An n=1 Clinical Network Analysis of Symptoms and Treatment in Psychosis

Systematic review of early warning signs of relapse and behavioural antecedents of symptom worsening in people living with schizophrenia spectrum disorders

BACKGROUND

Identification of the early warning signs (EWS) of relapse is key to relapse prevention in schizophrenia spectrum disorders, however, limitations to their precision have been reported. Substantial methodological innovations have recently been applied to the prediction of psychotic relapse and to individual psychotic symptoms. However, there has been no systematic review that has integrated findings across these two related outcomes and no systematic review of EWS of relapse for a decade.

METHOD

We conducted a systematic review of EWS of psychotic relapse and the behavioural antecedents of worsening psychotic symptoms. Traditional EWS and ecological momentary assessment/intervention studies were included. We completed meta-analyses of the pooled sensitivity and specificity of EWS in predicting relapse, and for the prediction of relapse from individual symptoms.

RESULTS

Seventy two studies were identified including 6903 participants. Sleep, mood, and suspiciousness, emerged as predictors of worsening symptoms. Pooled sensitivity and specificity of EWS in predicting psychotic relapse was 71% and 64% (AUC value = 0.72). There was a large pooled-effect size for the model predicting relapse from individual symptom which did not reach statistical significance (d = 0.81, 95%CIs = -0.01, 1.63).

CONCLUSIONS

Important methodological advancements in the prediction of psychotic relapse in schizophrenia spectrum disorders are evident with improvements in the precision of prediction. Further efforts are required to translate these advances into effective clinical innovations.

Predictors of paranoia in the daily lives of people with non-affective psychosis and non-clinical controls: A systematic review of intensive longitudinal studies

BACKGROUND AND OBJECTIVES

Worrying, self-esteem, sleep problems, anomalous internal experiences, reasoning biases, and interpersonal sensitivity are associated with paranoia. However, no review has examined whether these variables function as predictors of paranoia in everyday life. The present systematic review of intensive longitudinal studies (e.g., experience sampling) examined contemporaneous and time-lagged associations between paranoia and each candidate mechanism in individuals with non-affective psychosis and controls (pre-registration: https://osf.io/uwr9d).

METHODS

We searched electronic databases, PsyArXiv, and reference lists for studies published since 1994.

RESULTS

Of n = 5,918 results, n = 54 fulfilled inclusion criteria (n = 43 datasets). Most studies examined individuals with non-affective psychosis (n = 34). Strong evidence emerged for negative affect (subsumed under 'anomalous internal experiences') and sleep problems. For self-esteem, results suggest contemporaneous and lagged effects on paranoia but associations are likely driven by between-person variance. The low number of studies (n = 2 studies each) allowed no conclusions regarding worrying and reasoning biases. Findings on interpersonal sensitivity, which should be interpreted with caution because of the predictor's conceptual overlap with paranoia, indicate contemporaneous effects whereas time-lagged and within-person associations could not be judged due to insufficient data.

LIMITATIONS

The present review used a narrative data-synthetization and it did not cover outcomes such as hallucinations.

CONCLUSIONS

Despite convincing evidence for affect and sleep problems, it remains unclear whether affective states are precursors or also consequences of paranoia (vicious circle), and which of the actigraphy measures (sleep time, -efficiency, -fragmentation, etc.) best predicts paranoia.

Assessing the delivering of iMAgery-focused therapy for PSychosis (iMAPS) via telehealth

OBJECTIVES

To examine the feasibility and acceptability of a novel telehealth (video-conferencing software and telephone calls) imagery-based therapeutic intervention for people experiencing persecutory delusions. Utilising a multiple baseline case series design and exploring imagery-focused therapy for psychosis (iMAPS).

DESIGN

A non-concurrent A-B multiple baseline design was used.

METHODS

Participants experiencing persecutory delusions and self-reporting a psychosis or schizophrenia-spectrum diagnosis were recruited through online adverts. On completion of assessments, participants were randomly assigned to multiple baseline assessments, of between three and five sessions. Six therapy sessions followed, consisting of imagery formulation, safe-place imagery creation, compassionate imagery, imagery manipulation and rescripting. Participants completed pre- and post-measures and sessional measures via an online survey software or in semi-structured interviews. Two weeks post-intervention, a final measure was completed exploring any potential adverse effects of psychotherapy.

RESULTS

Five female participants completed all baseline and therapeutic sessions, suggesting the therapy was and mode of delivery was feasible and acceptable. Results indicate strong effect sizes across PANSS positive subscale and mood, as well as participants reporting a clinically significant change in at least one measure, for example, PSYRATS. All participants reported a reduction in the realness and compelling nature of distressing imagery.

CONCLUSIONS

Results suggest delivering a telehealth imagery-focused therapy is acceptable and feasibly delivered via telehealth. A control group and blinding of assessments would strengthen the methodological limitations present.

Practitioner and researcher perspectives on the utility of ecological momentary assessment in mental health care: A survey study

BACKGROUND

Ecological momentary assessment (EMA) is a scientific self-monitoring method to capture individuals' daily life experiences. Early on, EMA has been suggested to have the potential to improve mental health care. However, it remains unclear if and how EMA should be implemented. This requires an in-depth investigation of how practitioners and researchers view the implementation of EMA.

OBJECTIVE

Explore the perspectives of mental health practitioners and EMA researchers on the utility of EMA for mental health care.

METHODS

Practitioners (n = 89; psychiatrists, psychologists, psychiatric nurses) and EMA researchers (n = 62) completed a survey about EMA in clinical practice. This survey addressed EMA goals for practitioner and patient, requirements regarding clinical use of EMA, and (dis)advantages of EMA compared to treatment-as-usual. t-Tests were used to determine agreement with each statement and whether practitioners' and researchers' views differed significantly. Linear regression was used to explore predictors of goals and preferences (e.g., EMA experience).

RESULTS

Practitioners and researchers considered EMA to be a useful clinical tool for diverse stages of care. They indicated EMA to be most useful for gaining insight into the context specificity of symptoms (55.0 %), whereas receiving alerts when symptoms increase was rated the least useful (11.3 %, alerts is in 95 % of bootstrap iterations between rank 8 and 10). Compared to treatment-as-usual, EMA was considered easier to use (M = 4.87, t = 5.30, p < .001) and interpret (M = 4.52, t = 3.61, p < .001), but also more burdensome for the patient (M = 4.48, t = 3.17, p < .001). Although participants preferred personalization of the EMA diary, they also suggested that EMA should cost practitioners and patients limited time. The preference for creating personalized EMA was related to the level of experience with EMA. Finally, they highlighted the need for practitioner training and patient full-time access to the EMA feedback.

CONCLUSIONS

This survey study demonstrated that practitioners and researchers expect EMA to have added value for mental health care. Concrete recommendations for implementation of EMA are formulated. This may inform the development of specific clinical applications and user-friendly EMA software.

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.

Early Warning Signals in Phase Space: Geometric Resilience Loss Indicators From Multiplex Cumulative Recurrence Networks

The detection of Early Warning Signals (EWS) of imminent phase transitions, such as sudden changes in symptom severity could be an important innovation in the treatment or prevention of disease or psychopathology. Recurrence-based analyses are known for their ability to detect differences in behavioral modes and order transitions in extremely noisy data. As a proof of principle, the present paper provides an example of a recurrence network based analysis strategy which can be implemented in a clinical setting in which data from an individual is continuously monitored for the purpose of making decisions about diagnosis and intervention. Specifically, it is demonstrated that measures based on the geometry of the phase space can serve as Early Warning Signals of imminent phase transitions. A publicly available multivariate time series is analyzed using so-called cumulative Recurrence Networks (cRN), which are recurrence networks with edges weighted by recurrence time and directed towards previously observed data points. The results are compared to previous analyses of the same data set, benefits, limitations and future directions of the analysis approach are discussed.

Time to Intervene: A Continuous-Time Approach to Network Analysis and Centrality

Network analysis of ESM data has become popular in clinical psychology. In this approach, discrete-time (DT) vector auto-regressive (VAR) models define the network structure with centrality measures used to identify intervention targets. However, VAR models suffer from time-interval dependency. Continuous-time (CT) models have been suggested as an alternative but require a conceptual shift, implying that DT-VAR parameters reflect total rather than direct effects. In this paper, we propose and illustrate a CT network approach using CT-VAR models. We define a new network representation and develop centrality measures which inform intervention targeting. This methodology is illustrated with an ESM dataset.

Using language in social media posts to study the network dynamics of depression longitudinally

Network theory of mental illness posits that causal interactions between symptoms give rise to mental health disorders. Increasing evidence suggests that depression network connectivity may be a risk factor for transitioning and sustaining a depressive state. Here we analysed social media (Twitter) data from 946 participants who retrospectively self-reported the dates of any depressive episodes in the past 12 months and current depressive symptom severity. We construct personalised, within-subject, networks based on depression-related linguistic features. We show an association existed between current depression severity and 8 out of 9 text features examined. Individuals with greater depression severity had higher overall network connectivity between depression-relevant linguistic features than those with lesser severity. We observed within-subject changes in overall network connectivity associated with the dates of a self-reported depressive episode. The connectivity within personalized networks of depression-associated linguistic features may change dynamically with changes in current depression symptoms.

Dynamic symptom networks across different at-risk stages for psychosis: An individual and transdiagnostic perspective

The clinical staging model distinguishes different stages of mental illness. Early stages, are suggested to be more mild, diffuse and volatile in terms of expression of psychopathology than later stages. This study aimed to compare individual transdiagnostic symptom networks based on intensive longitudinal data between individuals in different early clinical stages for psychosis. It was hypothesized that with increasing clinical stage (i) density of symptom networks would increase and (ii) psychotic experiences would be more central in the symptom networks. Data came from a 90-day diary study, resulting in 8640 observations within N = 96 individuals, divided over four subgroups representing different early clinical stages (n₁ = 25, n₂ = 27, n₃ = 24, n₄ = 20). Sparse Time Series Chain Graphical Models were used to create individual contemporaneous and temporal symptom networks based on 10 items concerning symptoms of depression, anxiety, psychosis, non-specific and vulnerability domains. Network density and symptom centrality (strength) were calculated individually and compared between and within the four subgroups. Level of psychopathology increased with clinical stage. The symptom networks showed large between-individual variation, but neither network density not psychotic symptom strength differed between the subgroups in the contemporaneous (p_density = 0.59, p_strength > 0.51) and temporal (p_density = 0.75, p_strength > 0.35) networks. No support was found for our hypothesis that higher clinical stage comes with higher symptom network density or a more central role for psychotic symptoms. Based on the high inter-individual variability, our results highlight the importance of individualized assessment of symptom networks.

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.

Structure and stability of symptoms in first episode psychosis: a longitudinal network approach

Early psychosis is characterised by heterogeneity in illness trajectories, where outcomes remain poor for many. Understanding psychosis symptoms and their relation to illness outcomes, from a novel network perspective, may help to delineate psychopathology within early psychosis and identify pivotal targets for intervention. Using network modelling in first episode psychosis (FEP), this study aimed to identify: (a) key central and bridge symptoms most influential in symptom networks, and (b) examine the structure and stability of the networks at baseline and 12-month follow-up. Data on 1027 participants with FEP were taken from the National EDEN longitudinal study and used to create regularised partial correlation networks using the 'EBICglasso' algorithm for positive, negative, and depressive symptoms at baseline and at 12-months. Centrality and bridge estimations were computed using a permutation-based network comparison test. Depression featured as a central symptom in both the baseline and 12-month networks. Conceptual disorganisation, stereotyped thinking, along with hallucinations and suspiciousness featured as key bridge symptoms across the networks. The network comparison test revealed that the strength and bridge centralities did not differ significantly between the two networks (C = 0.096153; p = 0.22297). However, the network structure and connectedness differed significantly from baseline to follow-up (M = 0.16405, p = <0.0001; S = 0.74536, p = 0.02), with several associations between psychosis and depressive items differing significantly by 12 months. Depressive symptoms, in addition to symptoms of thought disturbance (e.g. conceptual disorganisation and stereotyped thinking), may be examples of important, under-recognized treatment targets in early psychosis, which may have the potential to lead to global symptom improvements and better recovery.

Personalizing Psychological Care for Chronic Cancer-Related Fatigue: A Case Study on Symptom Dynamics

Approximately 25% of cancer patients suffer from chronic cancer-related fatigue (CCRF), which is a complex, multifactorial condition. While there are evidence-based interventions, it remains unclear what treatment works best for the individual patient. Psychological network models can offer a schematic representation of interrelations among fatigue and protective and perpetuating factors for the individual patient. We explored whether feedback based on these individual fatigue networks can help personalize psychological care for CCRF. A 34-year old woman with CCRF was referred to our mental healthcare institute for psycho-oncology. During the waitlist period, she filled out an experience sampling app for 101 days, including five daily assessments of fatigue, pain, mood, activity and fatigue coping. The interplay between items was visualized in network graphs at the moment-level and day-level, which were discussed with the patient. For example, acceptance of fatigue in the past three hours was associated with less hopelessness and less fatigue in the following moment. At the day-level, acceptance was also being associated with less fatigue, less hopelessness, a better mood, and more motivation to do things. The patient recognized these patterns and explained how unexpected waves of fatigue can make her feel hopeless. This started a dialogue on how cultivating acceptance could potentially help her handle the fatigue. The patient would discuss this with her therapist. Feedback based on individual fatigue networks can provide direct insight into how one copes with CCRF and subsequently offer directions for treatment. Further research is needed in order to implement this in clinical practice.

Personalized ESM monitoring and feedback to support psychological treatment for depression: a pragmatic randomized controlled trial (Therap-i)

BACKGROUND

Major depressive disorder (MDD) is a highly prevalent mental disorder with large disease burden, high levels of relapse or persistence, and overall suboptimal outcomes of protocolized pharmacological and psychotherapeutic treatments. There is an urgent need to improve treatment effectiveness, possibly through systematic treatment personalization. In psychotherapeutic treatments this can be achieved by case conceptualization. To support this process, we developed the Therap-i module, which consists of personalized Experienced Sampling Methodology (ESM) and feedback. The Therap-i module is integrated into outpatient psychotherapeutic treatment as usual (TAU) for depression. The study aim is to investigate the efficacy of the Therap-i module in decreasing symptomatology in unresponsive or relapsing patients diagnosed with MDD. We hypothesize that the Therap-i module will contribute to TAU by i) decreasing depressive symptoms, and ii) improving general functioning, therapeutic working alliance, and illness perception. This paper provides details of the study rationale, aims, procedures, and a discussion on potential pitfalls and promises of the module.

METHODS

Patients diagnosed with MDD (n = 100) will enrol in a pragmatic two-armed randomized controlled trial. Randomization is stratified according to the patient's treatment resistance level assessed with the Dutch Method for quantification of Treatment Resistance in Depression (DM-TRD). All fill-out the Inventory of Depressive Symptomatology Self Report (IDS-SR), Outcome Questionnaire (OQ-45), Illness Perception Questionnaire Mental Health (IPQ-MH), and Work Alliance Inventory Self Report (WAI-SR). In the intervention arm, through close collaboration between patient, clinician, and researcher, a personalized ESM diary is developed based on the patient's case conceptualization. During the ESM monitoring period (8 weeks, 5 assessments/day), patients receive feedback three times, which is discussed among the abovementioned three parties. Both patients and clinicians will evaluate the Therap-i module.

RESULTS

Data collection is ongoing.

DISCUSSION

This is the first study in which personalized ESM and feedback is integrated in outpatient psychotherapeutic TAU for depression. The labour intensive procedure and methodological pitfalls are anticipated challenges and were taken into account when designing the study. When hypotheses are confirmed, the Therap-i module may advance treatment for depression by providing insights into personalized patterns driving or perpetuating depressive complaints.

TRIAL REGISTRATION

Trial NL7190 (NTR7381) , registered prospectively 03-08-2018.

Poor Separation of Clinical Symptom Profiles by DSM-5 Disorder Criteria

Assessment of mental illness typically relies on a disorder classification system that is considered to be at odds with the vast disorder comorbidity and symptom heterogeneity that exists within and across patients. Patients with the same disorder diagnosis exhibit diverse symptom profiles and comorbidities creating numerous clinical and research challenges. Here we provide a quantitative analysis of the symptom heterogeneity and disorder comorbidity across a sample of 107,349 adult individuals (aged 18-85 years) from 8 English-speaking countries. Data were acquired using the Mental Health Quotient, an anonymous, online, self-report tool that comprehensively evaluates symptom profiles across 10 common mental health disorders. Dissimilarity of symptom profiles within and between disorders was then computed. We found a continuum of symptom prevalence rather than a clear separation of normal and disordered. While 58.7% of those with 5 or more clinically significant symptoms did not map to the diagnostic criteria of any of the 10 DSM-5 disorders studied, those with symptom profiles that mapped to at least one disorder had, on average, 20 clinically significant symptoms. Within this group, the heterogeneity of symptom profiles was almost as high within a disorder label as between 2 disorder labels and not separable from randomly selected groups of individuals with at least one of any of the 10 disorders. Overall, these results quantify the scale of misalignment between clinical symptom profiles and DSM-5 disorder labels and demonstrate that DSM-5 disorder criteria do not separate individuals from random when the complete mental health symptom profile of an individual is considered. Greater emphasis on empirical, disorder agnostic approaches to symptom profiling would help overcome existing challenges with heterogeneity and comorbidity, aiding clinical and research outcomes.

A Tutorial on Estimating Time-Varying Vector Autoregressive Models

Time series of individual subjects have become a common data type in psychological research. These data allow one to estimate models of within-subject dynamics, and thereby avoid the notorious problem of making within-subjects inferences from between-subjects data, and naturally address heterogeneity between subjects. A popular model for these data is the Vector Autoregressive (VAR) model, in which each variable is predicted by a linear function of all variables at previous time points. A key assumption of this model is that its parameters are constant (or stationary) across time. However, in many areas of psychological research time-varying parameters are plausible or even the subject of study. In this tutorial paper, we introduce methods to estimate time-varying VAR models based on splines and kernel-smoothing with/without regularization. We use simulations to evaluate the relative performance of all methods in scenarios typical in applied research, and discuss their strengths and weaknesses. Finally, we provide a step-by-step tutorial showing how to apply the discussed methods to an openly available time series of mood-related measurements.

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.

Network approach of mood and functional gastrointestinal symptom dynamics in relation to childhood trauma in patients with irritable bowel syndrome and comorbid panic disorder

OBJECTIVE

Irritable bowel syndrome (IBS) has a high comorbidity with mental disorders. The present paper aims to visualise the interplay between IBS and affect (anxiety and mood) in daily life. Furthermore, this interplay may be different depending on risk factors such as childhood trauma.

METHODS

Using momentary assessment (Experience Sampling Method), data of 24 individuals diagnosed with both IBS and panic disorder were analysed (15 non-trauma and 9 low-trauma-score patients). Networks were constructed, based on multilevel time-lagged linear regression analysis. Regression coefficients present network connections including three negative affect items (down, irritated, rushed), three positive affect items (happy, enthusiastic, cheerful), three abdominal complaints (abdominal pain, bloating, nausea) and one social item (feeling lonely). Those networks were stratified by levels of childhood trauma based on the Childhood Trauma Questionnaire.

RESULTS

Connections within the group of mood items and within the group of abdominal complaints were more frequent than between abdominal complaints and mood items. When data were stratified by childhood trauma, networks were different. In addition, node strengths were stronger in low-trauma than in non-trauma, although only one was significantly different (enthusiastic). Overall, there were mainly non-significant connections and a clear pattern was not visible.

CONCLUSIONS

A time-lagged network provides additional insight in connections between abdominal complaints and affective complaints, in patients with IBS and panic disorder, with different levels of childhood trauma. More research is needed to gain a better understanding of symptom formation and the impact of variation in context on individual symptom experiences in IBS with affective comorbidity. Baseline data of a clinical trial: NCT01551225 (http://www.clinicaltrials.gov).

Using person-specific networks in psychotherapy: challenges, limitations, and how we could use them anyway

BACKGROUND

The complexity of psychopathology is evident from its multifactorial etiology and diversity of symptom profiles and hampers effective treatment. In psychotherapy, therapists approach this complexity by using case conceptualization. During this process, patients and therapists closely collaborate on a personalized working theory of the patient's psychopathology. This is a challenging process and shows low reliability between therapists. With the experience sampling method (ESM), time-series data-valuable for case conceptualization-can be systematically gathered in a patient's normal daily life. These data can be analyzed and visualized in person-specific networks (PSNs). PSNs may support case conceptualization by providing a schematic representation of association patterns between affective, cognitive, behavioral, and context variables.

MAIN TEXT

We adopt a clinical perspective in considering how PSNs might be implemented to serve case conceptualization and what their role could be in psychotherapy. We suggest PSNs to be based on personalized ESM assessment to capture the unique constellation of variables in each patient. We reflect on the lack of a gold standard for creating PSNs, which may result in substantially different PSNs and thereby disparate information for case conceptualization. Moreover, even if PSNs are created in a consistent manner, results remain ambiguous as they are subject to multiple interpretations. Therefore, associations in PSNs do not allow for firm conclusions about a patient's psychopathology, but they may nevertheless be valuable in the process of case conceptualization. PSNs are based on systematically gathered, ecologically valid ESM data and provide a unique personalized perspective. When used responsibly, PSNs may be able to support case conceptualization by generating questions that serve as a starting point for a dialog between therapists and patients. Well-targeted questions are an essential tool for therapists to gain insight into the patients' psychopathology patterns and improve the quality of case conceptualization.

CONCLUSIONS

PSNs have limitations in terms of the reliability of the insights they provide directly. However, taking these challenges into account, we believe they have potential as a tool to help therapists and patients in their collaborative exploration of a patient's psychopathology. Clearly, this would need to be validated in future clinical research.

ESMvis: a tool for visualizing individual Experience Sampling Method (ESM) data

Purpose

The experience sampling method (ESM) is used for intensive longitudinal time-series data collection during normal daily life. ESM data give information on momentary affect, activities and (social) context of, for example, patients suffering from mental disorders, and allows for person-specific feedback reports. However, current personalized feedback reports only display a selection of measured variables, and typically involve only summary statistics, thus not reflecting the dynamic fluctuations in affect and its influencing factors. To address this shortcoming, we developed a tool for dynamically visualizing ESM data.

Methods

We introduce a new framework, ESMvis, for giving descriptive feedback, focusing on direct visualization of the dynamic nature of raw data. In this ESM feedback approach, raw ESM data are visualized using R software. We applied ESMvis to data collected for over 52 weeks on a patient diagnosed with an obsessive–compulsive disorder with comorbid depression.

Results

We provided personalized feedback, in which both the overall trajectory and specific time moments were captured in a movie format. Two relapses during the study period could be visually determined, and subsequently confirmed by the therapist. The therapist and patient evaluated ESMvis as an insightful add-on tool to care-as-usual.

Conclusion

ESMvis is a showcase on providing personalized feedback by dynamic visualization of ESM time-series data. Our tool is freely available and adjustable, making it widely applicable. In addition to potential applications in clinical practice, ESMvis can work as an exploratory tool that can lead to new hypotheses and inform more complex statistical techniques.

Electronic supplementary material

The online version of this article (10.1007/s11136-020-02701-4) contains supplementary material, which is available to authorized users.

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.

Concluding commentary: Treating perfectionism transdiagnostically with an eye on the future

The current piece of work is a commentary section for the special issue regarding the treatment of perfectionism. It discusses analytically the similarities and the differences between the six clinical case formulations included in the special issue to emphasize that there are different routes to beneficial outcomes. All the psychotherapeutic approaches included here are either transdiagnostic or have transdiagnostic applications. As such, the second part of the commentary focuses on the transdiagnostic future of the treatment of perfectionism. Concluding remarks evidence a collection of clinical case studies covering multiple forms of psychotherapy and an integrating overview of the literature from which practitioners can extract best practices examples. Though there is still a lot to be done with and for interventions on perfectionism, the current collection of case studies is a good starting point.

A Temporal Network Approach to Paranoia: A Pilot Study

Paranoid beliefs have been conceptualized as a central psychological process linked to schizophrenia and many mental disorders. Research on paranoia has indicated that it is pivotal to consider not only levels but also dynamic aspects of incriminated related mechanisms over time. In the present study, we conceptualized paranoia as a system of interacting elements. To do so, we used temporal network analysis to unfold the temporal dynamics between core psychological paranoia-related mechanisms, such as self-esteem, sadness, feeling close to others, and experiential avoidance. Time-series data of 23 participants with high scores in paranoia and/or interpersonal sensitivity were collected via experience sampling methodology (ESM). We applied a multilevel vector autoregressive (mlVAR) model approach and computed three distinct and complementary network models (i.e., contemporaneous, temporal, and between-subject) to disentangle associations between paranoia-related mechanisms in three different time frames. The contemporaneous model indicated that paranoia and sadness co-occurred within the same time frame, while sadness was associated with both low self-esteem and lack of closeness to others. The temporal model highlighted the importance of feeling close to others in predicting low paranoia levels in the next time frame. Finally, the between-subject model largely replicated an association found in both contemporaneous and temporal models. The current study reveals that the network approach offers a viable data-driven methodology for elucidating how paranoia-related mechanisms fluctuate over time and may determine its severity. Moreover, this novel perspective may open up new directions toward identifying potential targets for prevention and treatment of paranoia-related problems.

Toward a Complex Network of Risks for Psychosis: Combining Trauma, Cognitive Biases, Depression, and Psychotic-like Experiences on a Large Sample of Young Adults

Although the linkage between traumatic life events and psychotic-like experiences (PLEs) is well established, the knowledge of potential mechanisms of this relationship is scarce. The aim of the present study was to better understand the structure of connections between traumatic life events and PLEs by considering at the same time the role of cognitive biases and depressive symptoms in the population of young adults (18-35 years of age, M = 26.52, SD = 4.74, n = 6772). Our study was conducted within a framework of network analysis. PLEs were measured with the Prodromal Questionnaire (PQ-16), cognitive biases were measured with nine items from the Davos Assessment of Cognitive Biases Scale-18 (DACOBS-18), depressive symptoms were assessed with the Center for Epidemiologic Studies-Depression Scale (CESD-R) and exposure to traumatic life events was measured with a combination of Childhood Experience of Care and Abuse Questionnaire (CECA.Q) and Traumatic Experience Checklist (TEC). The results present a network of all nodes being interconnected within and between domains, with no isolated factors. Exposures to sexual trauma were the most central node in the network. Pathways were identified from trauma to PLEs via cognitive biases and depressive symptoms. However, the shortest pathway between the most central traumatic life event and PLEs was through other traumatic life events, without cognitive biases or depressive symptoms along the way. Our findings suggest the importance of environmental adversities as well as dysfunctional information processing and depression in the network of psychosis risks.

A network perspective on body dysmorphic disorder and major depressive disorder

BACKGROUND

Body dysmorphic disorder (BDD) is a highly debilitating mental disorder associated with notable psychosocial impairment and high rates of suicidality. This study investigated BDD from a network perspective, which conceptualizes mental disorders as systems of symptoms that cause and exacerbate one another (e.g., preoccupation with perceived appearance defect triggering compulsive checking in the mirror).

METHODS

In a sample of BDD patients (N = 148), we used cross-sectional network models to explore the network structure of 1) BDD symptoms and 2) BDD symptoms and major depressive disorder (MDD) symptoms, and tested which symptoms were most central (i.e., most strongly associated to other symptoms).

RESULTS

Interference in functioning due to appearance-related compulsions (BDD), feelings of worthlessness (MDD), and loss of pleasure (MDD) were most central.

CONCLUSION

These symptoms were most strongly predictive of other BDD and MDD symptoms and may be features of BDD that warrant prioritization in theory development and treatment. A limitation of our study is that the precision of these findings may be limited due to a small sample size relative to the number of parameters. Replication studies in larger samples of BDD patients are needed.

An Examination of Idiographic Networks of Posttraumatic Stress Disorder Symptoms

Although the application of network theory to posttraumatic stress disorder (PTSD) has yielded promising insights, the lack of equivalence between inter- and intraindividual variation limits the generalizability of these findings to any one individual with PTSD. Instead, a better understanding of how PTSD symptoms occur and vary over time within an individual requires exploring the idiographic network structure of PTSD. To do so, the present study used an intensive repeated-measures design to estimate intraindividual networks of PTSD symptoms on a person-by-person basis. Participants were 20 individuals who met criteria for PTSD and completed daily surveys assessing PTSD symptoms; surveys were completed four times per day for approximately 30 days. Employing a recently validated method provided by Fisher, Reeves, Lawyer, Medaglia, and Rubel (2017), we used these data to estimate a contemporaneous and temporal network of PTSD symptoms for individuals on a person-by-person basis. We then calculated centrality metrics to determine the relative importance of each symptom in each idiographic network. Across all contemporaneous networks, negative trauma-related cognitions and emotions were most commonly the most central symptoms. Further, across all temporal networks, (a) negative trauma-related emotions were the most common driver of variation in other symptoms over time and (b) distressing trauma-related dreams and sleep disturbance were the most common downstream consequences of variation in other PTSD symptoms over time. We also reviewed data from two randomly selected participants to illustrate how this approach could be used to identify maintenance factors of PTSD for each individual and guide individual treatment planning.

A time-series network approach to auditory verbal hallucinations: Examining dynamic interactions using experience sampling methodology

BACKGROUND

Identifying variables that influence daily-life fluctuations in auditory verbal hallucinations (AVHs) provides insight into potential mechanisms and targets for intervention. Network analysis, that uses time-series data collected by Experience Sampling Method (ESM), could be used to examine relations between multiple variables over time.

METHODS

95 daily voice-hearing individuals filled in a short questionnaire ten times a day for six consecutive days at pseudo-random moments. Using multilevel vector auto-regression, relations between voice-hearing and negative affect, positive affect, uncontrollable thoughts, dissociation, and paranoia were analysed in three types of networks: between-subjects (between persons, undirected), contemporaneous (within persons, undirected), and temporal (within persons, directed) networks. Strength centrality was measured to identify the most interconnected variables in the models.

RESULTS

Voice-hearing co-occurred with all variables, while on a 6-day period voice-hearing was only related to uncontrollable thoughts. Voice-hearing was not predicted by any of the factors, but it did predict uncontrollable thoughts and paranoia. All variables showed large autoregressions, i.e. mainly predicted themselves in this severe voice-hearing sample. Uncontrollable thoughts was the most interconnected factor, though relatively uninfluential.

DISCUSSION

Severe voice-hearing might be mainly related to mental state factors on the short-term. Once activated, voice-hearing appears to maintain itself. It is important to assess possible reactivity of AVH to triggers at the start of therapy; if reactive, therapy should focus on the triggering factor. If not reactive, Cognitive Behavioural interventions could be used first to reduce the negative effects of the voices. Limitations are discussed.

Therapeutic Chaos

The conventional view on interventions as mechanistically causing interchangeable clients to get better has come under attack. Group-based and linear approaches fall short in adequately describing the idiosyncratic and dynamic nature of treatment processes. Non-linear dynamic system theories in contrast hold great potential to better conceptualize and understand the generalities and idiosyncrasies of psychotherapeutic change processes. The aim of this study was to examine whether we can detect markers of complex dynamical systems behavior in two single-case therapies. All sessions from both therapies were coded with sequential plan analysis using a 10s sampling frequency. The coding system incorporates verbal and non-verbal behaviors and allows for the representation of contextualized interactive behaviors. The high sampling frequency results in long time series, which allowed us to apply non-linear analysis techniques. We found strong support for complex behavior and the existence of a butterfly effect, i.e., a relatively short prediction horizon in which reliable predictions about the system's future behavior could be made. Further, critical fluctuations as a marker for phase-transitions were detected that were accompanied with different interactional patterns in both therapies. Finally, there was strong support for self-organized pattern formation, with a few interactional patterns dominating the interaction. Considering that we are intervening on complex dynamical systems means that we have to (1) acknowledge the principal individuality of change processes, (2) accept the fundamental limitations of the mechanistic input-output model of treatment effects and (3) appreciate the impossibility of long-term predictions of treatment responses.

Applied ambulatory assessment: Integrating idiographic and nomothetic principles of measurement

Ambulatory assessment (AA; also known as ecological momentary assessment) has enjoyed enthusiastic implementation in psychological research. The ability to assess thoughts, feelings, behavior, physiology, and context intensively and repeatedly in the moment in an individual's natural ecology affords access to data that can answer exciting questions about sequences of events and dynamic processes in daily life. AA also holds unique promise for developing personalized models of individuals (i.e., precision or person-specific assessment) that might be transformative for applied settings such as clinical practice. However, successfully translating AA from bench to bedside is challenging because of the inherent tension between idiographic and nomothetic principles of measurement. We argue that the value of applied AA will be most fully realized by balancing the ability to develop personalized models with ensuring comparability among individuals. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches

The ubiquity of smartphones opened up the possibility of widespread use of the Experience Sampling Method (ESM). The method is used to collect longitudinal data of participants' daily life experiences and is ideal to capture fluctuations in emotions (momentary mental states) as an indicator for later mental ill-health. In this study, ESM data of patients with psychosis spectrum disorder and controls were used to examine daily life emotions and higher order patterns thereof. We attempted to determine whether aggregated ESM data, in which statistical measures represent the distribution and dynamics of the original data, were able to distinguish patients from controls in a predictive modeling framework. Variable importance, recursive feature elimination, and ReliefF methods were used for feature selection. Model training, tuning, and testing were performed in nested cross-validation, based on algorithms such as Random Forests, Support Vector Machines, Gaussian Processes, Logistic Regression, and Neural Networks. ROC analysis was used to post-process these models. Stability of model performance was studied using Monte Carlo simulations. The results provide evidence that patterns in emotion changes can be captured by applying a combination of these techniques. Acceleration in the variables anxious and insecure was particularly successful in adding further predictive power to the models. The best results were achieved by Support Vector Machines with radial kernel (accuracy = 82% and sensitivity = 82%). This proof-of-concept work demonstrates that synergistic machine learning and statistical modeling may be used to harness the power of ESM data in the future.

Capturing the complexity of mental disorders in the medically ill: the network approach on behavioral medicine

Since Descartes introduced dualism, body and mind have been seen as separate entities. The latent disease model, the view that symptoms are caused by an underlying disease, was possible within the dualistic paradigm. This paradigm, although successful in some aspects of medicine, is also assumed to underlie psychiatric disorders. As an alternative to the latent disease model, the network approach conceptualizes disorders as complex networks of causally connected symptoms. It offers a new way of understanding psychiatric disorders by directing attention away from the underlying cause and towards the symptoms and their functional interconnectedness, making the distinction between mental and physical symptoms obsolete. This article discusses how the network perspective helps us to overcome some of the problems we have faced when diagnosing and treating psychopathology in the medically ill. Furthermore, we describe how the network perspective can stimulate new research to better understand psychopathology in medically ill patients and how it can help deliver the most suitable treatment to the individual patient.

Potential Applications of Digital Technology in Assessment, Treatment, and Self-help for Hallucinations

The field of digital mental health is rapidly expanding with digital tools being used in assessment, intervention, and supporting self-help. The application of digital mental health to hallucinations is, however, at a very early stage. This report from a working group of the International Consortium on Hallucinations Research considers particular synergies between the phenomenon of hallucinations and digital tools that are being developed. Highlighted uses include monitoring and managing intermittently occurring hallucinations in daily life; therapeutic applications of audio and video media including virtual and augmented reality; targeting verbal aspects of hallucinations; and using avatars to represent hallucinatory voices. Although there is a well-established Internet-based peer support network, digital resources for hallucinations have yet to be implemented in routine practice. Implementation may benefit from identifying how to market resources to the broad range of populations who experience hallucinations and identifying sustainable funding models. It is envisaged that digital tools will contribute to improved self-management and service provision for people experiencing hallucinations.

Network analysis of symptoms in a Parkinson patient using experience sampling data: An n = 1 study

BACKGROUND

Around 50% of Parkinson's disease patients experience motor fluctuations after long-term treatment with levodopa. These fluctuations may be accompanied by mood fluctuations. Routine cross-sectional assessments cannot capture the extent of these motor and mood fluctuations and their possible associations. Experience sampling techniques that use frequently repeated measurements of symptoms over time are able to capture such fluctuations. Based on such data, longitudinal associations between symptoms can be studied using network analysis.

AIM

The purpose of this study is to identify longitudinal associations between motor symptoms and mood states in a patient with Parkinson's disease.

METHODS

A 53-year-old man with Parkinson's disease and motor fluctuations collected experience sampling data during 34 consecutive days. A set of dependent variables included tremor, rigidity, balance problems, and "on/off" state, and the mood variables anxiety, cheerful, and "down." Independent variables were the same variables assessed at the preceding measurement. Regression coefficients were calculated and presented in a network graph.

RESULTS

In this patient, anxiety and cheerfulness had a central position within the symptom network. Higher anxiety was prospectively associated with increased rigidity and tremor and with feeling "down." Cheerfulness was associated with less tremor. Balance problems were not influenced by cheerfulness nor anxiety, but increased balance problems were associated with reduced cheerfulness at the next assessment. Feeling "down" did not influence self-reported motor symptom severity at the next assessment.

CONCLUSION

This n = 1 study shows that network analysis of experience sampling data may reveal longitudinal associations of self-reported motor symptoms and mood states that may have relevance for treatment strategies. © 2018 International Parkinson and Movement Disorder Society.

Dynamic networks of PTSD symptoms during conflict

BACKGROUND

Conceptualizing posttraumatic stress disorder (PTSD) symptoms as a dynamic system of causal elements could provide valuable insights into the way that PTSD develops and is maintained in traumatized individuals. We present the first study to apply a multilevel network model to produce an exploratory empirical conceptualization of dynamic networks of PTSD symptoms, using data collected during a period of conflict.

METHODS

Intensive longitudinal assessment data were collected during the Israel-Gaza War in July-August 2014. The final sample (n = 96) comprised a general population sample of Israeli adult civilians exposed to rocket fire. Participants completed twice-daily reports of PTSD symptoms via smartphone for 30 days. We used a multilevel vector auto-regression model to produce contemporaneous and temporal networks, and a partial correlation network model to obtain a between-subjects network.

RESULTS

Multilevel network analysis found strong positive contemporaneous associations between hypervigilance and startle response, avoidance of thoughts and avoidance of reminders, and between flashbacks and emotional reactivity. The temporal network indicated the central role of startle response as a predictor of future PTSD symptomatology, together with restricted affect, blame, negative emotions, and avoidance of thoughts. There were some notable differences between the temporal and contemporaneous networks, including the presence of a number of negative associations, particularly from blame. The between-person network indicated flashbacks and emotional reactivity to be the most central symptoms.

CONCLUSIONS

This study suggests various symptoms that could potentially be driving the development of PTSD. We discuss clinical implications such as identifying particular symptoms as targets for interventions.

The network structure of paranoia in the general population

PURPOSE

Bebbington and colleagues' influential study on 'the structure of paranoia in the general population' used data from the British National Psychiatric Morbidity Survey and latent variable analysis methods. Network analysis is a relatively new approach in psychopathology research that considers mental disorders to be emergent phenomena from causal interactions among symptoms. This study re-analysed the British National Psychiatric Morbidity Survey data using network analysis to examine the network structure of paranoia in the general population.

METHODS

We used a Graphical Least Absolute Shrinkage and Selection Operator (glasso) method that estimated an optimal network structure based on the Extended Bayesian Information Criterion. Network sub-communities were identified by spinglass and EGA algorithms and centrality metrics were calculated per item and per sub-community.

RESULTS

We replicated Bebbington's four component structure of paranoia, identifying 'interpersonal sensitivities', 'mistrust', 'ideas of reference' and 'ideas of persecution' as sub-communities in the network. In line with previous experimental findings, worry was the most central item in the network. However, 'mistrust' and 'ideas of reference' were the most central sub-communities.

CONCLUSIONS

Rather than a strict hierarchy, we argue that the structure of paranoia is best thought of as a heterarchy, where the activation of high-centrality nodes and communities is most likely to lead to steady state paranoia. We also highlight the novel methodological approach used by this study: namely, using network analysis to re-examine a population structure of psychopathology previously identified by latent variable approaches.

Experience sampling methodology in mental health research: new insights and technical developments

In the mental health field, there is a growing awareness that the study of psychiatric symptoms in the context of everyday life, using experience sampling methodology (ESM), may provide a powerful and necessary addition to more conventional research approaches. ESM, a structured self-report diary technique, allows the investigation of experiences within, and in interaction with, the real-world context. This paper provides an overview of how zooming in on the micro-level of experience and behaviour using ESM adds new insights and additional perspectives to standard approaches. More specifically, it discusses how ESM: a) contributes to a deeper understanding of psychopathological phenomena, b) allows to capture variability over time, c) aids in identifying internal and situational determinants of variability in symptomatology, and d) enables a thorough investigation of the interaction between the person and his/her environment and of real-life social interactions. Next to improving assessment of psychopathology and its underlying mechanisms, ESM contributes to advancing and changing clinical practice by allowing a more fine-grained evaluation of treatment effects as well as by providing the opportunity for extending treatment beyond the clinical setting into real life with the development of ecological momentary interventions. Furthermore, this paper provides an overview of the technical details of setting up an ESM study in terms of design, questionnaire development and statistical approaches. Overall, although a number of considerations and challenges remain, ESM offers one of the best opportunities for personalized medicine in psychiatry, from both a research and a clinical perspective.

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

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

Application of network methods for understanding mental disorders: pitfalls and promise

Galvanized with the availability of sophisticated statistical techniques and large datasets, network medicine has emerged as an active area of investigation. Following this trend, network methods have been utilized to understand the interplay between symptoms of mental disorders. This realistic approach that may provide an improved framework into understanding mental conditions and underlying mechanisms is certainly to be welcomed. However, we have noticed that symptom network studies tend to lose sight of the fundamentals, overlook major limitations embedded in study designs, and make inferences that are difficult to justify with current findings. There is concern that disregarding these flaws may halt the progress of the network approach in psychiatry. Therefore, in this paper, we first attempt to identify the pitfalls: (1) a reductionist understanding of medicine and psychiatry, thereby inadvertently reintroducing the dichotomy of medicine (lung cancer) and psychiatry (depression), (2) a shortsighted view of signs and symptoms, (3) overlooking the limitations of available datasets based on scales with embedded latent class structures, (4) overestimating the importance of the current findings beyond what is supported by the study design. By addressing current issues, the hope is to navigate this rapidly growing field to a more methodologically sound and reproducible path that will contribute to our understanding of mental disorders and its underlying mechanisms.

The experience sampling method as an mHealth tool to support self-monitoring, self-insight, and personalized health care in clinical practice

BACKGROUND

The experience sampling method (ESM) builds an intensive time series of experiences and contexts in the flow of daily life, typically consisting of around 70 reports, collected at 8-10 random time points per day over a period of up to 10 days.

METHODS

With the advent of widespread smartphone use, ESM can be used in routine clinical practice. Multiple examples of ESM data collections across different patient groups and settings are shown and discussed, varying from an ESM evaluation of a 6-week randomized trial of mindfulness, to a twin study on emotion dynamics in daily life.

RESULTS

Research shows that ESM-based self-monitoring and feedback can enhance resilience by strengthening the capacity to use natural rewards. Personalized trajectories of starting or stopping medication can be more easily initiated and predicted if sensitive feedback data are available in real time. In addition, personalized trajectories of symptoms, cognitive abilities, symptoms impacting on other symptoms, the capacity of the dynamic system of mental health to "bounce back" from disturbance, and patterns of environmental reactivity yield uniquely personal data to support shared decision making and prediction in clinical practice. Finally, ESM makes it possible to develop insight into previous implicit patterns of thought, experience, and behavior, particularly if rapid personalized feedback is available.

CONCLUSIONS

ESM enhances clinical practice and research. It is empowering, providing co-ownership of the process of diagnosis, treatment evaluation, and routine outcome measurement. Blended care, based on a mix of face-to-face and ESM-based outside-the-office treatment, may reduce costs and improve outcomes.

A network theory of mental disorders

In recent years, the network approach to psychopathology has been advanced as an alternative way of conceptualizing mental disorders. In this approach, mental disorders arise from direct interactions between symptoms. Although the network approach has led to many novel methodologies and substantive applications, it has not yet been fully articulated as a scientific theory of mental disorders. The present paper aims to develop such a theory, by postulating a limited set of theoretical principles regarding the structure and dynamics of symptom networks. At the heart of the theory lies the notion that symptoms of psychopathology are causally connected through myriads of biological, psychological and societal mechanisms. If these causal relations are sufficiently strong, symptoms can generate a level of feedback that renders them self-sustaining. In this case, the network can get stuck in a disorder state. The network theory holds that this is a general feature of mental disorders, which can therefore be understood as alternative stable states of strongly connected symptom networks. This idea naturally leads to a comprehensive model of psychopathology, encompassing a common explanatory model for mental disorders, as well as novel definitions of associated concepts such as mental health, resilience, vulnerability and liability. In addition, the network theory has direct implications for how to understand diagnosis and treatment, and suggests a clear agenda for future research in psychiatry and associated disciplines.