Statistical network models describing multivariate dependency structures in psychological data have gained increasing popularity. Such comparably novel statistical techniques require specific guidelines to make them accessible to the research community. So far, researchers have provided tutorials guiding the estimation of networks and their accuracy. However, there is currently little guidance in determining what parts of the analyses and results should be documented in a scientific report. A lack of such reporting standards may foster researcher degrees of freedom and could provide fertile ground for questionable reporting practices. Here, we introduce reporting standards for network analyses in cross-sectional data, along with a tutorial and two examples. The presented guidelines are aimed at researchers as well as the broader scientific community, such as reviewers and journal editors evaluating scientific work. We conclude by discussing how the network literature specifically can benefit from such guidelines for reporting and transparency. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Research Design , Research Report , Humans , Cross-Sectional Studies , Models, Statistical
To understand how compliance develops both in everyday and corporate environments, it is crucial to understand how different mechanisms work together to shape individuals' (non)compliant behavior. Existing compliance studies typically focus on a subset of theories (i.e., rational choice theories, social theories, legitimacy theories, capacity theories, and opportunity theories) to understand how key variables from one or several of these theories shape individual compliance. The present study provides a first integrated understanding of compliance, rooted in complexity science, in which key elements from these theories are considered simultaneously, and their relations to compliance and each other are explored using network analysis. This approach is developed by analyzing online survey data (N = 562) about compliance with COVID-19 mitigation measures. Traditional regression analysis shows that elements from nearly all major compliance theories (except for social theories) are associated with compliance. The network analysis revealed groupings and interconnections of variables that did not track the existing compliance theories and point to a complexity overlooked in existing compliance research. These findings demonstrate a fundamentally different perspective on compliance, which moves away from traditional narrow, non-network approaches. Instead, they showcase a complexity science understanding of compliance, in which compliance is understood as a network of interacting variables derived from different theories that interact with compliance. This points to a new research agenda that is oriented on mapping compliance networks, and testing and modelling how regulatory and management interventions interact with each other and compliance within such networks. Supplementary Information: The online version contains supplementary material available at 10.1007/s10551-022-05128-8.
Resumen El modelo de la psicopatología como red de síntomas propone centrarse en las interacciones dinámicas y causales entre los síntomas constitutivos del problema clínico. La idea principal es que la activación de un síntoma clínico lleva a la activación de otro síntoma vecino. Las conexiones entre ellos pueden ser biológicas, psicológicas o sociales. Los trastornos mentales son concebidos como estados estables alternativos de redes de síntomas fuertemente conectados. Esto permite un modelo explicativo común para todos los trastornos mentales, un modelo integral de psicopatología. A pesar del éxito de este nuevo camino metodológico, la mayoría de la información relevante se encuentra publicada en inglés. En este artículo, se presenta, en idioma español, la teoría de la psicopatología como red de síntomas y su modelo, su relevancia para la investigación, docencia y práctica clínica de la psicología y la psiquiatría, a los fines de incrementar su difusión y diseminación.
Abstract Over the past years, psychopathology has frequently been represented as a complex system, where psychiatric symptoms are causally interconnected in a network architecture. The network theory of psychopathology has led to more than 300 novel publications, academic courses, methodology for estimating novel models, and freely available software. However, despite the success of this novel research avenue, all relevant information has mostly been published in English. This paper translates the network theory of psychopathology and its model, together with its relevance for research and clinical practice of psychology and psychiatry, to the Spanish language. To serve the dissemination of this theory, this paper serves as an introductory paper for Spanish scholars, for example, as a starting point to learn more about the approach or for academic courses. The main idea of the network theory of psychopathology is that the activation of one clinical symptom in the network leads to the activation of a neighboring symptom. If symptoms are strongly connected with each other, for example, excessive worry and insomnia, they are more likely to be in the same state, meaning that if a person faces a stressful life event such as losing one's job, the activation of the symptom excessive worry will increase the probability they will also suffer from insomnia. In this way, a whole symptom activation pattern develops from which mental disorders emerge. Mental disorders are conceived as stable states of strongly connected symptom networks, allowing for a common explanatory model for multiple mental disorders, thereby providing a comprehensive model of psychopathology. Traditional representations of mental disorders conceptualize symptoms as merely passive indicators of latent, underlying mental disorders which act as common causes for patients' symptomatology. The network theory of psychopathology flips the explanatory and statistical model: instead of focusing on one underlying cause or underlying causes, it proposes to study the direct interactions between these symptoms. This imposes two important implications for the conceptualization of mental disorders. First, symptoms are no longer statistically exchangeable since every symptom can have a different role in the onset and development of psychopathology. Some symptoms can be more important than others in keeping the whole system "stuck" in a disordered state. Second, comorbidity is conceptualized as clustering symptoms which are connected to each other via certain "bridge symptoms". Bridge symptoms are symptoms which are attributed to two (or more) mental disorders, such as Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD). If a person suffers from symptoms of MDD, such as loss of motivation and depressed mood, this can lead to the activation of bridge symptoms such as fatigue and concentration problems, which by themselves lead to the activation of GAD symptoms such as irritability and excessive worry.
Identifying the different influences of symptoms in dynamic psychopathology models may hold promise for increasing treatment efficacy in clinical applications. Dynamic psychopathology models study the behavioral patterns of symptom networks, where symptoms mutually enforce each other. Interventions could be tailored to specific symptoms that are most effective at lowering symptom activity or that hinder the further development of psychopathology. Simulating interventions in psychopathology network models fits in a novel tradition where symptom-specific perturbations are used as in silico interventions. Here, we present the NodeIdentifyR algorithm (NIRA) to identify the projected most efficient, symptom-specific intervention target in a network model (i.e., the Ising model). We implemented NIRA in a freely available R package. The technique studies the projected effects of symptom-specific interventions by simulating data while symptom parameters (i.e., thresholds) are systematically altered. The projected effect of these interventions is defined in terms of the expected change in overall symptom activity across simulations. With this algorithm, it is possible to study (1) whether symptoms differ in their projected influence on the behavior of the symptom network and, if so, (2) which symptom has the largest projected effect in lowering or increasing overall symptom activation. As an illustration, we apply the algorithm to an empirical dataset containing Post-Traumatic Stress Disorder symptom assessments of participants who experienced the Wenchuan earthquake in 2008. The most important limitations of the method are discussed, as well as recommendations for future research, such as shifting towards modeling individual processes to validate these types of simulation-based intervention methods.
Mental Disorders , Psychopathology , Algorithms , Humans , Mental Disorders/diagnosis , Research Design
Inspired by modeling approaches from the ecosystems literature, in this paper, we expand the network approach to psychopathology with risk and protective factors to arrive at an integrated analysis of resilience. We take a complexity approach to investigate the multifactorial nature of resilience and present a system in which a network of interacting psychiatric symptoms is targeted by risk and protective factors. These risk and protective factors influence symptom development patterns and thereby increase or decrease the probability that the symptom network is pulled toward a healthy or disorder state. In this way, risk and protective factors influence the resilience of the network. We take a step forward in formalizing the proposed system by implementing it in a statistical model and translating different influences from risk and protective factors to specific targets on the node and edge parameters of the symptom network. To analyze the behavior of the system under different targets, we present two novel network resilience metrics: Expected Symptom Activity (ESA, which indicates how many symptoms are active or inactive) and Symptom Activity Stability (SAS, which indicates how stable the symptom activity patterns are). These metrics follow standard practices in the resilience literature, combined with ideas from ecology and physics, and characterize resilience in terms of the stability of the system's healthy state. By discussing the advantages and limitations of our proposed system and metrics, we provide concrete suggestions for the further development of a comprehensive modeling approach to study the complex relationship between risk and protective factors and resilience.
Background: Due to the COVID-19 pandemic, Argentina has been under mandatory quarantine. We have aimed to investigate the state of mental health of the Argentine population and the behaviours adopted to cope with mental distress during quarantine. Method: An online survey was conducted using a probabilistic sampling technique and stratified according to the geographic regions of the country. The survey covered days 7-11 (n = 2,631) and days 50-55 (n = 2,068) after compulsory quarantine. The psychological impact was measured using the 27-item Symptom CheckList (SCL-27), which provides a Global Severity Index (GSI). An ad hoc questionnaire registered problematic, healthy and other behaviours. Two network models were estimated using a Mixed Graphical Model. Data from the two periods were compared and analysed. Outcomes: Higher GSI scores and greater risk of experiencing mental disorder were found in Period 2 as compared with Period 1. The lowest GSI scores were associated with physical activity in both periods, and meditation and yoga in Period 1. Drug users reported the highest GSI scores in both periods. The Network Comparison Test confirmed a significant change in symptomatology structure over the two quarantine periods. Conclusion: This study showed that psychological symptoms and the risk of experiencing mental disorder increased significantly from Period 1 to Period 2. Network analysis suggested that the quarantine might have brought about changes in the relationships between symptoms. Overall results revealed the relevance of mental health and the need to take mental health actions upon imposing quarantine during the current COVID-19 pandemic.
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.
Mental Disorders/psychology , Resilience, Psychological , Stress, Psychological/psychology , Adaptation, Psychological/physiology , Emotions/physiology , Humans , Individuality , Models, Psychological
