Bridging the gap between complexity science and clinical practice by formalizing idiographic theories: a computational model of functional analysis

Applying dynamic systems theory and complexity theory methods in psychotherapy research: A systematic literature review

OBJECTIVE

Dynamic systems theory and complexity theory (DST/CT) is a framework explaining how complex systems change and adapt over time. In psychotherapy, DST/CT can be used to understand how a person's mental and emotional state changes during therapy incorporating higher levels of complexity. This study aimed to systematically review the variability of DST/CT methods applied in psychotherapy research.

METHODS

A primary studies search was conducted in the EBSCO and Web of Knowledge databases, extracting information about the analyzed DST/CT phenomena, employed mathematical methods to investigate these phenomena, descriptions of specified dynamic models, psychotherapy phenomena, and other information regarding studies with empirical data (e.g., measurement granularity).

RESULTS

After screening 38,216 abstracts and 4,194 full texts, N = 41 studies published from 1990 to 2021 were identified. The employed methods typically included measures of dynamic complexity or chaoticity. Computational and simulation studies most often employed first-order ordinary differential equations and typically focused on describing the time evolution of client-therapist dyadic influences. Eligible studies with empirical data were usually based on case studies and focused on data with high time intensity of within-session dynamics.

CONCLUSION

This review provides a descriptive synthesis of the current state of the proliferation of DST/CT methods in the psychotherapy research field.

Beyond Diagnosis and Comorbidities-A Scoping Review of the Best Tools to Measure Complexity for Populations with Mental Illness

Beyond the challenges of diagnosis, complexity measurement in clients with mental illness is an important but under-recognized area. Accurate and appropriate psychiatric diagnoses are essential, and further complexity measurements could contribute to improving patient understanding, referral, and service matching and coordination, outcome evaluation, and system-level care planning. Myriad conceptualizations, frameworks, and definitions of patient complexity exist, which are operationalized by a variety of complexity measuring tools. A limited number of these tools are developed for people with mental illness, and they differ in the extent to which they capture clinical, psychosocial, economic, and environmental domains. Guided by the PRISMA Extension for Scoping Reviews, this review evaluates the tools best suited for different mental health settings. The search found 5345 articles published until November 2023 and screened 14 qualified papers and corresponding tools. For each of these, detailed data on their use of psychiatric diagnostic categories, definition of complexity, primary aim and purpose, context of use and settings for their validation, best target populations, historical references, extent of biopsychosocial information inclusion, database and input technology required, and performance assessments were extracted, analyzed, and presented for comparisons. Two tools-the INTERMED, a clinician-scored and multiple healthcare data-sourced tool, and the VCAT, a computer-based instrument that utilizes healthcare databases to generate a comprehensive picture of complexity-are exemplary among the tools reviewed. Information on these limited but suitable tools related to their unique characteristics and utilities, and specialized recommendations for their use in mental health settings could contribute to improved patient care.

What time-varying network models based on functional analysis tell us about the course of a patient's problem

Background: Relations among psychological variables are assumed to be complex and to vary over time. Personalized networks can model multivariate complex interactions. The development of time-varying networks allows to model the variation of parameters over time. Objectives: We aimed to determine the value of time-varying networks for clinical practice. Methods: We applied time-varying mixed graphical models (TV-MGM) and time-varying vector autoregressive models (TV-VAR) to intensive longitudinal data of nine participants with depressive symptoms (n = 6) or anxiety (n = 3). Results: Most of the participants showed temporal changes in network topology within the assessment period of 30 days. Time-varying networks of participants with small, medium, and large time variability in edge parameters clearly show the different temporal evolvements of dynamic interactions between variables. The case example indicates clinical utility but also limitations to the application of time-varying networks in clinical practice. Conclusion: Time-varying network models provide a data-driven and exploratory approach that could complement current diagnostic standards by reflecting interacting, often mutually reinforcing processes of mental health problems and by accounting for variation over time. They can be used to generate hypotheses for further confirmatory and clinical testing.

Understanding the Risk of Sexual Reoffending in Adult Men: A Network-Based Model

The predominant approach to understand dynamic risk factors of sexual reoffending has been referred to as the Propensities Model (Thornton, 2016). According to this model, dynamic risk factors can be conceptualized as latent constructs whose change alters the risk of sexual reoffending. Despite its strengths and contributions to research, this model does not offer answers to the question of how dynamic risk factors contribute to the risk of sexual reoffending, or of how sustained change in risk might take place. In this paper we introduce the Network-Based Model of Risk of Sexual Reoffending (NBM-RSR), which addresses several limitations and constraints of the Propensities Model and offers empirically testable propositions regarding the nature and development of the risk of sexual reoffending. The NBM-RSR considers risk of sexual reoffending to involve a self-sustaining network of causally connected dynamic risk factors. Consistent with this, an increased risk of sexual reoffending is characterized through a network that contains more and stronger interconnected dynamic risk factors with a higher strength. Sustained change in risk of sexual reoffending occurs when activity in the network exceeds a critical point resulting in a new self-sustaining network. Propositions based on the NBM-RSR are introduced and translated into testable hypotheses. These propositions revolve around (a) risk of sexual reoffending resulting from the construction of a network of causally connected dynamic risk factors, (b) network stability, sudden changes, and critical transitions, and (c) dynamic risk factors' relative influence on risk of sexual reoffending.

Implications of the Network Theory for the Treatment of Mental Disorders: A Secondary Analysis of a Randomized Clinical Trial

Importance

Conceptualizing mental disorders as latent entities has been challenged by the network theory of mental disorders, which states that psychological problems are constituted by a network of mutually interacting symptoms. While the implications of the network approach for planning and evaluating treatments have been intensively discussed, empirical support for the claims of the network theory regarding treatment effects is lacking.

Objective

To assess the extent to which specific hypotheses derived from the network theory regarding the (interindividual) changeability of symptom dynamics in response to treatment align with empirical data.

Design, Setting, and Participants

This secondary analysis entails data from a multisite randomized clinical trial, in which 254 patients with chronic depression reported on their depressive symptoms at every treatment session. Data collection was conducted between March 5, 2010, and October 14, 2013, and this analysis was conducted between November 1, 2021, and May 31, 2022.

Intervention

Thirty-two sessions of either disorder-specific or nonspecific psychotherapy for chronic depression.

Main Outcomes and Measures

Longitudinal associations of depressive symptoms with each other and change of these associations through treatment estimated by a time-varying longitudinal network model.

Results

In a sample of 254 participants (166 [65.4%] women; mean [SD] age, 44.9 [11.9] years), symptom interactions changed through treatment, and this change varied across treatments and individuals. The mean absolute (ie, valence-ignorant) strength of symptom interactions (logarithmic odds ratio scale) increased from 0.40 (95% CI, 0.36-0.44) to 0.60 (95% CI, 0.52-0.70) during nonspecific psychotherapy and to 0.56 (95% CI, 0.48-0.64) during disorder-specific psychotherapy. In contrast, the mean raw (ie, valence-sensitive) strength of symptom interactions decreased from 0.32 (95% CI, 0.28-0.36) to 0.26 (95% CI, 0.20-0.32) and to 0.09 (95% CI, 0.02-0.16), respectively. Changing symptom severity could be explained to a large extent by symptom interactions.

Conclusions and Relevance

These findings suggest that specific treatment-related hypotheses of the network theory align well with empirical data. Conceptualizing mental disorders as symptom networks and treatments as measures that aim to change these networks is expected to give further insights into the working mechanisms of mental health treatments, leading to the improvement of current and the development of new treatments.

Trial Registration

ClinicalTrials.gov Identifier: NCT00970437.

Measuring the active elements of cognitive-behavioral therapies

Understanding how and for whom cognitive-behavioral therapies work is central to the development and improvement of mental health interventions. Suboptimal quantification of the active elements of cognitive-behavioral therapies has hampered progress in elucidating mechanisms of change. To advance process research on cognitive-behavioral therapies, we describe a theoretical measurement framework that focuses on the delivery, receipt, and application of the active elements of these interventions. We then provide recommendations for measuring the active elements of cognitive-behavioral therapies aligned with this framework. Finally, to support measurement harmonization and improve study comparability, we propose the development of a publicly available repository of assessment tools: the Active Elements of Cognitive-Behavioral Therapies Measurement Kit.

Hyperbaric Oxygen and Outcomes Following the Brain Injury: A Systematic Review

Introduction

Hyperbaric oxygen therapy (HBO2) aims to address ischemia resulting from brain injury by subjecting patients to an atmosphere that dramatically raises the concentration of inspired oxygen (100% O2 at greater than 1 ATA). This results in elevated levels of oxygen in the plasma, which in turn boosts the delivery of oxygen for diffusion to the brain tissue.

Objective

To study the efficacy of hyperbaric oxygen (HBO)-based modalities in brain injury.

Method

Preferred reporting items for systematic reviews protocol was applied to perform literature search regarding this analytical review.

Results

In our study, fifteen studies are included in this review, involving 1067 people. The mean age group of patients enrolled was 57.0±11.6 and the mean NIHSS score was 10.5±8.7, of which 21 participants had moderate to severe neurological impairment. The total number of HBO treatments was 8 to 70 times (28.3±17.9), at the end of the 6-month follow-up period. mRS (modified Rankin scale) ≤3 was found in 25 cases, of which 12 patients with high-grade aSAH recovered. Poor prognosis was prevalent in patients who experienced delayed cerebral ischemia, this was true for 22.7% of patients in this study. In 3 studies conducted by Rockswold, ICP (mm Hg) was significantly lower in the HBO2 group after the treatment than pretreatment. (p<0.05). 4 studies showed an improvement in GCS score after HBO2 therapy.One trial (Imai 2006) reported that three patients in the HBO group died due to pneumonia (two) and heart failure (one) and one patient died in the control group due to heart failure. Overall, it is relatively safe to use HBO in the treatment of brain-related haemorrhage, strokes, and injury as there were no major complications reported.

Conclusion

This systematic review demonstrates that HBO2 has significant clinical potential in treatment of brain related haemorrhages, stroke and injury.

Heterogeneity in Individual Network Analysis: Reality or Illusion?

The use of idiographic research techniques has gained popularity within psychological research and network analysis in particular. Idiographic research has been proposed as a promising avenue for future research, with differences between idiographic results highlighting evidence for radical heterogeneity. However, in the quest to address the individual in psychology, some classic statistical problems, such as those arising from sampling variation and power limitations, should not be overlooked. This article aims to determine to what extent current tools to compare idiographic networks are suited to disentangle true from illusory heterogeneity in the presence of sampling error. To this end, we investigate the performance of tools to inspect heterogeneity (visual inspection, comparison of centrality measures, investigating standard deviations of random effects, and GIMME) through simulations. Results show that power limitations hamper the validity of conclusions regarding heterogeneity and that the power required to assess heterogeneity adequately is often not realized in current research practice. Of the tools investigated, inspecting standard deviations of random effects and GIMME proved the most suited. However, all tools evaluated leave the door wide open to misinterpret all observed variability in terms of individual differences. Hence, the current paper calls for caution in the use and interpretation of new time-series techniques when it comes to heterogeneity.

A cultural-ecosocial systems view for psychiatry

While contemporary psychiatry seeks the mechanisms of mental disorders in neurobiology, mental health problems clearly depend on developmental processes of learning and adaptation through ongoing interactions with the social environment. Symptoms or disorders emerge in specific social contexts and involve predicaments that cannot be fully characterized in terms of brain function but require a larger social-ecological view. Causal processes that result in mental health problems can begin anywhere within the extended system of body-person-environment. In particular, individuals' narrative self-construal, culturally mediated interpretations of symptoms and coping strategies as well as the responses of others in the social world contribute to the mechanisms of mental disorders, illness experience, and recovery. In this paper, we outline the conceptual basis and practical implications of a hierarchical ecosocial systems view for an integrative approach to psychiatric theory and practice. The cultural-ecosocial systems view we propose understands mind, brain and person as situated in the social world and as constituted by cultural and self-reflexive processes. This view can be incorporated into a pragmatic approach to clinical assessment and case formulation that characterizes mechanisms of pathology and identifies targets for intervention.

A scoping review of neurodegenerative manifestations in explainable digital phenotyping

Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.

Dynamical systems in computational psychiatry: A toy-model to apprehend the dynamics of psychiatric symptoms

Introduction

These last years, scientific research focuses on the dynamical aspects of psychiatric disorders and their clinical significance. In this article, we proposed a theoretical framework formalized as a generic mathematical model capturing the heterogeneous individual evolutions of psychiatric symptoms. The first goal of this computational model based on differential equations is to illustrate the nonlinear dynamics of psychiatric symptoms. It offers an original approach to nonlinear dynamics to clinical psychiatrists.

Methods

In this study, we propose a 3+1 dimensions model (x, y, z + f) reproducing the clinical observations encountered in clinical psychiatry with: a variable modeling environmental noise (z) on the patient's internal factors (y) with its temporal specificities (f) and symptomatology (x). This toy-model is able to integrate empirical or simulated data from the influence of perceived environmental over time, their potential importance on the internal and subjective patient-specific elements, and their interaction with the apparent intensity of symptoms.

Results

Constrained by clinical observation of case formulations, the dynamics of psychiatric symptoms is studied through four main psychiatric conditions were modeled: i) a healthy situation, ii) a kind of psychiatric disorder evolving following an outbreak (i.e., schizophrenia spectrum), iii) a kind of psychiatric disorder evolving by kindling and bursts (e.g., bipolar and related disorders); iv) and a kind of psychiatric disorder evolving due to its high susceptibility to the environment (e.g., spersistent complex bereavement disorder). Moreover, we simulate the action of treatments on different psychiatric conditions.

Discussion

We show that the challenges of dynamical systems allow to understand the interactions of psychiatric symptoms with environmental, descriptive, subjective or biological variables. Although this non-linear dynamical model has limitations (e.g., explanatory scope or discriminant validity), simulations provide at least five main interests for clinical psychiatry, such as a visualization of the potential different evolution of psychiatric disorders, formulation of clinical cases, information about attracting states and bifurcations, or the possibility of a nosological refinement of psychiatric models (e.g., staging and symptom network models).

Perceived Causal Problem Networks: Reliability, Central Problems, and Clinical Utility for Depression

Personalized case conceptualization is often regarded as a prerequisite for treatment success in psychotherapy for patients with comorbidity. This article presents Perceived Causal Networks, a novel method in which patients rate perceived causal relations among behavioral and emotional problems. First, 231 respondents screening positive for depression completed an online Perceived Causal Networks questionnaire. Median completion time (including repeat items to assess immediate test-retest reliability) was 22.7 minutes, and centrality measures showed excellent immediate test-retest reliability. Networks were highly idiosyncratic, but worrying and ruminating were the most central items for a third of respondents. Second, 50 psychotherapists rated the clinical utility of Perceived Causal Networks visualizations. Ninety-six percent rated the networks as clinically useful, and the information in the individual visualizations was judged to contain 47% of the information typically collected during a psychotherapy assessment phase. Future studies should individualize networks further and evaluate the validity of perceived causal relations.

The role of moderate- and high-intensity supervised aerobic training in reducing steatosis and hepatic fibrosis in patients with non-alcoholic fatty liver disease; a randomized controlled trial

Aim

This study aimed to compare the effect of the same volume of moderate- and high-intensity aerobic exercise on patients' liver steatosis and fibrosis.

Background

Exercise is known strategy to deal with non-alcoholic fatty liver disease (NAFLD).

Methods

This Randomized Control Trial was performed on 60 patients randomly assigned to three arms of the study (1:1:1). Fibrosis and steatosis of liver including Control Attenuated Parameter (CAP) determined using Transient Elastography (TE). The control group was advised to adjust their lifestyle, as a routine management. The intervention groups additionally, participated on supervised exercise programs with two different intensities but the same volume of 1000 KCal per week. The intensities of 50% and 70% of V02 reserve were considered for moderate-intensity and vigorous programs, respectively.

Results

On six-month follow-up, none of outcomes were statistically significant among three arms of study. However, changes in some outcomes were reached to statistically significant difference in follow-up in comparison with baseline. The mean of CAP score changes was -19.43 (31.43) (P=0.03), 9.92 (26.81) (P=0.21), and 14.61 (18.03) (P=0.01) in control, moderate- and high-intensity groups, respectively. In the high-intensity group, in addition to steatosis, this difference was also observed in the rate of fibrosis. Besides, the level of serum aminotransferases in the group with moderate exercise after six months had a significant decrease compared to baseline. (P=0.01).

Conclusion

Improvement in steatosis and fibrosis was more evident in high- intensity group. As the rate of drop out was high, caution is needed in interpretation of the results.

Integrating clinician and patient case conceptualization with momentary assessment data to construct idiographic networks: Moving toward personalized treatment for eating disorders

Eating disorders are serious psychiatric illnesses with treatments ineffective for about 50% of individuals due to high heterogeneity of symptom presentation even within the same diagnoses, a lack of personalized treatments to address this heterogeneity, and the fact that clinicians are left to rely upon their own judgment to decide how to personalize treatment. Idiographic (personalized) networks can be estimated from ecological momentary assessment data, and have been used to investigate central symptoms, which are theorized to be fruitful treatment targets. However, both efficacy of treatment target selection and implementation with 'real world' clinicians could be maximized if clinician input is integrated into such networks. An emerging line of research is therefore proposing to integrate case conceptualizations and statistical routines, tying together the benefits from clinical expertise as well as patient experience and idiographic networks. The current pilot compares personalized treatment implications from different approaches to constructing idiographic networks. For two patients with a diagnosis of anorexia nervosa, we compared idiographic networks 1) based on the case conceptualization from clinician and patient, 2) estimated from patient EMA data (the current default in the literature), and 3) based on a combination of case conceptualization and patient EMA data networks, drawing on informative priors in Bayesian inference. Centrality-based treatment recommendations differed to varying extent between these approaches for patients. We discuss implications from these findings, as well as how these models may inform clinical practice by pairing evidence-based treatments with identified treatment targets.

Does the network structure of obsessive-compulsive symptoms at treatment admission identify patients at risk for non-response?

Exposure and response prevention is the gold-standard treatment for obsessive compulsive disorder (OCD), yet up to half of patients do not adequately respond. Thus, different approaches to identifying and intervening with non-responders are badly needed. One approach would be to better understand the functional connections among aspects of OCD symptoms and, ultimately, how to target those associations in treatment. In a large sample of patients who completed intensive treatment for OCD and related disorders (N = 1343), we examined whether differences in network structure of OCD symptom aspects existed at baseline between treatment responders versus non-responders. A network comparison test indicated a significant difference between OCD network structure for responders versus non-responders (M = 0.19, p = .02). Consistent differences emerged between responders and non-responders in how they responded to emotional distress. This pattern of associations suggests that non-responders may have been more reactive to their distress by performing compulsions, thereby worsening their functioning. By examining the association between baseline distress intolerance with other symptom aspects that presumably maintain the disorder (e.g., ritualizing), clinicians can more effectively target those associations in treatment.

Recovering Within-Person Dynamics from Psychological Time Series

Idiographic modeling is rapidly gaining popularity, promising to tap into the within-person dynamics underlying psychological phenomena. To gain theoretical understanding of these dynamics, we need to make inferences from time series models about the underlying system. Such inferences are subject to two challenges: first, time series models will arguably always be misspecified, meaning it is unclear how to make inferences to the underlying system; and second, the sampling frequency must be sufficient to capture the dynamics of interest. We discuss both problems with the following approach: we specify a toy model for emotion dynamics as the true system, generate time series data from it, and then try to recover that system with the most popular time series analysis tools. We show that making straightforward inferences from time series models about an underlying system is difficult. We also show that if the sampling frequency is insufficient, the dynamics of interest cannot be recovered. However, we also show that global characteristics of the system can be recovered reliably. We conclude by discussing the consequences of our findings for idiographic modeling and suggest a modeling methodology that goes beyond fitting time series models alone and puts formal theories at the center of theory development.

A network approach can improve eating disorder conceptualization and treatment

Eating disorders are severe mental illnesses with the second highest mortality rate of all psychiatric illnesses. Eating disorders are exceedingly deadly because of their complexity. Specifically, eating disorders are highly comorbid with other psychiatric illnesses (up to 95% of individuals with an eating disorder have at least one additional psychiatric illness), have extremely heterogeneous presentations, and individuals often migrate from one specific eating disorder diagnosis to another. In this Perspective, we propose that understanding eating disorder comorbidity and heterogeneity via a network theory approach offers substantial benefits for both conceptualization and treatment. Such a conceptualization, strongly based on theory, can identify specific pathways that maintain psychiatric comorbidity, how diagnoses vary across individuals, and how specific symptoms and comorbidities maintain illness for one individual, thereby paving the way for personalized treatment.

When people die by suicide: Introducing unacceptable loss thresholds as a potential missing link between suicide readiness states and actively suicidal clinical states

INTRODUCTION

Contemporary models of suicide have largely overlooked why a person at high risk for suicide attempts suicide at a specific time. We propose the construct of unacceptable loss thresholds (i.e., a person's tolerance limit for a negative life event, which if violated results in an increase in suicide risk), which addresses many paradoxes in the literature related to suicide triggers. The aim of this paper is to provide preliminary proof of concept and to stimulate replication and further empirical study.

METHODS

We recruited an online community sample of individuals with a suicide attempt history (n = 144). These individuals answered questions about the time leading up to their most recent suicide attempt.

RESULTS

The majority (70.8% yes; 18.1% cannot remember; 11.1% no) reported creating a threshold of unacceptable loss, and that relatively small events were enough to trigger feelings that life was not worth living (63.9% yes; 30.6% maybe; 5.6% no). Further, the majority (57.6% yes; 21.5% yes, but only if asked; 20.8% - no) reported they would be willing to tell their therapist/doctor about their thresholds of unacceptable loss.

CONCLUSION

The construct of unacceptable loss deserves further empirical inquiry. Individuals contemplating suicide set them and if the loss occurs, it may trigger suicidal action in suicide ready individuals. Thresholds could provide risk assessment and safety planning data currently being overlooked.

Integrating a functional view on suicide risk into idiographic statistical models

Acute risk of death by suicide manifests in heightened suicidal ideation in certain contexts and time periods. These increases are thought to emerge from complex and mutually reinforcing relationships between dispositional vulnerability factors and individually suicidogenic short-term stressors. Together, these processes inform clinical safety planning and our therapeutic tools accommodate a reasonable degree of idiosyncrasy when we individualize interventions. Unraveling these multifaceted factors and processes on a quantitative level, however, requires estimation frameworks capable of representing idiosyncrasies relevant to intervention and psychotherapy. Using, data from a 21-day ambulatory assessment protocol that included six random prompts per day, we developed personalized (i.e., idiographic) models of interacting risk factors and suicidal ideation via Group Iterative Multiple Model Estimation (GIMME) in a sample of people diagnosed with borderline personality disorder (N = 95) stratified for a history of high lethality suicide attempts. Our models revealed high levels of heterogeneity in state risk factors related to suicidal ideation, with no features shared among the majority of participants or even among relatively homogenous clusters of participants (i.e., empirically derived subgroups). We discuss steps toward clinical implementation of personalized models, which can eventually capture suicidogenic changes in proximal risk factors and inform safety planning and interventions.

Evaluation of the cognitive behavioural theory of eating disorders: A network analysis investigation

OBJECTIVE

One of the prevailing theories of eating disorders (ED) is the transdiagnostic cognitive behavioural theory of eating disorders, which suggests that certain ED symptoms, such as over-valuation of eating, shape, and weight, may be more central than others. In the present study, network analyses were used to evaluate these assumptions in a patient sample.

METHODS

Participants were 336 individuals receiving treatment at an expert center for ED in the Netherlands. Eating disorder symptoms were used to create transdiagnostic and diagnosis-specific networks and assess symptom centrality and density of the networks.

RESULTS

Networks for patients with bulimia nervosa and binge eating disorder confirmed that over-valuation of shape, weight, and eating is the most central symptom in the network. A transdiagnostic network of ED symptoms and separate networks for patients with anorexia nervosa and bulimia nervosa showed that strict dieting was an additional central ED symptom. An exploratory analysis revealed that, although eating disorder symptoms decreased, there were no differences in density of the eating disorder networks before and after treatment with cognitive behavioural therapy.

DISCUSSION

In conclusion, the current study confirmed that over-valuation of shape, weight, and eating is a central symptom across eating disorders, in agreement with the transdiagnostic cognitive behavioural model of eating disorders. Specifically targeting this symptom in treatment could lead to other symptoms improving as a result.

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.

Intervening on psychopathology networks: Evaluating intervention targets through simulations

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.

Advancing urban mental health research: from complexity science to actionable targets for intervention

Urbanisation and common mental disorders (CMDs; ie, depressive, anxiety, and substance use disorders) are increasing worldwide. In this Review, we discuss how urbanicity and risk of CMDs relate to each other and call for a complexity science approach to advance understanding of this interrelationship. We did an ecological analysis using data on urbanicity and CMD burden in 191 countries. We found a positive, non-linear relationship with a higher CMD prevalence in more urbanised countries, particularly for anxiety disorders. We also did a review of meta-analytic studies on the association between urban factors and CMD risk. We identified factors relating to the ambient, physical, and social urban environment and showed differences per diagnosis of CMDs. We argue that factors in the urban environment are likely to operate as a complex system and interact with each other and with individual city inhabitants (including their psychological and neurobiological characteristics) to shape mental health in an urban context. These interactions operate on various timescales and show feedback loop mechanisms, rendering system behaviour characterised by non-linearity that is hard to predict over time. We present a conceptual framework for future urban mental health research that uses a complexity science approach. We conclude by discussing how complexity science methodology (eg, network analyses, system-dynamic modelling, and agent-based modelling) could enable identification of actionable targets for treatment and policy, aimed at decreasing CMD burdens in an urban context.

Moore swab performs equal to composite and outperforms grab sampling for SARS-CoV-2 monitoring in wastewater

Wastewater-based epidemiology (WBE) approaches to detect SARS-CoV-2 in municipal wastewater can provide unique information on the incidence or prevalence of COVID-19 in community. However, there are several technical challenges coupled with sewage sampling for SARS-CoV-2, including intermittent shedding of viruses, sampling time, volume, and frequency. Sampling schemes thus may need to be tailored to reach out highly sensitive, accurate, and reliable results. Herein, we compared the accuracy and threshold cycle (Ct) profiles of SARS-CoV-2 in Moore swabs, composite (16-h), and grab samples taken from sewage manholes (n = 17) at the Middle Eastern city of Tehran, Iran, on two occasions (November 2020 and May 2021). Samples were concentrated by polyethylene glycol precipitation and the corresponding Ct values for CDC 'N' and 'ORF1ab' assays were derived by means of real time RT-qPCR. Overall, the Moore swabs performed equal to samples composited over 16 h for qualitative monitoring, and 34/34 (100%) were positive for SARS-CoV-2. The 'N' assay showed the highest detection frequency as compared to 'ORF1ab'. The mean Moore swab Ct profiles were more consistent with 16 h composite sampling as compared with corresponding grab samples, providing hints as to the best sampling protocol to adopt when planning a sewage monitoring campaign particularly under WBE. Furthermore, our analyses on local differences showed somewhat higher virus copy numbers in the southern areas. The experimental design of this study revealed that the Moore swab and composite samples are more sensitive than grab samples, suggesting that the collection of grab samples may be inappropriate for characterizing total number of viral RNA copies in sewage samples. Given the transiently presence of human host-restricted infections such as SARS-CoV-2 and the simplicity and affordability of Moore swab, the method is well suited for disease surveillance in resource poor regions struggling with limited capacity for clinical testing.

Computational Psychiatry Needs Time and Context

Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting that modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The causal systems approach to prolonged grief: Recent developments and future directions

The network theory of prolonged grief posits that causal interactions among symptoms of prolonged grief play a significant role in their coherence and persistence as a syndrome. Drawing on recent developments in the broader network approach to psychopathology, we argue that advancing our understanding of the causal system that gives rise to prolonged grief will require that we (a) strengthen our assessment of each component of the grief syndrome, (b) investigate intra-individual relationships among grief components as they evolve over time within individuals, (c) incorporate biological and social components into network studies of grief, and (d) generate formal theories that posit precisely how these biological, psychological, and social components interact with one another to give rise to prolonged grief disorder.

Innovative methods for observing and changing complex health behaviors: four propositions

Precision health initiatives aim to progressively move from traditional, group-level approaches to health diagnostics and treatments toward ones that are individualized, contextualized, and timely. This article aims to provide an overview of key methods and approaches that can help facilitate this transition in the health behavior change domain. This article is a narrative review of the methods used to observe and change complex health behaviors. On the basis of the available literature, we argue that health behavior change researchers should progressively transition from (i) low- to high-resolution behavioral assessments, (ii) group-only to group- and individual-level statistical inference, (iii) narrative theoretical models to dynamic computational models, and (iv) static to adaptive and continuous tuning interventions. Rather than providing an exhaustive and technical presentation of each method and approach, this article articulates why and how researchers interested in health behavior change can apply these innovative methods. Practical examples contributing to these efforts are presented. If successfully adopted and implemented, the four propositions in this article have the potential to greatly improve our public health and behavior change practices in the near future.

Using clinical expertise and empirical data in constructing networks of trauma symptoms in refugee youth

Background: In recent years, many adolescents have fled their home countries due to war and human rights violations, consequently experiencing various traumatic events and putting them at risk of developing mental health problems. The symptomatology of refugee youth was shown to be multifaceted and often falling outside of traditional diagnoses. Objective: The present study aimed to investigate the symptomatology of this patient group by assessing the network structure of a wide range of symptoms. Further, we assessed clinicians' perceptions of symptoms relations in order to evaluate the clinical validity of the empirical network. Methods: Empirical data on Post-Traumatic Stress Disorder (PTSD), depression and other trauma symptoms from N = 366 refugee youth were collected during the routine diagnostic process of an outpatient centre for refugee youth in Germany. Additionally, four clinicians of this outpatient centre were asked how they perceive symptom relations in their patients using a newly developed tool. Separate networks were constructed based on 1) empirical symptom data and 2) clinicians' perceived symptom relations (PSR). Results: Both the network based on empirical data and the network based on clinicians' PSR showed that symptoms of PTSD and depression related most strongly within each respective cluster (connected mainly via sleeping problems), externalizing symptoms were somewhat related to PTSD symptoms and intrusions were central. Some differences were found within the clinicians' PSR as well as between the PSR and the empirical network. Still, the general PSR-network structure showed a moderate to good fit to the empirical data. Conclusion: Our results suggest that sleeping problems and intrusions play a central role in the symptomatology of refugee children, which has tentative implications for diagnostics and treatment. Further, externalizing symptoms might be an indicator for PTSD-symptoms. Finally, using clinicians' PSR for network construction offered a promising possibility to gain information on symptom networks and their clinical validity.

An Answer to "So What?" Implications of Network Theory for Research and Practice

Research and practice in psychiatry and clinical psychology have been guided by differing schools of thought over the years. Recently, the network theory of psychopathology has arisen as a framework for thinking about mental health. Network theory challenges three common assumptions: psychological problems are caused by disease entities that exist independently of their signs and symptoms, classification and diagnosis of psychological problems should follow a medical model, and psychological problems are caused by diseases or aberrations in the brain. Conversely, network theory embraces other assumptions that are well accepted in clinical practice (e.g., the interaction of thoughts, behaviors, and emotions, as posited in cognitive-behavioral therapies) and integrates those assumptions into a coherent framework for research and practice. In this article, the authors review developments in network theory by focusing on anxiety-related conditions, discuss future areas for change, and outline implications of network theory for research and clinical practice.

Integrating theory-based and data-driven methods to case conceptualization: A functional analysis approach with ecological momentary assessment

Objective Ecological momentary assessment (EMA) and network analysis are promising empirical developments for psychotherapy research and practice, but they lack a therapeutic rationale that could guide case conceptualization and treatment planning. We developed an assessment strategy that aims to assess functional analysis with EMA. Method: The assessment strategy was applied to a series of three N-of-1 assessments in a proof-of-concept study. After selecting a personalized set of items, EMA was implemented with three measurement time points per day for a period of 30 days. The participants evaluated feasibility and acceptance. Practicing psychotherapists discussed clinical implications in a focus group. Results: The implementation of the assessment strategy seemed feasible and accepted; participants did not report any side effects. Principal component and network analyses indicated interpretable components (e.g., participant 1: hopelessness, procrastination, coping, avoidance). The focus group pointed out potentials (e.g., efficient profit of the waiting time, empowering patients) and challenges (e.g., prioritize and interpret all the information). Conclusion: The presented assessment strategy may enhance the scientific quality of case conceptualization empowering therapists' decision-making regarding treatment planning. At the same time, it is a concrete demonstration of the challenges that need to be addressed in future research.

Person-specific networks in psychopathology: Past, present, and future

In the psychological network approach, mental disorders such as major depressive disorder are conceptualized as networks. The network approach focuses on the symptom structure or the connections between symptoms instead of the severity (i.e., mean level) of a symptom. To infer a person-specific network for a patient, time-series data are needed. By far the most common model to statistically model the person-specific interactions between symptoms or momentary states has been the vector autoregressive (VAR) model. Although the VAR model helps to bring psychological network theory into clinical research and closer to clinical practice, several discrepancies arise when we map the psychological network theory onto the VAR-based network models. These challenges and possible solutions are discussed in this review.

What Research Evidence Is Valid for Psychotherapy Research?

Evidence-Based Medicine (EBM) have contributed to improved clinical practice with increased use of effective and life-saving treatments for severe diseases. However, the EBM model is less suitable for psychotherapy research than for pharmacological research and somatic medicine. The randomized controlled trial (RCT) design is an example of experimental methodology, which inevitably has more imperfections in psychotherapy research because psychotherapy RCTs cannot use double-blinding and the treatments tested are composite treatment packages. Long-term psychotherapy for severe and complex mental disorders is especially difficult to study with an RCT design. During the last decades, advanced analytic methods have been developed in psychotherapy process research, which enables investigation of causal connections regarding change mechanisms in psychotherapy. Therefore, we propose that the top of the research evidence hierarchy for psychotherapy should encompass: (1) RCT for circumscribed disorders, (2) cohort studies for complex disorders, and (3) advanced process studies for change mechanisms.

Single-Subject Research in Psychiatry: Facts and Fictions

Scientific evidence in the field of psychiatry is mainly derived from group-based ("nomothetic") studies that yield group-aggregated results, while often the need is to answer questions that apply to individuals. Particularly in the presence of great inter-individual differences and temporal complexities, information at the individual-person level may be valuable for personalized treatment decisions, individual predictions and diagnostics. The single-subject study design can be used to make inferences about individual persons. Yet, the single-subject study is not often used in the field of psychiatry. We believe that this is because of a lack of awareness of its value rather than a lack of usefulness or feasibility. In the present paper, we aimed to resolve some common misconceptions and beliefs about single-subject studies by discussing some commonly heard "facts and fictions." We also discuss some situations in which the single-subject study is more or less appropriate, and the potential of combining single-subject and group-based study designs into one study. While not intending to plea for single-subject studies at the expense of group-based studies, we hope to increase awareness of the value of single-subject research by informing the reader about several aspects of this design, resolving misunderstanding, and providing references for further reading.

Early warning signals in psychopathology: what do they tell?

BACKGROUND

Despite the increasing understanding of factors that might underlie psychiatric disorders, prospectively detecting shifts from a healthy towards a symptomatic state has remained unattainable. A complex systems perspective on psychopathology implies that such symptom shifts may be foreseen by generic indicators of instability, or early warning signals (EWS). EWS include, for instance, increasing variability, covariance, and autocorrelation in momentary affective states-of which the latter was studied. The present study investigated if EWS predict (i) future worsening of symptoms as well as (ii) the type of symptoms that will develop, meaning that the association between EWS and future symptom shifts would be most pronounced for congruent affective states and psychopathological domains (e.g., feeling down and depression).

METHODS

A registered general population cohort of adolescents (mean age 18 years, 36% male) provided ten daily ratings of their affective states for 6 consecutive days. The resulting time series were used to compute EWS in feeling down, listless, anxious, not relaxed, insecure, suspicious, and unwell. At baseline and 1-year follow-up, symptom severity was assessed by the Symptom Checklist-90 (SCL-90). We selected four subsamples of participants who reported an increase in one of the following SCL-90 domains: depression (N = 180), anxiety (N = 192), interpersonal sensitivity (N = 184), or somatic complaints (N = 166).

RESULTS

Multilevel models showed that EWS in feeling suspicious anticipated increases in interpersonal sensitivity, as hypothesized. EWS were absent for other domains. While the association between EWS and symptom increases was restricted to the interpersonal sensitivity domain, post hoc analyses showed that symptom severity at baseline was related to heightened autocorrelations in congruent affective states for interpersonal sensitivity, depression, and anxiety. This pattern replicated in a second, independent dataset.

CONCLUSIONS

The presence of EWS prior to symptom shifts may depend on the dynamics of the psychopathological domain under consideration: for depression, EWS may manifest only several weeks prior to a shift, while for interpersonal sensitivity, EWS may already occur 1 year in advance. Intensive longitudinal designs where EWS and symptoms are assessed in real-time are required in order to determine at what timescale and for what type of domain EWS are most informative of future psychopathology.

Complexity in psychological self-ratings: implications for research and practice

BACKGROUND

Psychopathology research is changing focus from group-based "disease models" to a personalized approach inspired by complex systems theories. This approach, which has already produced novel and valuable insights into the complex nature of psychopathology, often relies on repeated self-ratings of individual patients. So far, it has been unknown whether such self-ratings, the presumed observables of the individual patient as a complex system, actually display complex dynamics. We examine this basic assumption of a complex systems approach to psychopathology by testing repeated self-ratings for three markers of complexity: memory, the presence of (time-varying) short- and long-range temporal correlations; regime shifts, transitions between different dynamic regimes; and sensitive dependence on initial conditions, also known as the "butterfly effect," the divergence of initially similar trajectories.

METHODS

We analyzed repeated self-ratings (1476 time points) from a single patient for the three markers of complexity using Bartels rank test, (partial) autocorrelation functions, time-varying autoregression, a non-stationarity test, change point analysis, and the Sugihara-May algorithm.

RESULTS

Self-ratings concerning psychological states (e.g., the item "I feel down") exhibited all complexity markers: time-varying short- and long-term memory, multiple regime shifts, and sensitive dependence on initial conditions. Unexpectedly, self-ratings concerning physical sensations (e.g., the item "I am hungry") exhibited less complex dynamics and their behavior was more similar to random variables.

CONCLUSIONS

Psychological self-ratings display complex dynamics. The presence of complexity in repeated self-ratings means that we have to acknowledge that (1) repeated self-ratings yield a complex pattern of data and not a set of (nearly) independent data points, (2) humans are "moving targets" whose self-ratings display non-stationary change processes including regime shifts, and (3) long-term prediction of individual trajectories may be fundamentally impossible. These findings point to a limitation of popular statistical time series models whose assumptions are violated by the presence of these complexity markers. We conclude that a complex systems approach to mental health should appreciate complexity as a fundamental aspect of psychopathology research by adopting the models and methods of complexity science. Promising first steps in this direction, such as research on real-time process monitoring, short-term prediction, and just-in-time interventions, are discussed.

Advancing the Understanding of Suicide: The Need for Formal Theory and Rigorous Descriptive Research

Suicide is a leading cause of death worldwide and perhaps the most puzzling and devastating of all human behaviors. Suicide research has primarily been guided by verbal theories containing vague constructs and poorly specified relationships. We propose two fundamental changes required to move toward a mechanistic understanding of suicide. First, we must formalize theories of suicide, expressing them as mathematical or computational models. Second, we must conduct rigorous descriptive research, prioritizing direct observation and precise measurement of suicidal thoughts and behaviors and of the factors posited to cause them. Together, theory formalization and rigorous descriptive research will facilitate abductive theory construction and strong theory testing, thereby improving the understanding and prevention of suicide and related behaviors.

Systems all the way down: embracing complexity in mental health research

In this editorial for the collection on complexity in mental health research, we introduce and summarize the inaugural contributions to this collection: a series of theoretical, methodological, and empirical papers that aim to chart a path forward for investigating mental health in all its complexity. A central theme emerges from these contributions: if we are to make genuine progress in explaining, predicting, and treating mental illness, we must study the systems from which psychopathology emerges. As the articles in this collection make clear, the systems that give rise to psychopathology encompass a host of components across biological, psychological, and social levels of analysis, intertwined in a web of complex interactions. The task of advancing our understanding of these systems will be a challenging one. Yet, this challenge presents a unique opportunity. From physics to ecology, there is a rapidly evolving body of interdisciplinary research dedicated to investigating complex systems. This work provides clear guidance for psychiatric research, opportunities for collaboration, and a set of tools and concepts from which we can draw in our efforts to understand mental health, helping us move toward our ultimate aim of improving the prevention and treatment of psychopathology.

A complex systems approach to the study of change in psychotherapy

BACKGROUND

A growing body of research highlights the limitations of traditional methods for studying the process of change in psychotherapy. The science of complex systems offers a useful paradigm for studying patterns of psychopathology and the development of more functional patterns in psychotherapy. Some basic principles of change are presented from subdisciplines of complexity science that are particularly relevant to psychotherapy: dynamical systems theory, synergetics, and network theory. Two early warning signs of system transition that have been identified across sciences (critical fluctuations and critical slowing) are also described. The network destabilization and transition (NDT) model of therapeutic change is presented as a conceptual framework to import these principles to psychotherapy research and to suggest future research directions.

DISCUSSION

A complex systems approach has a number of implications for psychotherapy research. We describe important design considerations, targets for research, and analytic tools that can be used to conduct this type of research.

CONCLUSIONS

A complex systems approach to psychotherapy research is both viable and necessary to more fully capture the dynamics of human change processes. Research to date suggests that the process of change in psychotherapy can be nonlinear and that periods of increased variability and critical slowing might be early warning signals of transition in psychotherapy, as they are in other systems in nature. Psychotherapy research has been limited by small samples and infrequent assessment, but ambulatory and electronic methods now allow researchers to more fully realize the potential of concepts and methods from complexity science.

Why Do We Need Computational Models of Psychological Change and Recovery, and How Should They Be Designed and Tested?

Traditional research methodologies typically assume that humans operate on the basis of an "open loop" stimulus-process-response rather than the "closed loop" control of internal state. They also average behavioral data across repeated measures rather than assess it continuously, and they draw inferences about the working of an individual from statistical group effects. As such, we propose that they are limited in their capacity to accurately identify and test for the mechanisms of change within psychological therapies. As a solution, we explain the advantages of using a closed loop functional architecture, based on an extended homeostatic model of the brain, to construct working computational models of individual clients that can be tested against real-world data. Specifically, we describe tests of a perceptual control theory (PCT) account of psychological change that combines the components of negative feedback control, hierarchies, conflict, reorganization, and awareness into a working model of psychological function, and dysfunction. In brief, psychopathology is proposed to be the loss of control experienced due to chronic, unresolved conflict between important personal goals. The mechanism of change across disorders and different psychological therapies is proposed to be the capacity for the therapist to help the client shift and sustain their awareness on the higher level goals that are driving goal conflict, for sufficiently long enough to permit a trial-and-error learning process, known as reorganization, to "stumble" upon a solution that regains control. We report on data from studies that have modeled these components both separately and in combination, and we describe the parallels with human data, such as the pattern of early gains and sudden gains within psychological therapy. We conclude with a description of our current research program that involves the following stages: (1) construct a model of the conflicting goals that are held by people with specific phobias; (2) optimize a model for each individual using their dynamic movement data from a virtual reality exposure task (VRET); (3) construct and optimize a learning parameter (reorganization) within each model using a subsequent VRET; (3) validate the model of each individual against a third VRET. The application of this methodology to robotics, attachment dynamics in childhood, and neuroimaging is discussed.