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

Exploring Temporal Relationships Between Anxiety, Mood and Mental Imagery in Patients With Bipolar Disorder: A Network Analysis

INTRODUCTION

Bipolar disorder is a severe mental health problem with limited treatment success. There is a call for improving interventions, requiring an increased understanding of factors driving mood instability. One promising avenue is to study temporal associations between factors that appear relevant according to the emotional amplifier model of Holmes are changes in mood, anxiety and mental imagery.

METHODS

The current study used data from a recent RCT for a secondary analysis which applied a network analysis approach to explore temporal associations between weekly measurements of mania, depression, anxiety and mental imagery measured during 32 weeks in two randomised groups (N = 55) receiving either imagery-focused cognitive therapy (ImCT) or group psychoeducation (PE).

RESULTS

Both negative intrusive mental imagery and anxiety appeared central in the network analyses, driving changes in both mania and depression, but only in the PE group. In the ImCT group, only anxiety was driving changes in mania and depression.

CONCLUSION

Although exploratory, findings suggest that prior increases in anxiety and negative intrusive mental imagery might be associated with subsequent increases in depression and mania symptoms in patients with bipolar disorder. Anxiety might in turn increase negative intrusive imagery and associated negative emotions. Although more research is needed, results are in line with the emotional amplifier model and stress that future interventions with a focus on anxiety and imagery might help to improve psychosocial therapies for patients with bipolar disorder. In addition, this study suggests that a network approach is a helpful and feasible way to study mood instability, anxiety and mental imagery to increase our understanding of mechanisms underpinning mood instability.

Unravelling inter-relations within and between psychodynamic constructs and psychopathology using network analysis

Psychodynamic constructs and psychopathology are closely inter-related, but more detailed insight is needed. We investigated these complex inter-relations using network analysis. A Gaussian graphical model in a sample of N = 2232 psychotherapeutic inpatients was estimated. Self-administered questionnaires to assess interpersonal relations (Inventory of Interpersonal Problems-32), psychodynamic conflicts (Operationalized Psychodynamic Diagnosis-Conflict Questionnaire), personality functioning (Operationalized Psychodynamic Diagnosis-Structure Questionnaire-Short Form, Inventory of Personality Organization-Short Form), and psychopathology (Brief Symptom Inventory) were utilized. We investigated the network structure, identified the most inter-related psychodynamic constructs and the psychodynamic constructs with the strongest inter-relations to psychopathology, and explored the clustering of all included constructs. Active and passive conflict processing modes were negatively inter-related in most conflicts. Passive conflict processing modes were more strongly related to psychopathology than active ones in all conflicts, apart from the care versus autarky conflict. Identity diffusion shared the strongest inter-relations within psychodynamic constructs. The psychodynamic constructs that were most strongly related to psychopathology were impairments in self-perception and the passive self-worth conflict. Psychopathology and psychodynamic constructs formed distinct clusters. Our results emphasize the relevance of personality functioning within psychodynamic constructs and in relation to psychopathology.

Personality Functioning, Problems in Living, and Personality Traits

The publication of the Alternative Model of Personality Disorder (AMPD) was a signpost achievement in the personality assessment. However, research on the AMPD has generally not led to either a deeper understanding of personality disorder or personality assessment or new ideas about how to provide better care for people with personality disorder diagnoses. A significant portion of research has focused on narrow issues and appears to be driven in part by ideological differences between scholars who prefer Criterion A (personality functioning) or Criterion B (maladaptive traits). I trace these issues to ambiguity about the concept of personality functioning as defined in the AMPD and its conceptual distinction from personality traits and problems in living. In this paper, I reground these concepts in coherent and distinct definitions, elaborate upon the implications of their differences, and show how these differences can help clarify and reorient AMPD research to focus on generating clinically useful models for personality pathology and personality assessment.

Routine Outcome Monitoring and Clinical Feedback in Psychotherapy: Recent Advances and Future Directions

In the past decade, there has been an increase in research related to the routine collection and active use of standardized patient data in psychotherapy. Research has increasingly focused on personalization of care to patients, clinical skills and interventions that modulate treatment outcomes, and implementation strategies, all of which appear to enhance the beneficial effects of ROM and feedback. In this article, we summarize trends and recent advances in the research on this topic and identify several essential directions for the field in the short to medium term. We anticipate a broadening of research from the focus on average effects to greater specificity around what kinds of feedback, provided at what time, to which individuals, in what settings, are most beneficial. We also propose that the field needs to focus on issues of health equity, ensuring that ROM can be a vehicle for increased wellbeing for those who need it most. The complexity of mental healthcare systems means that there may be multiple viable measurement solutions with varying costs and benefits to diverse stakeholders in different treatment contexts, and research is needed to identify the most influential components in each of these contexts.

Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data

BACKGROUND

As depression is highly heterogenous, an increasing number of studies investigate person-specific associations of depressive symptoms in longitudinal data. However, most studies in this area of research conceptualize symptom interrelations to be static and time invariant, which may lead to important temporal features of the disorder being missed.

OBJECTIVE

To reveal the dynamic nature of depression, we aimed to use a recently developed technique to investigate whether and how associations among depressive symptoms change over time.

METHODS

Using daily data (mean length 274, SD 82 d) of 20 participants with depression, we modeled idiographic associations among depressive symptoms, rumination, sleep, and quantity and quality of social contacts as dynamic networks using time-varying vector autoregressive models.

RESULTS

The resulting models showed marked interindividual and intraindividual differences. For some participants, associations among variables changed in the span of some weeks, whereas they stayed stable over months for others. Our results further indicated nonstationarity in all participants.

CONCLUSIONS

Idiographic symptom networks can provide insights into the temporal course of mental disorders and open new avenues of research for the study of the development and stability of psychopathological processes.

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.

Intraindividual time-varying dynamic network of affects: linear autoregressive mixed-effects models for ecological momentary assessment

An interesting recent development in emotion research and clinical psychology is the discovery that affective states can be modeled as a network of temporally interacting moods or emotions. Additionally, external factors like stressors or treatments can influence the mood network by amplifying or dampening the activation of specific moods. Researchers have turned to multilevel autoregressive models to fit these affective networks using intensive longitudinal data gathered through ecological momentary assessment. Nonetheless, a more comprehensive examination of the performance of such models is warranted. In our study, we focus on simple directed intraindividual networks consisting of two interconnected mood nodes that mutually enhance or dampen each other. We also introduce a node representing external factors that affect both mood nodes unidirectionally. Importantly, we disregard the potential effects of a current mood/emotion on the perception of external factors. We then formalize the mathematical representation of such networks by exogenous linear autoregressive mixed-effects models. In this representation, the autoregressive coefficients signify the interactions between moods, while external factors are incorporated as exogenous covariates. We let the autoregressive and exogenous coefficients in the model have fixed and random components. Depending on the analysis, this leads to networks with variable structures over reasonable time units, such as days or weeks, which are captured by the variability of random effects. Furthermore, the fixed-effects parameters encapsulate a subject-specific network structure. Leveraging the well-established theoretical and computational foundation of linear mixed-effects models, we transform the autoregressive formulation to a classical one and utilize the existing methods and tools. To validate our approach, we perform simulations assuming our model as the true data-generating process. By manipulating a predefined set of parameters, we investigate the reliability and feasibility of our approach across varying numbers of observations, levels of noise intensity, compliance rates, and scalability to higher dimensions. Our findings underscore the challenges associated with estimating individualized parameters in the context of common longitudinal designs, where the required number of observations may often be unattainable. Moreover, our study highlights the sensitivity of autoregressive mixed-effect models to noise levels and the difficulty of scaling due to the substantial number of parameters.

Emotion dynamics of clients with test anxiety before and after an imagery-based treatment

BACKGROUND AND OBJECTIVES

Imagery-based techniques have become a promising means in the treatment of test anxiety (TA). Although previous studies have demonstrated the effectiveness of imagery-based treatment, not all clients seem to benefit from it. The present study compares clients' pre- as well as post-treatment emotion dynamics between responders and non-responders. Furthermore, it examines treatment-related changes in emotion dynamics in both subgroups.

METHODS

The results are based on 44 clients suffering from TA who underwent a six-session imagery-based treatment and include Ecological Momentary Assessment (EMA). Emotions were assessed with the Profile of Mood States four times a day over the course of two weeks before and after the treatment. Temporal networks were computed to index emotion dynamics.

RESULTS

Pre-treatment emotion dynamics differed between responders and non-responders. Similarly, post-treatment emotion dynamics differed as well between both groups. Some changes were also observed between pre-treatment and post-treatment networks: for responders, fatigue no longer predicted anger, and depression predicted itself; for non-responders, calmness predicted fatigue, anger, depression, contentment, and anxiety. In addition, fatigue no longer predicted itself and anxiety predicted vigor.

LIMITATIONS

The investigation is marked by several limitations: a liberal inclusion threshold of at least a 50% response to EMA prompts, and a relatively homogenous sample.

CONCLUSION

These results provide first evidence for the idea that emotion dynamics may be associated with response to treatment for TA. Furthermore, effective imagery-based treatments may be tied to changes within these dynamics.

A novel approach for constructing personalized networks from longitudinal perceived causal relations

Personalized networks of psychological symptoms aim to advance therapy by identifying treatment targets for specific patients. Statistical relations in such networks can be estimated from intensive longitudinal data, but their causal interpretation is limited by strong statistical assumptions. An alternative is to create networks from patient perceptions, which comes with other limitations such as retrospective bias. We introduce the Longitudinal Perceived Causal Problem Networks (L-PECAN) approach to address both these concerns. 20 participants screening positive for depression completed 4 weeks day of brief daily assessments of perceived symptom interactions. Quality criteria of this new method are introduced, answering questions such as "Which symptoms should be included in networks?", "How many datapoints need to be collected to achieve stable networks?", and "Does the network change over time?". Accordingly, about 40% of respondents achieved stable networks and only few respondents exhibited network structure that changed during the assessment period. The method was time-efficient (on average 7.4 min per day), and well received. Overall, L-PECAN addresses several of the prevailing issues found in statistical networks and therefore provides a clinically meaningful method for personalization.

Estimating the prognostic value of cross-sectional network connectivity for treatment response in depression

BACKGROUND

Tightly connected symptom networks have previously been linked to treatment resistance, but most findings come from small-sample studies comparing single responder v. non-responder networks. We aimed to estimate the association between baseline network connectivity and treatment response in a large sample and benchmark its prognostic value against baseline symptom severity and variance.

METHODS

N = 40 518 patients receiving treatment for depression in routine care in England from 2015-2020 were analysed. Cross-sectional networks were constructed using the Patient Health Questionnaire-9 (PHQ-9) for responders and non-responders (N = 20 259 each). To conduct parametric tests investigating the contribution of PHQ-9 sum score mean and variance to connectivity differences, networks were constructed for 160 independent subsamples of responders and non-responders (80 each, n = 250 per sample).

RESULTS

The baseline non-responder network was more connected than responders (3.15 v. 2.70, S = 0.44, p < 0.001), but effects were small, requiring n = 750 per group to have 85% power. Parametric analyses revealed baseline network connectivity, PHQ-9 sum score mean, and PHQ-9 sum score variance were correlated (r = 0.20-0.58, all p < 0.001). Both PHQ-9 sum score mean (β = -1.79, s.e. = 0.07, p < 0.001), and PHQ-9 sum score variance (β = -1.67, s.e. = 0.09, p < 0.001) had larger effect sizes for predicting response than connectivity (β = -1.35, s.e. = 0.12, p < 0.001). The association between connectivity and response disappeared when PHQ-9 sum score variance was accounted for (β = -0.28, s.e. = 0.19, p = 0.14). We replicated these results in patients completing longer treatment (8-12 weeks, N = 22 952) and using anxiety symptom networks (N = 70 620).

CONCLUSIONS

The association between baseline network connectivity and treatment response may be largely due to differences in baseline score variance.

Understanding the complexity of individual developmental pathways: A primer on metaphors, models, and methods to study resilience in development

The modern study of resilience in development is conceptually based on a complex adaptive system ontology in which many (intersystem) factors are involved in the emergence of resilient developmental pathways. However, the methods and models developed to study complex dynamical systems have not been widely adopted, and it has recently been noted this may constitute a problem moving the field forward. In the present paper, I argue that an ontological commitment to complex adaptive systems is not only possible, but highly recommended for the study of resilience in development. Such a commitment, however, also comes with a commitment to a different causal ontology and different research methods. In the first part of the paper, I discuss the extent to which current research on resilience in development conceptually adheres to the complex systems perspective. In the second part, I introduce conceptual tools that may help researchers conceptualize causality in complex systems. The third part discusses idiographic methods that could be used in a research program that embraces the interaction dominant causal ontology and idiosyncratic nature of the dynamics of complex systems. The conclusion is that a strong ontological commitment is warranted, but will require a radical departure from nomothetic science.

Which symptom to address in psychological treatment for cancer survivors when fear of cancer recurrence, depressive symptoms, and cancer-related fatigue co-occur? Exploring the level of agreement between three systematic approaches to select the focus of treatment

PURPOSE

To investigate the extent to which three systematic approaches for prioritizing symptoms lead to similar treatment advices in cancer survivors with co-occurring fear of cancer recurrence, depressive symptoms, and/or cancer-related fatigue.

METHODS

Psychological treatment advices were was based on three approaches: patient preference, symptom severity, and temporal precedence of symptoms based on ecological momentary assessments. The level of agreement was calculated according to the Kappa statistic.

RESULTS

Overall, we found limited agreement between the three approaches. Pairwise comparison showed moderate agreement between patient preference and symptom severity. Most patients preferred treatment for fatigue. Treatment for fear of cancer recurrence was mostly indicated when based on symptom severity. Agreement between temporal precedence and the other approaches was slight. A clear treatment advice based on temporal precedence was possible in 57% of cases. In cases where it was possible, all symptoms were about equally likely to be indicated.

CONCLUSIONS

The three approaches lead to different treatment advices. Future research should determine how the approaches are related to treatment outcome. We propose to discuss the results of each approach in a shared decision-making process to make a well-informed and personalized decision with regard to which symptom to target in psychological treatment.

IMPLICATIONS FOR CANCER SURVIVORS

This study contributes to the development of systematic approaches for selecting the focus of psychological treatment in cancer survivors with co-occurring symptoms by providing and comparing three different systematic approaches for prioritizing symptoms.

How are psychodynamic conflicts associated with personality functioning? A network analysis

Personality functioning and psychodynamic conflicts are central constructs in psychoanalytic theories of psychopathology as well as in many psychodynamic treatment models. Although there has been a longstanding conceptual discussion on how they relate to each other, empirical evidence on this question is still scarce. In this study, we explore the associations between psychodynamic conflicts and levels of structural integration (which can be used synonymously with personality functioning) by means of a partial correlation network analysis in a sample of N = 220 outpatients interviewed and rated according to Operationalized Psychodynamic Diagnosis (OPD-2). We examined network centrality, bridge centrality, clustering, and network stability. The network analysis resulted in separate clusters for levels of structural integration and conflicts, supporting the assumption of distinct psychodynamic constructs. The greatest association between the two clusters was found between the individuation vs. dependency conflict (C1) and the structural capacity to attach to internal objects. In general, C1 showed significantly greater connections with structural dimensions compared to the other five OPD conflicts included. C1 was also more central in the network compared to most other conflicts, whereas the structural dimensions did not differ in centrality. All structural dimensions were found to be strongly interconnected. C1 showed exclusively negative edges to the other conflicts, suggesting that a profound C1 decreases the probability of other psychodynamic conflicts. We discuss clinical as well as conceptual implications of our findings for psychodynamic diagnosis and treatment.

A Pilot, Time-Series Investigation of Depression, Anxiety, and Eating Disorder Symptoms in Adults Experiencing Major Depressive Symptoms: The Need for Eating Disorder Assessment and Research in Depression

Major Depressive Disorder (MDD) is a prevalent psychiatric disorder impacting 10-16% of Americans in their lifetime. Approximately 60% of individuals with MDD have comorbid anxiety disorders. Additionally, although scarce research has examined eating disorders (EDs) in depression, a bidirectional association exists between ED and MDD symptoms. The current pilot study (N = 31 individuals with moderate to severe depression) modeled networks of depressive, anxiety, and ED symptoms using intensive time-series data. This study also tested if temporal central symptoms predicted six-month clinical outcomes. The most central symptoms were guilt, self-dislike, lack of energy, and difficulty concentrating. Several anxiety and ED symptoms were also central, including physical anxiety, social anxiety, body dissatisfaction, and desire for thinness. The central symptom crying predicted six-month depression with a medium effect size. These findings suggest anxiety and ED symptoms may influence the day-to-day course of depression in some individuals with comorbid diagnoses, but predictors of symptoms across hours may differ from predictors across longer time scales (i.e., months). Time scale should be considered when conducting and interpreting research on MDD. Research, assessment, and treatment for MDD should continue to explore transdiagnostic approaches including anxiety and ED symptoms to optimize care for individuals with complex presentations.

Restoring the missing person to personalized medicine and precision psychiatry

Precision psychiatry has emerged as part of the shift to personalized medicine and builds on frameworks such as the U.S. National Institute of Mental Health Research Domain Criteria (RDoC), multilevel biological "omics" data and, most recently, computational psychiatry. The shift is prompted by the realization that a one-size-fits all approach is inadequate to guide clinical care because people differ in ways that are not captured by broad diagnostic categories. One of the first steps in developing this personalized approach to treatment was the use of genetic markers to guide pharmacotherapeutics based on predictions of pharmacological response or non-response, and the potential risk of adverse drug reactions. Advances in technology have made a greater degree of specificity or precision potentially more attainable. To date, however, the search for precision has largely focused on biological parameters. Psychiatric disorders involve multi-level dynamics that require measures of phenomenological, psychological, behavioral, social structural, and cultural dimensions. This points to the need to develop more fine-grained analyses of experience, self-construal, illness narratives, interpersonal interactional dynamics, and social contexts and determinants of health. In this paper, we review the limitations of precision psychiatry arguing that it cannot reach its goal if it does not include core elements of the processes that give rise to psychopathological states, which include the agency and experience of the person. Drawing from contemporary systems biology, social epidemiology, developmental psychology, and cognitive science, we propose a cultural-ecosocial approach to integrating precision psychiatry with person-centered care.

The connections of physical and psychosocial symptoms among patients with terminal illnesses: A network analysis

BACKGROUND

Patients with terminal illnesses have different symptoms. The goal of palliative care is to alleviate the suffering induced by these symptoms. Available research on symptoms has focused on their prevalence or identification of clusters. Little is known about the central symptoms contributing to the maintenance of the whole system of symptoms or the mechanism of influence between symptoms, particularly between physical and psychosocial symptoms.

AIM

The study used network analysis to explore symptom clusters among patients with terminal illnesses and identify the central symptoms, the bridge symptoms, and pathways between physical and psychosocial symptoms.

DESIGN AND SETTING/PARTICIPANTS

This is a retrospective cohort study using baseline symptom assessment of 677 adults with terminal illnesses receiving care from the Integrated Community End-of-Life Care Support Teams.

RESULTS

Three symptom clusters were identified: physical, psychological, and practical clusters. "Weakness or lack of energy" was identified as the crucial symptom affecting the changes of other symptoms, followed by depression and anxiety. Three connections between the psychological and physical symptoms were identified: anxiety-pain, depression-constipation, and perceived family anxiety-poor appetite pathways. Pathways of practical concern-peace and practical concern-depression were indicated between practical and psychological symptoms.

CONCLUSIONS

This study is the first of its kind to investigate the connections and mechanism of influence between symptoms among patients with terminal illnesses. It offers clear pathways for intervention with the most influential symptoms of weakness, anxiety, and depression.

Daily dynamics and mechanisms of anxious symptomatology in the general population: A network study during the COVID-19 pandemic

To understand the interplay between anxiety symptoms and their maintaining psychological processes in the population, an analysis of longitudinal within-person relationships is required. A sample of 1706 individuals completed daily measures during a 40-day period with strict mitigation protocols. Data of 1368 individuals who completed at least 30 assessments were analyzed with the multilevel vector autoregressive (mlVAR) model. This model estimates a temporal, a contemporaneous, and a between-person network. Uncontrollability of worry, generalized worry, fear of being infected, fear of significant others being infected, and threat monitoring had the highest outstrength within the temporal network, indicating that daily fluctuations in these components were the most predictive of next-day fluctuations in other components. Of specific connections, both fear of self and fear of close others being infected predicted generalized worry and threat monitoring. In turn, generalized worry and threat monitoring engaged in several positive feedback loops with other anxiety symptoms and processes. Also, intolerance of uncertainty was predictive of other components. The findings align with the mechanisms both in the metacognitive therapy (MCT) model and in the intolerance of uncertainty model of generalized anxiety disorder (GAD).

Dynamics of daily positive and negative affect and relations to anxiety and depression symptoms in a transdiagnostic clinical sample

BACKGROUND

Despite interest in transdiagnostic dimensional approaches to psychopathology, little is known about the dynamic interplay of affecting and internalizing symptoms that cut across diverse mental health disorders. We examined within-person reciprocal effects of negative and positive affect (NA, PA) and symptoms (depression and anxiety), and their between-person associations with affective dynamics (i.e., affect inertia).

METHODS

Individuals currently receiving treatment for psychological disorders (N = 776) completed daily assessments of affect and symptoms across 14 treatment days (average). We used dynamic structural equation modeling to examine daily affect-symptom dynamics.

RESULTS

Within-person results indicated NA-symptom reciprocal effects; PA only predicted subsequent depression symptoms. After accounting for changes in mean symptoms and affect over time, NA-anxiety and PA-depression relations remained particularly robust. Between-person correlations indicated NA inertia was positively associated with NA-symptom effects; PA inertia was negatively associated with PA-symptoms effects.

CONCLUSIONS

Results suggest that transdiagnostic affective treatment approaches may be more useful for reducing internalizing symptoms by decreasing NA compared to increasing PA. Individual differences in resistance to shifting out of affective states (i.e., high NA vs. PA inertia) may be a useful marker for developing tailored interventions.

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.

Feasibility, usability and clinical value of intensive longitudinal diary assessments in older persons with cognitive impairment and depressive symptoms

OBJECTIVES

To evaluate the feasibility, usability and clinical value of daily diary assessments combined with actigraphy in older persons with cognitive impairment.

METHODS

For 63 days, patients ≥60 years with cognitive impairments filled out a daily diary (including standardized questionnaires and cognitive test battery), and wore an actiwatch (sleep). After the study, participants and clinicians received personal feedback about patterns and daily triggers of depressive symptoms, sleep and cognitive performance. We assessed feasibility (participation rate, compliance and subjective burden), usability (variability and floor- or ceiling effects) and clinical value for patients and their clinicians (questionnaires).

RESULTS

Of 96 eligible patients, 13 agreed to participate (13.5%). One patient dropped out after 2 days, another after 37 days, and another did not complete the cognitive test battery. Compliance rate was high (6.7-10% missing values). Subjective burden was relatively low. Time-series data showed sufficient variability and no floor- or ceiling effects, except for one relevant ceiling effect on the One Back task. The personal feedback report was considered insightful by 4 out of 11 participants and 5 out of 7 clinicians.

CONCLUSION

Daily assessments are suitable for a minority of cognitively impaired older persons, but is helpful to increase insight into their symptoms.

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.

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.

Multisource Network and Latent Variable Models of Sluggish Cognitive Tempo, ADHD-Inattentive, and Depressive Symptoms with Spanish Children: Equivalent Findings and Recommendations

Multisource network and latent variable models were used to examine the construct validity of sluggish cognitive tempo (SCT) symptoms relative to attention-deficit/hyperactivity disorder-inattentive (ADHD-IN) and depressive symptoms. The five objectives were to determine the (1) distinctiveness of SCT, ADHD-IN, and depressive symptom communities, (2) similarity of the three symptom communities across mother, father, and teacher ratings, (3) individual symptoms with the strongest influence on other symptoms, (4) individual symptoms with the strongest relations to academic and social impairment, and (5) similarity between network and latent variable model results. Mothers, fathers, and teachers rated SCT, ADHD-IN, and depressive symptoms for 2,142 Spanish children (49.49% girls, ages 8-13 years, third to sixth grade). Walktrap community analysis resulted in SCT, ADHD-IN, and depressive symptom communities with three SCT symptom communities within the overall SCT symptom community (daydreams, mental confusion, and hypoactive communities). The symptom networks were also similar across mothers, fathers, and teachers, especially mothers and fathers. Finally, for all three sources, the same two SCT and two ADHD-IN symptoms showed unique relations with academic impairment and the same depressive symptom showed unique relations with social impairment. A latent variable model yielded equivalent results. Both models thus supported the validity of SCT symptoms relative to ADHD-IN and depressive symptoms. Complexities are noted in the selection of network and latent variable models to study child and adolescent psychopathology with recommendations for their selection.

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.

Measurement-Based and Data-Informed Psychological Therapy

Outcome measurement in the field of psychotherapy has developed considerably in the last decade. This review discusses key issues related to outcome measurement, modeling, and implementation of data-informed and measurement-based psychological therapy. First, an overview is provided, covering the rationale of outcome measurement by acknowledging some of the limitations of clinical judgment. Second, different models of outcome measurement are discussed, including pre-post, session-by-session, and higher-resolution intensive outcome assessments. Third, important concepts related to modeling patterns of change are addressed, including early response, dose-response, and nonlinear change. Furthermore, rational and empirical decision tools are discussed as the foundation for measurement-based therapy. Fourth, examples of clinical applications are presented, which show great promise to support the personalization of therapy and to prevent treatment failure. Finally, we build on continuous outcome measurement as the basis for a broader understanding of clinical concepts and data-driven clinical practice in the future. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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.

Within- and across-day patterns of interplay between depressive symptoms and related psychopathological processes: a dynamic network approach during the COVID-19 pandemic

BACKGROUND

In order to understand the intricate patterns of interplay connected to the formation and maintenance of depressive symptomatology, repeated measures investigations focusing on within-person relationships between psychopathological mechanisms and depressive components are required.

METHODS

This large-scale preregistered intensive longitudinal study conducted 68,240 observations of 1706 individuals in the general adult population across a 40-day period during the COVID-19 pandemic to identify the detrimental processes involved in depressive states. Daily responses were modeled using multi-level dynamic network analysis to investigate the temporal associations across days, in addition to contemporaneous relationships between depressive components within a daily window.

RESULTS

Among the investigated psychopathological mechanisms, helplessness predicted the strongest across-day influence on depressive symptoms, while emotion regulation difficulties displayed more proximal interactions with symptomatology. Helplessness was further involved in the amplification of other theorized psychopathological mechanisms including rumination, the latter of which to a greater extent was susceptible toward being influenced rather than temporally influencing other components of depressive states. Distinctive symptoms of depression behaved differently, with depressed mood and anhedonia most prone to being impacted, while lethargy and worthlessness were more strongly associated with outgoing activity in the network.

CONCLUSIONS

The main mechanism predicting the amplifications of detrimental symptomatology was helplessness. Lethargy and worthlessness revealed greater within-person carry-over effects across days, providing preliminary indications that these symptoms may be more strongly associated with pushing individuals toward prolonged depressive state experiences. The psychopathological processes of rumination, helplessness, and emotion regulation only exhibited interactions with the depressed mood and worthlessness component of depression, being unrelated to lethargy and anhedonia. The findings have implications for the impediment of depressive symptomatology during and beyond the pandemic period. They further outline the gaps in the literature concerning the identification of psychopathological processes intertwined with lethargy and anhedonia on the within-person level.

Emotion network density in burnout

Background

Health care workers are often affected by burnout, resulting in reduced personal well-being and professional functioning. Although emotional exhaustion is considered a core component of burnout, little is known about the dynamics of emotions and their relation to burnout. We used network analysis to investigate the correlation between the density of a negative emotion network, a marker for emotional rigidity in person-specific networks, and burnout severity.

Methods

Using an ecological momentary assessment design, the intensity of negative emotions of forty-three health care workers and medical students was assessed five times per day (between 6 am and 8 pm) for 17 days. Burnout symptoms were assessed at the end of the study period with the Maslach Burnout Inventory. Multilevel vector autoregressive models were computed to calculate network density of subject-specific temporal networks. The one-sided correlation between network density and burnout severity was assessed. The study protocol and analytic plan were registered prior to the data collection.

Results

We found a medium-sized correlation between the negative emotion network density and burnout severity at the end of the study period r(45) = .32, 95% CI = .09–1.0, p = .014).

Conclusions

The strength of the temporal interplay of negative emotions is associated with burnout, highlighting the importance of emotions and emotional exhaustion in reaction to occupational-related distress in health care workers. Moreover, our findings align with previous investigations of emotion network density and impaired psychological functioning, demonstrating the utility of conceptualizing the dynamics of emotions as a network.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40359-021-00670-y.

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.

A Narrative Review of Network Studies in Depression: What Different Methodological Approaches Tell Us About Depression

The network theory of psychopathology proposes that mental disorders arise from direct interactions between symptoms. This theory provides a promising framework to understand the development and maintenance of mental disorders such as depression. In this narrative review, we summarize the literature on network studies in the field of depression. Four methodological network approaches are distinguished: (i) studies focusing on symptoms at the macro-level vs. (ii) on momentary states at the micro-level, and (iii) studies based on cross-sectional vs. (iv) time-series (dynamic) data. Fifty-six studies were identified. We found that different methodological approaches to network theory yielded largely inconsistent findings on depression. Centrality is a notable exception: the majority of studies identified either positive affect or anhedonia as central nodes. To aid future research in this field, we outline a novel complementary network theory, the momentary affect dynamics (MAD) network theory, to understand the development of depression. Furthermore, we provide directions for future research and discuss if and how networks might be used in clinical practice. We conclude that more empirical network studies are needed to determine whether the network theory of psychopathology can indeed enhance our understanding of the underlying structure of depression and advance clinical treatment.