-of-membership models. Finally, we discuss recent alternative approaches based on network analysis and dynamical systems theory, which entail that the structure of constructs may be continuous for some individuals but categorical for others. Our evaluation of the psychometric literature shows that the kinds-continua distinction is considerably more subtle than is often presupposed in research; in particular, the hypotheses of kinds and continua are not mutually exclusive or exhaustive. We discuss opportunities to go beyond current research on the issue by using dynamical systems models, intra-individual time series and experimental manipulations.

Unpacking Constructs: A Network Approach for Studying War Exposure, Daily Stressors and Post-Traumatic Stress Disorder

Conflict-affected populations are exposed to stressful events during and after war, and it is well established that both take a substantial toll on individuals’ mental health. Exactly how exposure to events during and after war affect mental health is a topic of considerable debate. Various hypotheses have been put forward on the relation between stressful war exposure (SWE), daily stressors (DS) and the development of post-traumatic stress disorder (PTSD). This paper seeks to contribute to this debate by critically reflecting upon conventional modeling approaches and by advancing an alternative model to studying interrelationships between SWE, DS, and PTSD variables. The network model is proposed as an innovative and comprehensive modeling approach in the field of mental health in the context of war. It involves a conceptualization and representation of variables and relationships that better approach reality, hence improving methodological rigor. It also promises utility in programming and delivering mental health support for war-affected populations.

Transdiagnostic Networks: Commentary on Nolen-Hoeksema and Watkins (2011)

Nolen-Hoeksema and Watkins (2011, this issue) propose a useful model for thinking about transdiagnostic processes involved in mental disorders. Here, we argue that their model is naturally compatible with a network account of mental disorders, in which disorders are viewed as sets of mutually reinforcing symptoms. We show that network models are typically transdiagnostic in nature, because different disorders often share symptoms. We illustrate this by constructing a network for generalized anxiety and major depression. In addition, we show that even a simple network structure naturally accounts for the phenomena of multifinality and divergent trajectories that Nolen-Hoeksema and Watkins identify as crucial in thinking about transdiagnostic phenomena.

From loss to loneliness: The relationship between bereavement and depressive symptoms

Spousal bereavement can cause a rise in depressive symptoms. This study empirically evaluates 2 competing explanations concerning how this causal effect is brought about: (a) a traditional latent variable explanation, in which loss triggers depression which then leads to symptoms; and (b) a novel network explanation, in which bereavement directly affects particular depression symptoms which then activate other symptoms. We used data from the Changing Lives of Older Couples (CLOC) study and compared depressive symptomatology, assessed via the 11-item Center for Epidemiologic Studies Depression Scale (CES-D), among those who lost their partner (N = 241) with a still-married control group (N = 274). 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. Compared to the control group, widow(er)s' scores were significantly higher for symptoms of loneliness, sadness, depressed mood, and appetite loss, and significantly lower for happiness and enjoyed life. 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 direct effects of spousal loss on particular symptoms are inconsistent with the predictions of latent variable models, but can be explained from a network perspective. The findings support a growing body of literature showing that specific adverse life events differentially affect depressive symptomatology, and suggest that future studies should examine interventions that directly target such symptoms.

Network analysis: an integrative approach to the structure of psychopathology

In network approaches to psychopathology, disorders result from the causal interplay between symptoms (e.g., worry → insomnia → fatigue), possibly involving feedback loops (e.g., a person may engage in substance abuse to forget the problems that arose due to substance abuse). The present review examines methodologies suited to identify such symptom networks and discusses network analysis techniques that may be used to extract clinically and scientifically useful information from such networks (e.g., which symptom is most central in a person's network). The authors also show how network analysis techniques may be used to construct simulation models that mimic symptom dynamics. Network approaches naturally explain the limited success of traditional research strategies, which are typically based on the idea that symptoms are manifestations of some common underlying factor, while offering promising methodological alternatives. In addition, these techniques may offer possibilities to guide and evaluate therapeutic interventions.

Dimensions of Normal Personality as Networks in Search of Equilibrium: You Can't like Parties if you Don't like People

In one currently dominant view on personality, personality dimensions (e.g. extraversion) are causes of human behaviour, and personality inventory items (e.g. ‘I like to go to parties’ and ‘I like people’) are measurements of these dimensions. In this view, responses to extraversion items correlate because they measure the same latent dimension. In this paper, we challenge this way of thinking and offer an alternative perspective on personality as a system of connected affective, cognitive and behavioural components. We hypothesize that these components do not hang together because they measure the same underlying dimension; they do so because they depend on one another directly for causal, homeostatic or logical reasons (e.g. if one does not like people and it is harder to enjoy parties). From this ‘network perspective’, personality dimensions emerge out of the connectivity structure that exists between the various components of personality. After outlining the network theory, we illustrate how it applies to personality research in four domains: (i) the overall organization of personality components; (ii) the distinction between state and trait; (iii) the genetic architecture of personality; and (iv) the relation between personality and psychopathology. Copyright © 2012 John Wiley & Sons, Ltd.

Measurable Like Temperature or Mereological like Flocking? on the Nature of Personality Traits

Some commentators wholeheartedly disagreed with the central tenet of the network perspective on personality, namely that traits are the result of mutual interactions between thoughts, feelings and behaviours. In this rejoinder, we primarily focus on these commentaries by (i) clarifying the main differences between the latent versus the network view on traits; (ii) discussing some of the arguments in favour of the latent trait views that were put forward by these commentators; and by (iii) comparing the capacity of both views to explain thoughts, feelings and behaviours. Some commentators were by and large positive about the network perspective, and we discuss their excellent suggestions for defining components and linking these to genes and other biological mechanisms. We conclude that no doors should be closed in the study of personality and that, as such, alternative theories such as the network perspective should be welcomed, formalised and tested. Copyright © 2012 John Wiley & Sons, Ltd.

The pathoplasticity of dysphoric episodes: differential impact of stressful life events on the pattern of depressive symptom inter-correlations

BACKGROUND

Previous research has shown that stressful life events (SLEs) influence the pattern of individual depressive symptoms. However, we do not know how these differences arise. Two theories about the nature of psychiatric disorders have different predictions about the source of these differences: (1) SLEs influence depressive symptoms and correlations between them indirectly, via an underlying acute liability to develop a dysphoric episode (DE; common cause hypothesis); and (2) SLEs influence depressive symptoms and correlations between them directly (network hypothesis). The present study investigates the predictions of these two theories.

METHOD

We divided a population-based sample of 2096 Caucasian twins (49.9% female) who reported at least two aggregated depressive symptoms in the last year into four groups, based on the SLE they reported causing their symptoms. For these groups, we calculated tetrachoric correlations between the 14 disaggregated depressive symptoms and, subsequently, tested whether the resulting correlation patterns were significantly different and if those differences could be explained by underlying differences in a single acute liability to develop a DE.

RESULTS

The four SLE groups had markedly different correlation patterns between the depressive symptoms. These differences were significant and could not be explained by underlying differences in the acute liability to develop a DE.

CONCLUSIONS

Our results are not compatible with the common cause perspective but are consistent with the predictions of the network hypothesis. We elaborate on the implications of a conceptual shift to the network perspective for our diagnostic and philosophical approach to the concept of what constitutes a psychiatric disorder.

The small world of psychopathology

BACKGROUND

Mental disorders are highly comorbid: people having one disorder are likely to have another as well. We explain empirical comorbidity patterns based on a network model of psychiatric symptoms, derived from an analysis of symptom overlap in the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV).

PRINCIPAL FINDINGS

We show that a) half of the symptoms in the DSM-IV network are connected, b) the architecture of these connections conforms to a small world structure, featuring a high degree of clustering but a short average path length, and c) distances between disorders in this structure predict empirical comorbidity rates. Network simulations of Major Depressive Episode and Generalized Anxiety Disorder show that the model faithfully reproduces empirical population statistics for these disorders.

CONCLUSIONS

In the network model, mental disorders are inherently complex. This explains the limited successes of genetic, neuroscientific, and etiological approaches to unravel their causes. We outline a psychosystems approach to investigate the structure and dynamics of mental disorders.

Measurement invariance with respect to ethnicity of the Patient Health Questionnaire-9 (PHQ-9)

BACKGROUND

The Patient Health Questionnaire-9 (PHQ-9) has been widely used in research and clinical settings. To be able to attribute differences in PHQ-9 scores between groups with different cultural backgrounds to differences in the level of depression, the instrument has to possess measurement invariance.

METHODS

Data from the Apollo-D study were used. We used two strongly contrasting cultural groups (n=1772). Measurement invariance was assessed by comparing four categorical single factor models with an increasing number of restrictions, representing an increasingly stronger measurement invariance assumption.

RESULTS

The PHQ-9 was measurement invariant for ethnicity in women and partially measurement invariant for ethnicity in men. The item 'psychomotor problems' seemed to be culturally biased in the Surinam Dutch males. It had a higher loading and threshold compared to Dutch males.

LIMITATIONS

The sample is restricted to high risk primary care patients, we did not include a gold standard measure of depression and the analyses pertain to a single cross cultural comparison.

CONCLUSIONS

The observed higher total depression score for females in the Surinam Dutch group can be attributed to a true difference in the latent trait depression. For Surinam Dutch and Dutch men some caution is warranted when comparing results obtained with the PHQ-9. In the former group the scores may be biased slightly downward. Future research is needed to examine how the item 'psychomotor problems' performs in different populations. These findings highlight the necessity of establishing measurement invariance before drawing conclusions based on observed scores.