Transdiagnostic Networks: Commentary on Nolen-Hoeksema and Watkins (2011)

Temporal Directionality Between Symptoms During Treatment of Depressed Inpatients: A Dynamic Time Warp Network Analysis

BACKGROUND

Depression is generally perceived from the perspective of the common-cause disease model. However, the network perspective assumes mutual influence of individual symptoms and stresses the importance of investigating symptom dynamics. Gaining a better understanding of symptom dynamics within individuals might contribute to more effective treatments.

METHODS

Current exploratory longitudinal research studied the associations and directionality between 43 symptoms from the generic questionnaire Symptom Questionnaire-48 (SQ-48) using dynamic time warp (DTW) analyses, in which trajectories with similar time-dependent patterns can be identified. Data from individuals were analysed first, yielding distance matrices for all symptom trajectories, after which the data were aggregated.

RESULTS

The 148 included patients were all admitted for the treatment of their clinical depression. Undirected DTW analyses of three patients with longer time series but otherwise randomly chosen showed large variability among individuals. Group-level undirected DTW analyses showed numerous significant edges between symptoms, largely clustering symptoms according to the eight pre-existing subscales of the SQ-48. Group-level directed DTW analyses showed five symptoms with significant outstrength: 'hopeless', 'restless', 'down/depressed', 'feeling tense' and 'no enjoyment', meaning that change in these key symptoms preceded change in other symptoms.

LIMITATIONS

The 43 included symptoms of the SQ-48 primarily focus on internalizing problems in severely depressed inpatients, potentially limiting generalizability.

CONCLUSIONS

DTW networks provided us with five key symptoms based on the dynamics of symptom scores. Future studies could explore whether process-based therapy targeted at symptoms with high outstrength might result in more effectivity as part of personalized treatment.

Longitudinal Relationships Between Depressive Symptoms and Generalized Anxiety Symptoms in Adolescents: A Cross-Lagged Network Analysis

The comorbidity of depression and generalized anxiety have raised questions about the existing classification system. Based on the perspective of network analysis, the developmental relationship between psychological symptoms can be explored more clearly. The current study aimed to explore the longitudinal network association of depressive symptoms and generalized anxiety symptoms in non-clinical adolescents. The sample contained 1,947 Chinese adolescents (Mage = 13.93, SD = 1.71; 48.5% girls). Their depression (PHQ-9) and generalized anxiety (GAD-7) symptoms were assessed at baseline (T1) and 6-month follow-up (T2). Data was analyzed using a cross-lagged network analysis. Depressive symptoms and generalized anxiety symptoms were closely related and did not appear to be distinct categories in adolescence. More generalized anxiety symptoms predicted depressive symptoms rather than vice versa. After controlling for gender, and age, "Difficulty relaxing" presented the highest out-expected influence (out-EI) in the network, while "trouble concentrating", and "suicidal ideation" showed the lowest in expected influence (in-EI). Findings suggested that interventions for generalized anxiety symptoms may help reduce depressive symptoms among Chinese adolescents. Specifically, alleviating "difficulty relaxing" may improve overall mental health.

Network Analysis of Comorbid Depressive and Anxious Symptoms in Family Caregivers of People with Dementia

OBJECTIVES

The aim of this study is an in-depth approach to depressive-anxious comorbidity in caregivers according to stress reactivity to disruptive behaviors using network analysis.

METHODS

The sample was composed of 317 primary family caregivers recruited through Day Care Centers and Neurology Services. The sample was split into low and high stress reactivity groups, based on their reports of reaction to disruptive behaviors. Depressive and anxious symptoms, daily hours dedicated to caregiving, time of caring, frequency of disruptive behaviors, co-residence, and kinship were also cross-sectionally measured.

RESULTS

The sample had a mean age of 62.38 years (SD = 12.97) and 68.5% were women. Regarding the network analysis, while the low reactivity group presents a sparse network, with no connection between anxious and depressive symptoms, the high reactivity group shows a high connection of intra and intercategory symptoms, with apathy, sadness, feeling depressed, and tension being the bridge symptoms between disorders.

CONCLUSIONS

Caregivers' stress reaction to disruptive behaviors might be a key factor for understanding comorbidity between depressive and anxious symptoms.

CLINICAL IMPLICATIONS

Tension, apathy, sadness, and feeling depressed should be clinical targets in the interventions, as they act as bridge symptoms between anxious and depressive symptomatology.

A systematic review of literature examining mediators and mechanisms of change in empirically supported treatments for posttraumatic stress disorder

Despite the availability of empirically supported treatments (ESTs) for posttraumatic stress disorder (PTSD), relatively little is known regarding these treatments' mechanisms of change. This systematic review moves beyond previous reviews by summarizing the findings and reviewing the methodological quality of literature that specifically examined mediators/mechanisms of change in ESTs for PTSD. Studies were included if they were written in English, empirical, peer-reviewed, claimed to study mediators/mechanisms of a recommended PTSD treatment, measured the mediator/mechanism during or before and after treatment, and included a posttreatment PTSD or global outcome (e.g., functioning). PsycINFO and PubMed were searched on October 7, 2022. Two coders screened and coded studies. Sixty-two eligible studies were identified. The most consistent mediator/mechanism was reduction in negative posttraumatic cognitions, followed by between-session extinction and decreased depression. Only 47% of studies measured the mediator/mechanism before the outcome and measured the mediator/mechanism and outcome at least three times, and 32% also used growth curve modeling to establish temporal precedence of change in the mediator/mechanism and outcome. Many of the mediators/mechanisms examined had weak or no empirical support. Results highlight the need for improved methodological rigor in treatment mediator and mechanism research. Implications for clinical care and research are discussed. PROSPERO ID: 248088.

Development of the Transdiagnostic Skills Scale (T2S)

INTRODUCTION

Psychotherapy has proved its efficacy for treating a wide range of psychological disorders. Most types of psychotherapy have been developed to treat specific disorders and validated through controlled-randomized trials. In recent years, researchers have developed a new way to conceptualize patients' difficulties, focusing on processes instead of diagnoses. However, there is no simple scale that evaluates transdiagnostic processes, and the development of such a tool is thus the aim of this study.

METHOD

We identified 12 processes that can be targeted in cognitive behavior therapy and created the Transdiagnostic Skills Scale (T2S) to evaluate them. We measured its internal consistency, factor structure and convergent validity in clinical and non-clinical samples.

RESULTS

We found a 6-factor structure composed of emotion regulation, behavioral activation/planning, emotional identification, assertiveness, problem solving and emotional confrontation. The T2S has high internal consistency (Cronbach's alpha = 0.95). We found negative associations between skills and symptoms of anxiety, depression and eating disorders. We found no association between these processes and symptoms of either alcohol or cannabis use disorder.

CONCLUSIONS

The T2S is a useful and valid tool to identify the skills that clinicians should work on with their patients. It offers a complementary way to understand patients' difficulties when categorical assessment is complicated.

Symptoms of major depressive disorder and post-traumatic stress disorder in veterans with mild traumatic brain injury: A network analysis

Mild Traumatic Brain Injury (mTBI, or concussion) is a debilitating condition that often leads to persistent cognitive and mental health problems post-injury. Post-traumatic Stress Disorder (PTSD) and Major Depressive Disorder (MDD) are two most commonly occurring mental health problems following mTBI and are suggested to be strong contributors to the persistent post-concussion symptoms. Thus, it is important to understand the symptomatology of PTSD and MDD post-mTBI, to better inform targets for behavioral health interventions. Therefore, the current study examined the symptom structure of post-mTBI co-morbid PTSD and MDD through network approaches; we compared the network structure of participants with a positive mTBI screen (N = 753) to the network structure of participants with a negative mTBI screen (N = 2044); lastly, we examined a network of PTSD and MDD symptoms with clinical covariates in a positive mTBI sample. We found that feeling distant/cutoff (P10) and difficulty concentrating (P15) were the most central symptoms in the positive mTBI network and sleep problems were the most prominent bridge nodes across the disorders. No significant difference between the positive and negative mTBI network were found through network comparison tests. Moreover, anxiety and insomnia were strongly associated with sleep symptoms and irritability symptoms, and emotional support and resilience were potential buffers against most of the PTSD and MDD symptoms. The results of this study might be particularly useful for identifying targets (i.e., feeling distant, concentration and sleep problems) for screening, monitoring and treatment after concussion to better inform post-mTBI mental health care and to improve treatment outcomes.

Testing intrusive thoughts as illness pathways between eating disorders and obsessive-compulsive disorder symptoms: a network analysis

Obsessive-compulsive disorder (OCD) and eating disorders (EDs) frequently co-occur. Intrusive thoughts are a mechanism that may maintain this comorbidity. This study used network analysis to identify central ED-related intrusive thoughts and tested which intrusive thoughts connected ED and OCD symptoms. Two cross-sectional graphical LASSO networks were computed using a sample of 353 non-clinical participants (mean age = 35.38, SD = 9.9, 40% female, 81.6% Caucasian) with elevated disordered eating symptoms. Model 1 included just ED-related intrusive thoughts, and Model 2 included ED-related intrusive thoughts, ED, and OCD symptoms. In Model 1, we found that thoughts about one's bodily appearance (i.e., looking horrible, getting fat, gaining weight) were most central. In Model 2, we found that desire to lose weight, eating in secret, and shape dissatisfaction were most central. We identified one illness pathway (i.e., difficulty concentrating due to thoughts of food/calories) connecting intrusive thoughts, ED symptoms, and OCD symptoms. However, intrusive thoughts did not bridge ED and OCD symptoms. Hence, we found some evidence that ED-related intrusive thoughts may contribute to ED and OCD symptoms based on thought content and frequency. However, other aspects of intrusive thoughts should be considered to ascertain whether they do in fact significantly contribute to ED and OCD comorbidity. Prevention efforts targeting ED-related intrusive thoughts may attenuate ED and OCD symptoms among subclinical individuals.

Network analysis to identify symptoms clusters and temporal interconnections in oncology patients

Oncology patients experience numerous co-occurring symptoms during their treatment. The identification of sentinel/core symptoms is a vital prerequisite for therapeutic interventions. In this study, using Network Analysis, we investigated the inter-relationships among 38 common symptoms over time (i.e., a total of six time points over two cycles of chemotherapy) in 987 oncology patients with four different types of cancer (i.e., breast, gastrointestinal, gynaecological, and lung). In addition, we evaluated the associations between and among symptoms and symptoms clusters and examined the strength of these interactions over time. Eight unique symptom clusters were identified within the networks. Findings from this research suggest that changes occur in the relationships and interconnections between and among co-occurring symptoms and symptoms clusters that depend on the time point in the chemotherapy cycle and the type of cancer. The evaluation of the centrality measures provides new insights into the relative importance of individual symptoms within various networks that can be considered as potential targets for symptom management interventions.

Transdiagnostic Psychopathology in a Help-Seeking Population of an Early Recognition Center for Mental Disorders: Protocol for an Experience Sampling Study

BACKGROUND

Prevention in psychiatry provides a promising way to address the burden of mental illness. However, established approaches focus on specific diagnoses and do not address the heterogeneity and manifold potential outcomes of help-seeking populations that present at early recognition services. Conceptualizing the psychopathology manifested in help-seeking populations from a network perspective of interacting symptoms allows transdiagnostic investigations beyond binary disease categories. Furthermore, modern technologies such as smartphones facilitate the application of the Experience Sampling Method (ESM).

OBJECTIVE

This study is a combination of ESM with network analyses to provide valid insights beyond the established assessment instruments in a help-seeking population.

METHODS

We will examine 75 individuals (aged 18-40 years) of the help-seeking population of the Cologne early recognition center. For a maximally naturalistic sample, only minimal exclusion criteria will be applied. We will collect data for 14 days using a mobile app to assess 10 transdiagnostic symptoms (ie, depressive, anxious, and psychotic symptoms) as well as distress level 5 times a day. With these data, we will generate average group-level symptom networks and personalized symptom networks using a 2-step multilevel vector autoregressive model. Additionally, we will explore associations between symptom networks and sociodemographic, risk, and resilience factors, as well as psychosocial functioning.

RESULTS

The protocol was designed in February 2020 and approved by the Ethics Committee of the University Hospital Cologne in October 2020. The protocol was reviewed and funded by the Köln Fortune program in September 2020. Data collection began in November 2020 and was completed in November 2021. Of the 258 participants who were screened, 93 (36%) fulfilled the inclusion criteria and were willing to participate in the study. Of these 93 participants, 86 (92%) completed the study. The first results are expected to be published in 2022.

CONCLUSIONS

This study will provide insights about the feasibility and utility of the ESM in a help-seeking population of an early recognition center. Providing the first explorative phenotyping of transdiagnostic psychopathology in this population, our study will contribute to the innovation of early recognition in psychiatry. The results will help pave the way for prevention and targeted early intervention in a broader patient group, and thus, enable greater intended effects in alleviating the burden of psychiatric disorders.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/35206.

Jumping back onto the giants' shoulders: Why emotional memory should be considered in a network perspective of psychopathology

Clinical psychology finds itself with a paradox: On the one hand, there is abundant empirical evidence showing that aversive experiences increase the risk for psychopathology. In fact, a learning and memory framework forms the foundation of numerous psychological theories and treatments. For example, various CBT approaches aim to target maladaptive emotional memories (e.g., schemas or cognitions) that are deemed to lie at the core of mental health conditions. On the other hand, a new approach - the network theory - is gaining ground, which ignores underlying causes for mental disorders and instead dictates a focus on symptoms and their causal interactions. While radical shifts are sometimes necessary in science, we argue why completely neglecting common causes, such as emotional memory, is not justified. We critically discuss the strengths and limitations of the network approach: While its transdiagnostic nature and recognition of symptom interactions have the potential to invigorate the field, the framework is merely descriptive, its concepts not well defined, and its clinical utility still to be established. To move forward, we propose an incorporation of latent constructs into the network model, starting with clearer definitions and operationalisations of concepts in both network and latent variable models.

Apathy and Huntington's Disease: A Literature Review Based on PRISMA

OBJECTIVE

Although apathy is commonly reported among patients with Huntington's disease (HD), this psychiatric symptom has not yet been clearly defined or extensively studied in HD. Most researchers have adopted descriptive approaches, showing that apathy is a multidimensional entity but leaving the processes underlying its different dimensions relatively unexplored.

METHODS

A systematic review of the literature on apathy in HD, focusing on current approaches and measurement tools, was conducted.

RESULTS

Searches in PubMed and PubMed Central yielded 368 articles, 25 of which were included in the present review.

CONCLUSIONS

This systematic review suggests that more comprehensive research is needed to help shed light on apathy in HD, especially regarding its multidimensional aspect and underlying mechanisms.

Network analysis of comorbid posttraumatic stress disorder and depression in adolescents across COVID-19 epidemic and Typhoon Lekima

BACKGROUND

Network analytic studies indicate that posttraumatic stress disorder (PTSD) may be comorbid with depression at the symptom level, but it remains unclear whether these findings are replicable and generalizable across trauma types.

OBJECTIVE

This study aim was to examine and compare PTSD-depression comorbidity networks of two types of trauma related to Typhoon Lekima and COVID-19 epidemic.

METHODS

Participants were 1605 and 601 adolescents recruited following Typhoon Lekima and the COVID-19 outbreak, respectively.

RESULTS

COVID-19 and Lekima PTSD-depression networks had considerable similarities, including adequate stability and accuracy, connected symptoms of PTSD and depression, symptoms with high centralities, and bridge symptoms. PTSD-depression comorbid symptoms were more complicated in the COVID-19 network but may show more persistence in the Lekima network. Distinct bridge symptoms contributed to the heterogeneity of PTSD-depression comorbidity characteristics between the two networks. Specifically, restricted affect and felt down and unhappy were two important bridge symptoms with high centrality unique to the COVID-19 network.

CONCLUSIONS

PTSD-depression comorbidity network has considerable replicability across trauma types, but specific symptom-level associations and some bridge symptoms may vary across trauma types. These findings also highlight the importance of negative emotions to comorbid PTSD and depression in adolescents following the COVID-19 outbreak compared with Typhoon Lekima.

Mapping the Presence of Anxiety Symptoms in Adults With Major Depressive Disorder

Background: Patients with major depressive disorder (MDD) often present with co-occurring anxiety symptoms. The network method provides a novel view on understanding the co-occurrence of depressive and anxiety symptoms. Thus, the purpose of our study was to explore it by applying network analysis methods. Methods: We used electronic medical records from West China Hospital in China. In total, 3,424 patients who met the criteria for MDD were included. R-studio 3.6 was used to estimate the network structure. First, we estimated the network structure of depression and anxiety symptoms using the graphic LASSO algorithm. Then, we estimated the centrality indices of nodes to determine which symptoms are more central in the network. We then estimated the bridge centrality indices using the bridge function via the R package networktools. Results: Some strong connections were found like "easy to wake up," "wake up early," and "difficulty falling asleep," "suicidal thoughts," and "hopelessness." "Depressed mood," "somatic anxiety," "hopelessness," "anxiety mood," and "tension" have the higher centrality indices. Results revealed eight bridge symptoms (e.g., concentration/memory difficulty, gastrointestinal symptoms) in the co-occurrence network structure. Conclusions: This research suggests that the described approach in mapping the presence of anxiety symptoms in individuals with major depression might potentially increase diagnostic precision and help choose more targeted interventions and potentially reduce the occurrence of treatment resistance.

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

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

The Relationship between the Theory of Mind Skills and Disorder Severity among Adolescents with ADHD

Objective

This study aimed to examine the sub-dimensions of the theory of mind (ToM) and to investigate the relationship between ToM skills and disorder severity by comparing adolescents with attention-deficit hyperactive disorder (ADHD) with healthy individuals.

Methods

The study included 42 adolescents with ADHD and education- and age-matched 41 healthy volunteers. The Smarties test, ice cream truck test, faux pas recognition test, and eyes test were applied to all participants. Turgay Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-IV)-Based Child and Adolescent Disruptive Behavioral Disorders Screening and Rating Scale (T-DSM-IV-S) was applied to the group with ADHD to measure the disorder severity.

Results

The group with ADHD was seen to have ToM skills impairment. There was a statistically significant difference between the groups in terms of the ice cream truck test, faux pas recognition test, and eyes test. A significant correlation was observed between the T-DSM-IV-S results and the eyes test results of the patients.

Conclusion

This study has shown that advanced ToM skills can be impaired in adolescents with ADHD and that impairment in skills is associated with disorder severity.

Symptom structure of PTSD and co-morbid depressive symptoms - a network analysis of combat veteran patients

BACKGROUND

Despite extensive research, symptom structure of posttraumatic stress disorder (PTSD) is highly debated. The network approach to psychopathology offers a novel method for understanding and conceptualizing PTSD. However, extant studies have mainly used small samples and self-report measures among sub-clinical populations, while also overlooking co-morbid depressive symptoms.

METHODS

PTSD symptom network topology was estimated in a sample of 1489 treatment-seeking veteran patients based on a clinician-rated PTSD measure. Next, clinician-rated depressive symptoms were incorporated into the network to assess their influence on PTSD network structure. The PTSD-symptom network was then contrasted with the network of 306 trauma-exposed (TE) treatment-seeking patients not meeting full criteria for PTSD to assess corresponding network differences. Finally, a directed acyclic graph (DAG) was computed to estimate potential directionality among symptoms, including depressive symptoms and daily functioning.

RESULTS

The PTSD symptom network evidenced robust reliability. Flashbacks and getting emotionally upset by trauma reminders emerged as the most central nodes in the PTSD network, regardless of the inclusion of depressive symptoms. Distinct clustering emerged for PTSD and depressive symptoms within the comorbidity network. DAG analysis suggested a key triggering role for re-experiencing symptoms. Network topology in the PTSD sample was significantly distinct from that of the TE sample.

CONCLUSIONS

Flashbacks and psychological reactions to trauma reminders, along with their strong connections to other re-experiencing symptoms, have a pivotal role in the clinical presentation of combat-related PTSD among veterans. Depressive and posttraumatic symptoms constitute two separate diagnostic entities, but with meaningful between-disorder connections, suggesting two mutually-influential systems.

Comorbidity between depression and anxiety: assessing the role of bridge mental states in dynamic psychological networks

BACKGROUND

Comorbidity between depressive and anxiety disorders is common. A hypothesis of the network perspective on psychopathology is that comorbidity arises due to the interplay of symptoms shared by both disorders, with overlapping symptoms acting as so-called bridges, funneling symptom activation between symptom clusters of each disorder. This study investigated this hypothesis by testing whether (i) two overlapping mental states "worrying" and "feeling irritated" functioned as bridges in dynamic mental state networks of individuals with both depression and anxiety as compared to individuals with either disorder alone, and (ii) overlapping or non-overlapping mental states functioned as stronger bridges.

METHODS

Data come from the Netherlands Study of Depression and Anxiety (NESDA). A total of 143 participants met criteria for comorbid depression and anxiety (65%), 40 participants for depression-only (18.2%), and 37 for anxiety-only (16.8%) during any NESDA wave. Participants completed momentary assessments of symptoms (i.e., mental states) of depression and anxiety, five times a day, for 2 weeks (14,185 assessments). First, dynamics between mental states were modeled with a multilevel vector autoregressive model, using Bayesian estimation. Summed average lagged indirect effects through the hypothesized bridge mental states were compared between groups. Second, we evaluated the role of all mental states as potential bridge mental states.

RESULTS

While the summed indirect effect for the bridge mental state "worrying" was larger in the comorbid group compared to the single disorder groups, differences between groups were not statistically significant. The difference between groups became more pronounced when only examining individuals with recent diagnoses (< 6 months). However, the credible intervals of the difference scores remained wide. In the second analysis, a non-overlapping item ("feeling down") acted as the strongest bridge mental state in both the comorbid and anxiety-only groups.

CONCLUSIONS

This study empirically examined a prominent network-approach hypothesis for the first time using longitudinal data. No support was found for overlapping mental states "worrying" and "feeling irritable" functioning as bridge mental states in individuals vulnerable for comorbid depression and anxiety. Potentially, bridge mental state activity can only be observed during acute symptomatology. If so, these may present as interesting targets in treatment, but not prevention. This requires further investigation.

The Digital Phenotyping Project: A Psychoanalytical and Network Theory Perspective

A new method of observation is currently emerging in psychiatry, based on data collection and behavioral profiling of smartphone users. Numerical phenotyping is a paradigmatic example. This behavioral investigation method uses computerized measurement tools in order to collect characteristics of different psychiatric disorders. First, it is necessary to contextualize the emergence of these new methods and to question their promises and expectations. The international mental health research framework invites us to reflect on methodological issues and to draw conclusions from certain impasses related to the clinical complexity of this field. From this contextualization, the investigation method relating to digital phenotyping can be questioned in order to identify some of its potentials. These new methods are also an opportunity to test psychoanalysis. It is then necessary to identify the elements of fruitful analysis that clinical experience and research in psychoanalysis have been able to deploy regarding the challenges of digital technology. An analysis of this theme’s literature shows that psychoanalysis facilitates a reflection on the psychological effects related to digital methods. It also shows how it can profit from the research potential offered by new technical tools, considering the progress that has been made over the past 50 years. This cross-fertilization of the potentials and limitations of digital methods in mental health intervention in the context of theoretical issues at the international level invites us to take a resolutely non-reductionist position. In the field of research, psychoanalysis offers a specific perspective that can well be articulated to an epistemology of networks. Rather than aiming at a numerical phenotyping of patients according to the geneticists’ model, the case formulation method appears to be a serious prerequisite to give a limited and specific place to the integration of smartphones in clinical investigation.

Applying a Dynamical Systems Model and Network Theory to Major Depressive Disorder

Mental disorders like major depressive disorder can be modeled as complex dynamical systems. In this study we investigate the dynamic behavior of individuals to see whether or not we can expect a transition to another mood state. We introduce a mean field model to a binomial process, where we reduce a dynamic multidimensional system (stochastic cellular automaton) to a one-dimensional system to analyse the dynamics. Using maximum likelihood estimation, we can estimate the parameter of interest which, in combination with a bifurcation diagram, reflects the expectancy that someone has to transition to another mood state. After numerically illustrating the proposed method with simulated data, we apply this method to two empirical examples, where we show its use in a clinical sample consisting of patients diagnosed with major depressive disorder, and a general population sample. Results showed that the majority of the clinical sample was categorized as having an expectancy for a transition, while the majority of the general population sample did not have this expectancy. We conclude that the mean field model has great potential in assessing the expectancy for a transition between mood states. With some extensions it could, in the future, aid clinical therapists in the treatment of depressed patients.

A new conception and subsequent taxonomy of clinical psychological problems

BACKGROUND

A taxonomy of the objects of study, theory, assessment, and intervention is critical to the development of all clinical sciences. Clinical psychology has been conceptually and administratively dominated by the taxonomy of an adjacent discipline - psychiatry's Diagnostic and statistical manual of mental disorders (DSM). Many have called for a 'paradigm shift' away from a medical nosology of diseases toward clinical psychology's own taxonomy of clinical psychological problems (CPPs), without being able to specify what is to be listed and classified.

MAIN TEXT

An examination of DSM's problems for clinical psychology, especially its lack of clinical utility, and a search for the essence of CPPs in what clinical psychologists actually do, leads to the proposal that: The critical psychological-level phenomenon underlying CPPs is the occurrence of 'problem-maintaining circles' (PMCs) of causally related cognitions, emotions, behaviours, and/or stimuli. This concept provides an empirically-derived, theory-based, treatment-relevant, categorical, essentialist, parsimonious, and nonstigmatizing definition of CPPs. It distinguishes psychological problems in which PMCs have not (yet?) formed, and which may respond to 'counseling', clinical psychological problems in which active PMCs require clinical intervention, and psychopathological problems which are unlikely to be 'cured' by PMC-breaking alone.

CONCLUSION

A subsequent classification and coding system of PMCs is proposed, and expected benefits to research, communication, and the quality of case formulation in clinical psychology are described, reliant upon a development effort of some meaningful fraction of that which has been devoted to the DSM.

The Interactive Effect of Happiness Emotion Goals and Emotion Regulation Self-Efficacy on Anxiety and Depression

Overvaluation of happiness might be a transdiagnostic risk factor for psychopathology. However, emotion regulation self-efficacy may influence the association between happiness emotion goals and psychopathology. The purpose of the present study was twofold. First, we sought to replicate prior findings showing that happiness emotion goals and depressive symptoms are positively related, but only among those with lower emotion regulation self-efficacy. Second, we examined whether the noted interaction effect would relate to generalized anxiety symptoms in a sample of general population adults (N = 504). Results from regression analyses were consistent with our predictions suggesting that individuals with unrealistic happiness emotion goals and low emotion regulation self-efficacy may be particularly prone to experiencing negative emotional states and psychological distress. Further, study findings suggest the possibility that the noted interaction has transdiagnostic value and it may be important to target emotion regulation self-efficacy in the service of alleviating internalizing psychopathology.

Network Analysis of the Multidimensional Symptom Experience of Oncology

Oncology patients undergoing cancer treatment experience an average of fifteen unrelieved symptoms that are highly variable in both their severity and distress. Recent advances in Network Analysis (NA) provide a novel approach to gain insights into the complex nature of co-occurring symptoms and symptom clusters and identify core symptoms. We present findings from the first study that used NA to examine the relationships among 38 common symptoms in a large sample of oncology patients undergoing chemotherapy. Using two different models of Pairwise Markov Random Fields (PMRF), we examined the nature and structure of interactions for three different dimensions of patients’ symptom experience (i.e., occurrence, severity, distress). Findings from this study provide the first direct evidence that the connections between and among symptoms differ depending on the symptom dimension used to create the network. Based on an evaluation of the centrality indices, nausea appears to be a structurally important node in all three networks. Our findings can be used to guide the development of symptom management interventions based on the identification of core symptoms and symptom clusters within a network.

[The current state and future prospects of depression research (clinical and classification problems)]

Despite decades of research, neurobiological studies of depression haven't achieved significant results. Many experts propose that one of the main reasons for this failure is current diagnostic standards not considering the heterogeneity and polymorphism of depression. Research is unable to identify specific neurobiological changes due to formal diagnosis 'major depressive disorder' and new diagnostic criteria are needed. RDoC (Research Domain Criteria) has intensified the confrontation between biological and clinical researchers and changes in approach to depressive psychopathology are discussed. A review presents the recent approaches used in studies of depressive disorders, the methodology they use, the scientific paradigms they rely on.

Estimating transdiagnostic symptom networks: The problem of "skip outs" in diagnostic interviews

Network models of the symptoms of psychological disorders provide a novel lens for examining comorbidity. Viewing symptoms as causal entities in their own right, researchers can attempt to identify specific symptoms that "bridge" diagnostic entities, providing a more granular perspective on comorbidity than the one provided by analysis at the syndromal level. Such analyses could help identify transdiagnostic targets for both research and clinical interventions. Although promising conceptually, extant work using this approach often uses structured diagnostic interview data that employ "skip outs," branching logic conditional on responses to gating questions (which may be criterial or risk markers). We demonstrate that skip outs, where screening items are asked for each disorder before assessing the remaining symptoms, can produce significant problems in interpretation of comorbidity between symptoms and, hence, transdiagnostic network models. The nature and extent of this problem is explored, and suggestions for future network studies of comorbidity are provided. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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

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

Evidence that psychopathology symptom networks have limited replicability

Network analysis is quickly gaining popularity in psychopathology research as a method that aims to reveal causal relationships among individual symptoms. To date, 4 main types of psychopathology networks have been proposed: (a) association networks, (b) regularized concentration networks, (c) relative importance networks, and (d) directed acyclic graphs. The authors examined the replicability of these analyses based on symptoms of major depression and generalized anxiety between and within 2 highly similar epidemiological samples (i.e., the National Comorbidity Survey-Replication [n = 9282] and the National Survey of Mental Health and Wellbeing [n = 8841]). Although association networks were stable, the 3 other types of network analysis (i.e., the conditional independence networks) had poor replicability between and within methods and samples. The detailed aspects of the models-such as the estimation of specific edges and the centrality of individual nodes-were particularly unstable. For example, 44% of the symptoms were estimated as the "most influential" on at least 1 centrality index across the 6 conditional independence networks in the full samples, and only 13-21% of the edges were consistently estimated across these networks. One of the likely reasons for the instability of the networks is the predominance of measurement error in the assessment of individual symptoms. The authors discuss the implications of these findings for the growing field of psychopathology network research, and conclude that novel results originating from psychopathology networks should be held to higher standards of evidence before they are ready for dissemination or implementation in the field. (PsycINFO Database Record

The Abductive Theory of Method: Scientific Inquiry and Clinical Practice

Clinical reasoning is one of the central components of psychological assessment. The identification of a client's psychological difficulties and the subsequent depiction of their onset, development, and interrelationships enables clinicians to plan treatment in a systematic and effective manner. In an article (Ward, Vertue, & Haig, 1999), we outlined the abductive theory of method (ATOM) and argued that it offered a useful framework for highlighting and integrating the major phases of psychological assessment. These phases involve detecting clinical phenomena, postulating psychological mechanisms, developing a case formulation, and evaluating a case formulation. In this article we present a revised version of the adaptation of ATOM and elaborate on the related clinical dimensions of assessment.

Network approach to the symptom-level association between alcohol use disorder and posttraumatic stress disorder

PURPOSE

The high prevalence of alcohol use disorder among individuals with a history of trauma and posttraumatic stress disorder is well documented. The current study applied network analysis to map the structure of symptom associations between these disorders.

METHODS

Data come from a community sample of 449 Australian adults with a history of trauma and alcohol consumption during the last 12 months. Data analysis consisted of the construction of the comorbidity network of PTSD/AUD symptoms, identification of the bridging symptoms, computation of the centrality measures, and evaluation of the robustness of the results.

RESULTS

Results highlighted two main symptom clusters, corresponding to two disorders, and that only nine edges connected the two clusters. Bridging symptoms connecting the two clusters were: alcohol use in dangerous situations, physical or mental health problems as a result of alcohol use, loss of interest or reduced social activities, and reckless/self-destructive behaviour.

CONCLUSIONS

Identification of both central symptoms, because of their key role in the constellation and strong associations with majority of symptoms, and bridge symptoms, because of their mediating role between two disorders, has some implications in terms of self-medication and risk-taking/self-regulation theories of comorbidity and provides a number of clinical implications, which warrants further exploration within clinical samples.

A network approach to the comorbidity between posttraumatic stress disorder and major depressive disorder: The role of overlapping symptoms

BACKGROUND

The role of symptom overlap between major depressive disorder and posttraumatic stress disorder in comorbidity between two disorders is unclear. The current study applied network analysis to map the structure of symptom associations between these disorders.

METHODS

Data comes from a sample of 909 Australian adults with a lifetime history of trauma and depressive symptoms. Data analysis consisted of the construction of two comorbidity networks of PTSD/MDD with and without overlapping symptoms, identification of the bridging symptoms, and computation of the centrality measures.

RESULTS

The prominent bridging role of four overlapping symptoms (i.e., sleep problems, irritability, concentration problems, and loss of interest) and five non-overlapping symptoms (i.e., feeling sad, feelings of guilt, psychomotor retardation, foreshortened future, and experiencing flashbacks) is highlighted.

LIMITATIONS

The current study uses DSM-IV criteria for PTSD and does not take into consideration significant changes made to PTSD criteria in DSM-5. Moreover, due to cross-sectional nature of the data, network estimates do not provide information on whether a symptom actively triggers other symptoms or whether a symptom mostly is triggered by other symptoms.

CONCLUSION

The results support the role of dysphoria-related symptoms in PTSD/MDD comorbidity. Moreover, Identification of central symptoms and bridge symptoms will provide useful targets for interventions that seek to intervene early in the development of comorbidity.

A Transdiagnostic Network Approach to Psychosis

Our ability to accurately predict development and outcome of early expression of psychosis is limited. To elucidate the mechanisms underlying psychopathology, a broader, transdiagnostic approach that acknowledges the complexity of mental illness is required. The upcoming network paradigm may be fruitful here. In this study, we applied a transdiagnostic network approach to psychosis. Data pertain to the third wave (second follow-up) of a sample of adolescents originally recruited at age 7-8 years. At baseline, N = 347 children with auditory verbal hallucinations (AVH) and N = 347 control children were included. N = 293 of these N = 694 children participated in the second follow-up (mean age 18.9 years; 59% women). Participants completed the Community Assessment of Psychic Experiences (CAPE) and the Depression, Anxiety and Stress Scale (DASS-21). A specific type of network model, the Ising model, was applied to dichotomized CAPE and DASS items. Interconnections of experiences within the same domain were observed, as well as interconnections between experiences of multiple domains of psychopathology. Quantitative and qualitative differences in network architecture were found in networks of psychopathological experiences in individuals with or without AVH at age 7-8 years. Although adolescents with or without previous AVH did not differ in their current CAPE scores, differences in the interconnectedness of psychopathology items were still found, possibly mirroring a difference in psychosis liability. This study showed that it is possible to map transdiagnostic experiences of psychopathology as a network and that important information can be derived from this approach in comparison to regular approaches.

Network analysis of depression and anxiety symptom relationships in a psychiatric sample

BACKGROUND

Researchers have studied psychological disorders extensively from a common cause perspective, in which symptoms are treated as independent indicators of an underlying disease. In contrast, the causal systems perspective seeks to understand the importance of individual symptoms and symptom-to-symptom relationships. In the current study, we used network analysis to examine the relationships between and among depression and anxiety symptoms from the causal systems perspective.

METHOD

We utilized data from a large psychiatric sample at admission and discharge from a partial hospital program (N = 1029, mean treatment duration = 8 days). We investigated features of the depression/anxiety network including topology, network centrality, stability of the network at admission and discharge, as well as change in the network over the course of treatment.

RESULTS

Individual symptoms of depression and anxiety were more related to other symptoms within each disorder than to symptoms between disorders. Sad mood and worry were among the most central symptoms in the network. The network structure was stable both at admission and between admission and discharge, although the overall strength of symptom relationships increased as symptom severity decreased over the course of treatment.

CONCLUSIONS

Examining depression and anxiety symptoms as dynamic systems may provide novel insights into the maintenance of these mental health problems.

Taxonicity and network structure of generalized anxiety disorder and major depressive disorder: An admixture analysis and complex network analysis

BACKGROUND

Recent years have witnessed sustained efforts to delineate the nosology of generalized anxiety disorder (GAD), especially in light of its substantial comorbidity with major depressive disorder (MDD). Traditional diagnostic conceptualizations regard these disorders as categorically distinct; however, extant literature attests to appreciable similarities. The application of admixture analyses and complex network analyses has become more prevalent in recent years to investigate the presence of meaningful subgroups in mental disorders and to address qualitative similarity in network structure across disorders. To date, no studies have extended these analytic techniques to determine whether GAD and MDD constitute independent syndromes. The current study used a clinical sample comprising individuals diagnosed with primary GAD or primary MDD to examine potential subgroups and network structure using symptoms of each disorder as indicators.

METHODS

The current sample comprised 111 individuals who received primary diagnoses of either GAD or MDD and completed a battery of assessments related to anxiety and depression.

RESULTS

Results of the admixture analyses converged on a single class solution, suggesting that individuals with GAD derive from the same population as those with MDD. Furthermore, results of the complex network analyses did not reveal differences in centrality parameters across disorders, suggesting qualitative similarity.

LIMITATIONS

The cross-sectional nature of this study precludes conclusions about the temporal and causal dynamics of these disorders

CONCLUSION

GAD and MDD exhibit robust similarities, as evidenced by the converging results of the admixture analyses and complex network analyses. This conclusion complements the findings of transdiagnostic research, which has identified common mechanisms underlying multiple emotional disorders.

Can network analysis transform psychopathology?

Experimental psychopathology has been the primary path to gaining causal knowledge about variables maintaining mental disorders. Yet a radically different approach to conceptualizing psychopathology promises to advance our understanding, thereby complementing traditional laboratory experiments. In contrast to viewing symptoms as reflective of underlying, latent categories or dimensions, network analysis conceptualizes symptoms as constitutive of mental disorders, not reflective of them. Disorders emerge from the causal interactions among symptoms themselves, and intervening on central symptoms in disorder networks promises to foster rapid recovery. One purpose of this article is to contrast network analysis with traditional approaches, and consider its strengths and limitations. A second purpose is to review novel computational methods that may enable researchers to discern the causal structure of disorders (e.g., Bayesian networks). I close by sketching exciting new developments in methods that have direct implications for treatment.

The application of Cartwright's concept of capacities to complex interventions in psychiatry

Cartwright and Munro argued that extrapolation of findings from randomized controlled trials to other settings can be difficult because information about the underlying causal structure and subgroups is often not available. They advocated the use of 'capacities' - that is fixed causal contributions - in predicting effects of interventions. In psychiatry, it is often not possible to determine what the fixed causal contributions are and one can only establish 'approximate capacities'. However, using 'approximate capacities' does imply a different way of evaluating health services, especially combined interventions. In health service research, if different studies, randomized controlled trials or other designs, have given different outcomes, the best way to investigate the effectiveness of a particular way of service organization is not to conduct more randomized controlled trials. It is preferable to study the effects of certain elements of the complex intervention, which have been tested before in other settings, that is investigating 'approximate capacities'. One should check whether the separate elements do form a part of the complex intervention in practice and whether they have the same effect as in other studies and if not, why not. This enhances knowledge about the underlying causal structure and increases the possibility of extrapolation of the findings.