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.

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.

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.

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.

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.

Reductionism in retreat.

We address the commentaries on our target article in terms of four major themes. First, we note that virtually all commentators agree that mental disorders are not brain disorders in the common interpretation of these terms, and establish the consensus that explanatory reductionism is not a viable thesis. Second, we address criticisms to the effect that our article was misdirected or aimed at a straw man; we argue that this is unlikely, given the widespread communication of reductionist slogans in psychopathology research and society. Third, we tackle the question of whether intentionality, extended systems, and multiple realizability are as problematic as claimed in the target article, and we present a number of nuances and extensions with respect to our article. Fourth, we discuss the question of how the network approach should incorporate biological factors, given that wholesale reductionism is an unlikely option.

Brain disorders? Not really: Why network structures block reductionism in psychopathology research.

In the past decades, reductionism has dominated both research directions and funding policies in clinical psychology and psychiatry. The intense search for the biological basis of mental disorders, however, has not resulted in conclusive reductionist explanations of psychopathology. Recently, network models have been proposed as an alternative framework for the analysis of mental disorders, in which mental disorders arise from the causal interplay between symptoms. In this target article, we show that this conceptualization can help explain why reductionist approaches in psychiatry and clinical psychology are on the wrong track. First, symptom networks preclude the identification of a common cause of symptomatology with a neurobiological condition; in symptom networks, there is no such common cause. Second, symptom network relations depend on the content of mental states and, as such, feature intentionality. Third, the strength of network relations is highly likely to depend partially on cultural and historical contexts as well as external mechanisms in the environment. Taken together, these properties suggest that, if mental disorders are indeed networks of causally related symptoms, reductionist accounts cannot achieve the level of success associated with reductionist disease models in modern medicine. As an alternative strategy, we propose to interpret network structures in terms of D. C. Dennett's (1987) notion of real patterns, and suggest that, instead of being reducible to a biological basis, mental disorders feature biological and psychological factors that are deeply intertwined in feedback loops. This suggests that neither psychological nor biological levels can claim causal or explanatory priority, and that a holistic research strategy is necessary for progress in the study of mental disorders.

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.

Critical slowing down as early warning for the onset and termination of depression.

About 17% of humanity goes through an episode of major depression at some point in their lifetime. Despite the enormous societal costs of this incapacitating disorder, it is largely unknown how the likelihood of falling into a depressive episode can be assessed. Here, we show for a large group of healthy individuals and patients that the probability of an upcoming shift between a depressed and a normal state is related to elevated temporal autocorrelation, variance, and correlation between emotions in fluctuations of autorecorded emotions. These are indicators of the general phenomenon of critical slowing down, which is expected to occur when a system approaches a tipping point. Our results support the hypothesis that mood may have alternative stable states separated by tipping points, and suggest an approach for assessing the likelihood of transitions into and out of depression.

Slowing Down of Recovery as Generic Risk Marker for Acute Severity Transitions in Chronic Diseases.

OBJECTIVE: We propose a novel paradigm to predict acute attacks and exacerbations in chronic episodic disorders such as asthma, cardiac arrhythmias, migraine, epilepsy, and depression. A better generic understanding of acute transitions in chronic dynamic diseases is increasingly important in critical care medicine because of the higher prevalence and incidence of these chronic diseases in our aging societies. DATA SOURCES: PubMed, Medline, and Web of Science. STUDY SELECTION: We selected studies from biology and medicine providing evidence of slowing down after a perturbation as a warning signal for critical transitions. DATA EXTRACTION: Recent work in ecology, climate, and systems biology has shown that slowing down of recovery upon perturbations can indicate loss of resilience across complex, nonlinear biologic systems that are approaching a tipping point. This observation is supported by the empiric studies in pathophysiology and controlled laboratory experiments with other living systems, which can flip from one state of clinical balance to a contrasting one. We discuss examples of such evidence in bodily functions such as blood pressure, heart rate, mood, and respiratory regulation when a tipping point for a transition is near. CONCLUSIONS: We hypothesize that in a range of chronic episodic diseases, indicators of critical slowing down, such as rising variance and temporal correlation, may be used to assess the risk of attacks, exacerbations, and even mortality. Identification of such early warning signals over a range of diseases will enhance the understanding of why, how, and when attacks and exacerbations will strike and may thus improve disease management in critical care medicine.

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.

A constructionist account of emotional disorders.

Lindquist et al. present a strong case for a constructionist account of emotion. First, we elaborate on the ramifications that a constructionist account of emotions might have for psychiatric disorders with emotional disturbances as core elements. Second, we reflect on similarities between Lindquist et al.'s model and recent attempts at formulating psychiatric disorders as networks of causally related symptoms.

Psychopathological networks: Theory, methods and practice.

In recent years, network approaches to psychopathology have sparked much debate and have had a significant impact on how mental disorders are perceived in the field of clinical psychology. However, there are many important challenges in moving from theory to empirical research and clinical practice and vice versa. Therefore, in this article, we bring together different points of view on psychological networks by methodologists and clinicians to give a critical overview on these challenges, and to present an agenda for addressing these challenges. In contrast to previous reviews, we especially focus on methodological issues related to temporal networks. This includes topics such as selecting and assessing the quality of the nodes in the network, distinguishing between- and within-person effects in networks, relating items that are measured at different time scales, and dealing with changes in network structures. These issues are not only important for researchers using network models on empirical data, but also for clinicians, who are increasingly likely to encounter (person-specific) networks in the consulting room.

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.

Comorbidity: a network perspective.

The pivotal problem of comorbidity research lies in the psychometric foundation it rests on, that is, latent variable theory, in which a mental disorder is viewed as a latent variable that causes a constellation of symptoms. From this perspective, comorbidity is a (bi)directional relationship between multiple latent variables. We argue that such a latent variable perspective encounters serious problems in the study of comorbidity, and offer a radically different conceptualization in terms of a network approach, where comorbidity is hypothesized to arise from direct relations between symptoms of multiple disorders. We propose a method to visualize comorbidity networks and, based on an empirical network for major depression and generalized anxiety, we argue that this approach generates realistic hypotheses about pathways to comorbidity, overlapping symptoms, and diagnostic boundaries, that are not naturally accommodated by latent variable models: Some pathways to comorbidity through the symptom space are more likely than others; those pathways generally have the same direction (i.e., from symptoms of one disorder to symptoms of the other); overlapping symptoms play an important role in comorbidity; and boundaries between diagnostic categories are necessarily fuzzy.

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 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 (Mage = 13.34 years); at Time 3 (T3) there were 236 participants (Mage = 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.

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.

The effects of cognitive-behavior therapy for depression on repetitive negative thinking: A meta-analysis.

It is not clear if treatments for depression targeting repetitive negative thinking (RNT: rumination, worry and content-independent perseverative thinking) have a specific effect on RNT resulting in better outcomes than treatments that do not specifically target rumination. We conducted a systematic search of PsycINFO, PubMed, Embase and the Cochrane library for randomized trials in adolescents, adults and older adults comparing CBT treatments for (previous) depression with control groups or with other treatments and reporting outcomes on RNT. Inclusion criteria were met by 36 studies with a total of 3307 participants. At post-test we found a medium-sized effect of any treatment compared to control groups on RNT (g = 0.48; 95% CI: 0.37-0.59). Rumination-focused CBT: g = 0.76, <0.01; Cognitive Control Training: g = 0.62, p < .01; CBT: g = 0.57, p < .01; Concreteness training: g = 0.53, p < .05; and Mindfulness-based Cognitive Therapy: g = 0.42, p < .05 had medium sized and significantly larger effect sizes than other types of treatment (i.e., anti-depressant medication, light therapy, engagement counseling, life review, expressive writing, yoga) (g = 0.14) compared to control groups. Effects on RNT at post-test were strongly associated with the effects on depression severity and this association was only significant in RNT-focused CBT. Our results suggest that in particular RNT-focused CBT may have a more pronounced effect on RNT than other types of interventions. Further mediation and mechanistic studies to test the predictive value of reductions in RNT following RNT-focused CBT for subsequent depression outcomes are called for.