The Study of Psychopathology from the Network Analysis Perspective: A Systematic Review

Exploring the structure of the GAD-7 scale in primary care patients with emotional disorders: A network analysis approach

OBJECTIVE

Anxiety symptoms are one of the most frequent manifestations in people attending primary care, although how the symptoms are associated is unclear. This study aimed to establish the symptom structure of the Generalized Anxiety Disorder scale (GAD-7) using a novel network approach in combination with traditional analytical tools.

METHODS

A sample of 1704 primary care patients with emotional disorders (i.e., anxiety, depression, and/or somatization) completed the GAD-7 to report their anxiety symptoms. We examined the GAD-7 structure using exploratory graph analysis (EGA) compared to exploratory factor analysis (EFA) and confirmatory factor analysis.

RESULTS

The EFA results showed a one-factor solution, but EGA revealed a two-factor solution (cognitive-emotional and somatic). "Worrying too much" and "difficulty relaxing" were the most relevant symptoms.

CONCLUSIONS

The results support the possible distinction between the somatic and cognitive-emotional components of the GAD-7, thus permitting more specific screening in primary care settings.

A computational network perspective on pediatric anxiety symptoms

BACKGROUND

While taxonomy segregates anxiety symptoms into diagnoses, patients typically present with multiple diagnoses; this poses major challenges, particularly for youth, where mixed presentation is particularly common. Anxiety comorbidity could reflect multivariate, cross-domain interactions insufficiently emphasized in current taxonomy. We utilize network analytic approaches that model these interactions by characterizing pediatric anxiety as involving distinct, inter-connected, symptom domains. Quantifying this network structure could inform views of pediatric anxiety that shape clinical practice and research.

METHODS

Participants were 4964 youths (ages 5-17 years) from seven international sites. Participants completed standard symptom inventory assessing severity along distinct domains that follow pediatric anxiety diagnostic categories. We first applied network analytic tools to quantify the anxiety domain network structure. We then examined whether variation in the network structure related to age (3-year longitudinal assessments) and sex, key moderators of pediatric anxiety expression.

RESULTS

The anxiety network featured a highly inter-connected structure; all domains correlated positively but to varying degrees. Anxiety patients and healthy youth differed in severity but demonstrated a comparable network structure. We noted specific sex differences in the network structure; longitudinal data indicated additional structural changes during childhood. Generalized-anxiety and panic symptoms consistently emerged as central domains.

CONCLUSIONS

Pediatric anxiety manifests along multiple, inter-connected symptom domains. By quantifying cross-domain associations and related moderation effects, the current study might shape views on the diagnosis, treatment, and study of pediatric anxiety.

Polygenic risk for immuno-metabolic markers and specific depressive symptoms: A multi-sample network analysis study

BACKGROUND

About every fourth patient with major depressive disorder (MDD) shows evidence of systemic inflammation. Previous studies have shown inflammation-depression associations of multiple serum inflammatory markers and multiple specific depressive symptoms. It remains unclear, however, if these associations extend to genetic/lifetime predisposition to higher inflammatory marker levels and what role metabolic factors such as Body Mass Index (BMI) play. It is also unclear whether inflammation-symptom associations reflect direct or indirect associations, which can be disentangled using network analysis.

METHODS

This study examined associations of polygenic risk scores (PRSs) for immuno-metabolic markers (C-reactive protein [CRP], interleukin [IL]-6, IL-10, tumour necrosis factor [TNF]-α, BMI) with seven depressive symptoms in one general population sample, the UK Biobank study (n = 110,010), and two patient samples, the Munich Antidepressant Response Signature (MARS, n = 1058) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D, n = 1143) studies. Network analysis was applied jointly for these samples using fused graphical least absolute shrinkage and selection operator (FGL) estimation as primary analysis and, individually, using unregularized model search estimation. Stability of results was assessed using bootstrapping and three consistency criteria were defined to appraise robustness and replicability of results across estimation methods, network bootstrapping, and samples.

RESULTS

Network analysis results displayed to-be-expected PRS-PRS and symptom-symptom associations (termed edges), respectively, that were mostly positive. Using FGL estimation, results further suggested 28, 29, and six PRS-symptom edges in MARS, STAR*D, and UK Biobank samples, respectively. Unregularized model search estimation suggested three PRS-symptom edges in the UK Biobank sample. Applying our consistency criteria to these associations indicated that only the association of higher CRP PRS with greater changes in appetite fulfilled all three criteria. Four additional associations fulfilled at least two consistency criteria; specifically, higher CRP PRS was associated with greater fatigue and reduced anhedonia, higher TNF-α PRS was associated with greater fatigue, and higher BMI PRS with greater changes in appetite and anhedonia. Associations of the BMI PRS with anhedonia, however, showed an inconsistent valence across estimation methods.

CONCLUSIONS

Genetic predisposition to higher systemic inflammatory markers are primarily associated with somatic/neurovegetative symptoms of depression such as changes in appetite and fatigue, consistent with previous studies based on circulating levels of inflammatory markers. We extend these findings by providing evidence that associations are direct (using network analysis) and extend to genetic predisposition to immuno-metabolic markers (using PRSs). Our findings can inform selection of patients with inflammation-related symptoms into clinical trials of immune-modulating drugs for MDD.

Unraveling specifics of mental health symptoms in war survivors who fled versus stayed in the area of conflict using network analysis

BACKGROUND

War survivors often report symptoms of post-traumatic stress disorder (PTSD), depression, anxiety, and somatization. Hence, understanding symptom constellations among different populations of war survivors is critical.

METHODS

Using the network approach to psychopathology, we examined symptom centrality for these conditions in war survivors from Balkan countries who had stayed in the area of former conflict compared to those individuals from Balkan countries who had fled to Western European countries (N = 4,167) with the Impact of Events Scale-Revised and the Brief Symptom Inventory. We further compared networks for war survivors who met criteria for PTSD-diagnosis (assessed with the MINI-International Neuropsychiatric Interview) to those without PTSD-diagnosis.

RESULTS

Globally, networks were similar across the groups, whereas specific differences emerged in symptom centrality. More consistencies were found between PTSD and Western country networks, which may be partially explained by a higher prevalence of PTSD in those who had fled to Western European than in those who had stayed in the Balkan countries.

LIMITATIONS

Given the cross-sectional nature of our data, the directionality of edges in our networks remains unclear. Further, higher levels of trauma exposure and symptom severity in Western country participants may have confounded results.

CONCLUSIONS

The PTSD findings are in line with previous research on PTSD symptoms. They further provide novel insights into depressive, anxiety, and somatization symptoms in survivors of war. These findings need to be substantiated and call for future intervention studies that test the effects of targeting central symptoms we identified in our study.

Longitudinal attention-deficit/hyperactivity disorder symptom networks in childhood and adolescence: Key symptoms, stability, and predictive validity

The current study visualized attention-deficit/hyperactivity disorder (ADHD) symptom networks in a longitudinal sample of participants across childhood and adolescence with exploratory examination of age and gender effects. Eight hundred thirty-six children ages 7-13 years were followed annually for 8 years in total. Across parent and teacher report, results suggested "is easily distracted" and "difficulties sustaining attention" as central symptoms across three testing points (i.e., Year 1, Year 3, and Years 5-8 collapsed). "Difficulties following instructions" and "intrudes/interrupts" also emerged as parent-reported central symptoms. Assessment of network structure across the three testing points suggested global robustness of relations among ADHD symptoms from midchildhood into early adolescence. However, relations among symptoms that cause problems in school settings (i.e., being easily distracted) were stronger in teacher-reported than parent-reported networks. When aggregated into a sum score, central symptoms during Year 1 predicted total difficulties related to mental health problems 5 years later just as well as all 18 symptoms. Central symptoms of ADHD may be useful as screeners of future emotional and behavioral difficulties. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Positive and negative urgency as a single coherent construct: Evidence from a large-scale network analysis in clinical and non-clinical samples

Aims

Negative and positive urgency are emotion‐related impulsivity traits that are thought to be transdiagnostic factors in psychopathology. However, it has recently been claimed that these two traits are closely related to each other and that considering them separately might have limited conceptual and methodological value. The present study aimed to examine whether positive and negative urgency constructs constitute separate impulsivity traits.

Methods

In contrast to previous studies that have used latent variable approaches, this study employed an item‐based network analysis conducted in two different samples: a large sample of non‐clinical participants (N = 18,568) and a sample of clinical participants with psychiatric disorders (N = 385).

Results

The network analysis demonstrated that items denoting both positive and negative urgency cohere as a single cluster of items termed “general urgency” in both clinical and non‐clinical samples, thereby suggesting that differentiating positive and negative urgency as separate constructs is not necessary.

Conclusion

These findings have important implications for the conceptualization and assessment of urgency and, more broadly, for future research on impulsivity, personality, and psychopathology.

What is binge drinking? Insights from a network perspective

This study aimed to delineate the specific characteristics of binge drinking habits by capitalizing on data-driven network analysis. Such an approach allowed us to consider binge drinking as a network system of interacting elements, thus identifying the key variables involved in this phenomenon. A total of 1,455 university students with excessive drinking habits were included in this study. We assessed the most critical features of binge drinking (i.e., the consumption of more than six alcohol units per occasion, drunkenness frequency, consumption speed), together with alcohol use and more general alcohol-related components of dysfunction and harm. All variables were considered in the network analysis. Centrality analysis identified drunkenness frequency as the most influential variable in the entire network. Community detection analysis showed three distinct subnetworks related to alcohol use, drunkenness, and dysfunction/harm components. Drunkenness frequency and blackout occurrence emerged as core bridge items in the binge drinking network. Drunkenness is recognized as the hallmark feature of binge drinking.

Network structure of depression and anxiety symptoms in Chinese female nursing students

BACKGROUND

Comorbidity between depressive and anxiety disorders is common. From network perspective, mental disorders arise from direct interactions between symptoms and comorbidity is due to direct interactions between depression and anxiety symptoms. The current study investigates the network structure of depression and anxiety symptoms in Chinese female nursing students and identifies the central and bridge symptoms as well as how other symptoms in present network are related to depression symptom "thoughts of death".

METHODS

To understand the full spectrum of depression and anxiety, we recruited 776 Chinese female nursing students with symptoms of depression and anxiety that span the full range of normal to abnormal. Depression symptoms were measured by Patient Health Questionnaire-9 while anxiety symptoms were measured by Generalized Anxiety Disorder 7-Item Questionnaire. Network analysis was used to construct networks. Specifically, we computed the predictability, expected influence and bridge expected influence for each symptom and showed a flow network of "thoughts of death".

RESULTS

Nine strongest edges existed in network were from the same disorder. Four were between depression symptoms, like "sleep difficulties" and "fatigue", and "anhedonia" and "fatigue". Five were between anxiety symptoms, like "nervousness or anxiety" and "worry too much", and "restlessness" and "afraid something will happen". The symptom "fatigue", "feeling of worthlessness" and "irritable" had the highest expected influence centrality. Results also revealed two bridge symptoms: "depressed or sad mood" and "irritable". As to "thoughts of death", the direct relations between it and "psychomotor agitation/retardation" and "feeling of worthlessness" were the strongest direct relations.

CONCLUSIONS

The current study highlighted critical central symptoms "fatigue", "feeling of worthlessness" and "irritable" and critical bridge symptoms "depressed or sad mood" and "irritable". Particularly, "psychomotor agitation/retardation" and "feeling of worthlessness" were identified as key priorities due to their strongest associations with suicide ideation. Implications for clinical prevention and intervention based on these symptoms are discussed.

Network Analysis of Psychopathology: Controversies and Challenges

Empirical publications inspired by the network approach to psychopathology have increased exponentially in the twenty-first century. The central idea that an episode of mental disorder arises from causal interactions among its symptomatic elements has especially resonated with those clinical scientists whose disenchantment with traditional categorical and dimensional approaches to mental illness has become all too apparent. As the field has matured, conceptual and statistical concerns about the limitations of network approaches to psychopathology have emerged, inspiring the development of novel methods to address these concerns. Rather than reviewing the vast empirical literature, I focus instead on the issues and controversies regarding this approach and sketch directions where the field might go next.

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

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

Symptoms as rapidly fluctuating over time: Revealing the close psychological interconnections among borderline personality disorder symptoms via within-person structures

Despite the clinical emphasis on processes happening within individuals, investigations into the psychological, structural connections between mental health symptoms have almost exclusively analyzed differences between people. These investigations have revealed important findings; however, they do not reveal the close connections among symptoms in an individuals' psychology. This study thus examined the psychological connections between symptoms directly, using borderline personality disorder (BPD) symptoms as an example. Participants (252; 74 with BPD) reported their momentary BPD symptoms five times daily, and 165 did so again 18 months later. In support of personalized medicine (Wright & Woods, 2020), individuals' BPD symptom structures differed considerably from each other and from the between-person structure. A novel technique revealed that differences were greater than expected by chance. Within-person structures tended to exhibit more symptom granularity (more factors and lower variance explained) and differing symptom meanings (patterns of loadings). For example, some individuals exhibited close connections between relationship turmoil and identity uncertainty, whereas other individuals exhibited close connections between relationship turmoil and impulsivity. Thus, conceptions of any given person's psychopathological processes using between-person structural findings will most likely be inaccurate. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The Hamilton Rating Scales for Depression: A Critical Review of Clinimetric Properties of Different Versions

The format of the original Hamilton Rating Scale for Depression (HAM-D) was unstructured: only general instructions were provided for rating individual items. Over the years, a number of modified versions of the HAM-D have been proposed. They differ not only in the number of items, but also in modalities of administration. Structured versions, including item definitions, anchor points and semi-structured or structured interview questions, were developed. This comprehensive review was conducted to examine the clinimetric properties of the different versions of the HAM-D. The aim was to identify the HAM-D versions that best display the clinimetric properties of reliability, validity, and sensitivity to change. The search was conducted on MEDLINE, Scopus, Web of Science, and PubMed, and yielded a total of 35,473 citations, but only the most representative studies were included. The structured versions of the HAM-D were found to display the highest inter-rater and test-retest reliability. The Clinical Interview for Depression and the 6-item HAM-D showed the highest sensitivity in differentiating active treatment from placebo. The findings indicate that the HAM-D is a valid and sensitive clinimetric index, which should not be discarded in view of obsolete and not clinically relevant psychometric criteria. The HAM-D, however, requires an informed use: unstructured forms should be avoided and the type of HAM-D version that is selected should be specified in the registration of the study protocol and in the methods of the trial.

An alternative approach to future diagnostic standards for major depressive disorder

During the period extending from 1780 to 1880, the conceptualization of melancholia changed from an intellectual to a mood model. The modern view of depression, based on Kraepelinian dualism, has reflected changes in opinion on psychiatric taxonomy of individual melancholia. From the point of view of an "operational revolution," the diagnostic criteria for major depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders, 3rd edition (DSM-III) were based on a neoKraepelinian approach rooted in disease essentialism. In the revision process from the DSM-IV to the DSM-5, a combined dimensional and categorial approach was used. In the DSM-5, the diagnostic criteria for major depressive disorder are polythetic and operational in approach reflecting the heterogeneity of major depressive disorder. Although 227 different symptom combinations fulfilling the diagnostic criteria for major depressive disorder can be theoretically calculated, certain symptom combinations are more prevalent than others in real clinical situations. The heterogeneity of these operational criteria for major depressive disorder have been criticized in a manner informed by the Wittgensteinian analogy of the language game. Herein, our network analysis proposes a novel perspective on the psychopathology of major depressive disorder. The novel approach suggested here may lay the foundation for a redefinition of the traditional taxonomy of depression.

Sampling Variability Is Not Nonreplication: A Bayesian Reanalysis of Forbes, Wright, Markon, and Krueger

Forbes, Wright, Markon, and Krueger claim that psychopathology network characteristics have "limited" or "poor" replicability, supporting their argument primarily with data from two waves of an observational study on depression and anxiety. They developed "direct metrics" to gauge change across networks (e.g., change in edge sign), and used these results to support their conclusion. Three key flaws undermine their critique. First, nonreplication across empirical datasets does not provide evidence against a method; such evaluations of methods are possible only in controlled simulations when the data-generating model is known. Second, they assert that the removal of shared variance necessarily decreases reliability. This is not true. Depending on the causal model, it can either increase or decrease reliability. Third, their direct metrics do not account for normal sampling variability, leaving open the possibility that the direct differences between samples are due to normal, unproblematic fluctuations. As an alternative to their direct metrics, we provide a Bayesian re-analysis that quantifies uncertainty and compares relative evidence for replication (i.e., equivalence) versus nonreplication (i.e., nonequivalence) for each network edge. This approach provides a principled roadmap for future assessments of network replicability. Our analysis indicated substantial evidence for replication and scant evidence for nonreplication.

The centrality of secure attachment within an interacting network of symptoms, cognition, and attachment dimensions in persons with schizophrenia-spectrum disorders: A preliminary study

BACKGROUND

Research in the field of psychosis broadly suggests that symptoms, neurocognitive deficits, social cognition, cognitive biases, and attachment experiences influence each other. However, little is known if any of these constructions play a more central role than others as they interact.

METHOD

To clarify this issue, we conducted a "network" analysis to explore the interplay among a set of variables related to attachment, cognition domains, and psychotic symptoms in a small sample of outpatients with stabilised schizophrenia-spectrum disorders (n = 25). Eighteen participants (72%) were first-episode patients. We assessed psychotic symptoms, attachment dimensions, neurocognitive performance, "theory of mind", emotion recognition, and "jumping to conclusions" bias using standardised instruments.

RESULTS

The study provides preliminary evidence about a network structure in which the secure attachment (SA) is the most central "node" within the interacting network considering all centrality measures, followed by general psychopathology. SA was closely connected to self-sufficiency (avoidant attachment) and child traumatism, as well as with neurocognition. Emotion recognition impairment was the most robust connection to positive symptoms and mediated the influence of SA on psychotic symptoms.

CONCLUSIONS

Beyond the importance of symptoms, our results, although preliminary, suggest the need to assess attachment experiences and cognition domains to improve specific interventions that can promote recovery in outpatients with psychosis.

Kellner's Symptom Questionnaire, a Highly Sensitive Patient-Reported Outcome Measure: Systematic Review of Clinimetric Properties

INTRODUCTION

Patient-reported outcomes (PROs) are of increasing importance in clinical medicine. However, their evaluation by classic psychometric methods carries considerable limitations. The clinimetric approach provides a viable framework for their assessment.

OBJECTIVE

The aim of this paper was to provide a systematic review of clinimetric properties of the Symptom Questionnaire (SQ), a simple, self-rated instrument for the assessment of psychological symptoms (depression, anxiety, hostility, and somatization) and well-being (contentment, relaxation, friendliness, and physical well-being).

METHODS

The PRISMA guidelines were used. Electronic databases were searched from inception up to March 2019. Only original research articles, published in English, reporting data about the clinimetric properties of the SQ, were included.

RESULTS

A total of 284 studies was selected. The SQ has been used in populations of adults, adolescents, and older individuals. The scale significantly discriminated between subgroups of subjects in both clinical and nonclinical settings, and differentiated medical and psychiatric patients from healthy controls. In longitudinal studies and in controlled pharmacological and psychotherapy trials, it was highly sensitive to symptoms and well-being changes and discriminated between the effects of psychotropic drugs and placebo.

CONCLUSIONS

The SQ is a highly sensitive clinimetric index. It may yield clinical information that similar scales would fail to provide and has a unique position among the PROs that are available. Its use in clinical trials is strongly recommended.

The Revised Mood Rhythm Instrument: A Large Multicultural Psychometric Study

Background: Recent studies with the mood rhythm instrument (MRhI) have shown that the presence of recurrent daily peaks in specific mood symptoms are significantly associated with increased risk of psychiatric disorders. Using a large sample collected in Brazil, Spain, and Canada, we aimed to analyze which MRhI items maintained good psychometric properties across cultures. As a secondary aim, we used network analysis to visualize the strength of the association between the MRhI items. Methods: Adults (n = 1275) between 18–60 years old from Spain (n = 458), Brazil (n = 415), and Canada (n = 401) completed the MRhI and the self-reporting questionnaire (SRQ-20). Psychometric analyses followed three steps: Factor analysis, item response theory, and network analysis. Results: The factor analysis indicated the retention of three factors that grouped the MRhI items into cognitive, somatic, and affective domains. The item response theory analysis suggested the exclusion of items that displayed a significant divergence in difficulty measures between countries. Finally, the network analysis revealed a structure where sleepiness plays a central role in connecting the three domains. These psychometric analyses enabled a psychometric-based refinement of the MRhI, where the 11 items with good properties across cultures were kept in a shorter, revised MRhI version (MRhI-r). Limitations: Participants were mainly university students and, as we did not conduct a formal clinical assessment, any potential correlations (beyond the validated SRQ) cannot be ascertained. Conclusions: The MRhI-r is a novel tool to investigate self-perceived rhythmicity of mood-related symptoms and behaviors, with good psychometric properties across multiple cultures.

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 network approach to parental burnout

BACKGROUND

The use of network analyses in psychology has increasingly gained traction in the last few years. A network perspective views psychological constructs as dynamic systems of interacting elements.

OBJECTIVE

We present the first study to apply network analyses to examine how the hallmark features of parental burnout - i.e., exhaustion related to the parental role, emotional distancing from children, and a sense of ineffectiveness in the parental role - interact with one another and with maladaptive behaviors related to the partner and the child(ren), when these variables are conceptualized as a network system.

PARTICIPANTS AND SETTING

In a preregistered fashion, we reanalyzed the data from a French-speaking sample (n = 1551; previously published in Mikolajczak, Brianda et al., 2018), focusing on seven specific variables: the three hallmark parental burnout features, partner conflict, partner estrangement, neglectful behavior toward children, and violent behavior toward children.

METHODS

We computed two types of network models, a graphical Gaussian model to examine network structure, potential communities, and influential nodes, and a directed acyclic graph to examine the probabilistic dependencies among the different variables.

RESULTS

Both network models pointed to emotional distance as an especially potent mechanism in activating all other nodes.

CONCLUSIONS

These results suggest emotional distance as critical to the maintenance of the parental burnout network and a prime candidate for future interventions, while affirming that network analysis can successfully expose the structure and relationship of variables related to parental burnout and its consequences related to the partner and the child(ren).

Norms According to Age and Gender for the Spanish Version of the Inventory of Depression and Anxiety Symptoms (IDAS-II)

Inventory of Depression and Anxiety Symptoms-II (IDAS-II) constitutes a useful measurement tool with demonstrated psychometric properties that is contributing to the advancement of knowledge of emotional disorders within transdiagnostic models. To implement its use in clinical settings it is important that the scores can be interpreted in order to guide clinical decisions. This study aims to develops normative data for the Spanish version of the IDAS-II. An anonymous online survey was applied to 1,072 subjects, recruited through a stratified random sampling procedure taking into account population gender, age, and geographical region of Spain. Results show that women tend to score higher than men, particularly on the Dysphoria, General Depression, Appetite Gain, and Lassitude scales. Largest effect sizes for differences in the scores according to age were found for Lassitude, Dysphoria, and General Depression. Therefore, normative data according to gender and age group for each IDAS-II scale is provided. The norms provided in this work complement those already available, facilitating the decision-making of clinical professionals. Evidence of unidimensionality is provided for the 19 IDAS-II scales that allows researchers and clinicians to use specific IDAS-II scales independently.

Dyskinesia is most centrally situated in an estimated network of extrapyramidal syndrome in Asian patients with schizophrenia: findings from research on Asian psychotropic prescription patterns for antipsychotics

BACKGROUND

Network analysis provides a new viewpoint that explicates intertwined and interrelated symptoms into dynamic causal architectures of symptom clusters. This is a process called 'symptomics' and is concurrently applied to various areas of symptomatology.

AIMS

Using the data from Research on Asian Psychotropic Prescription Patterns for Antipsychotics (REAP-AP), we aimed to estimate a network model of extrapyramidal syndrome in patients with schizophrenia.

METHODS

Using data from REAP-AP, extrapyramidal symptoms of 1046 Asian patients with schizophrenia were evaluated using the nine items of the Drug-Induced Extrapyramidal Symptoms Scale (DIEPSS). The estimated network of the ordered-categorical DIEPSS items consisted of nodes (symptoms) and edges (interconnections). A community detection algorithm was also used to identify distinctive symptom clusters, and correlation stability coefficients were used to evaluate the centrality stability.

RESULTS

An interpretable level of node strength centrality was ensured with a correlation coefficient. An estimated network of extrapyramidal syndrome showed that 26 (72.2%) of all possible 35 edges were estimated to be greater than zero. Dyskinesia was most centrally situated within the estimated network. In addition, earlier antipsychotic-induced extrapyramidal symptoms were divided into three distinctive clusters - extrapyramidal syndrome without parkinsonism, postural instability and gait difficulty-dominant parkinsonism, and tremor-dominant parkinsonism.

CONCLUSIONS

Our findings showed that dyskinesia is the most central domain in an estimated network structure of extrapyramidal syndrome in Asian patients with schizophrenia. These findings are consistent with the speculation that acute dystonia, akathisia, and parkinsonism could be the risk factors of tardive dyskinesia.

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.

The network and dimensionality structure of affective psychoses: an exploratory graph analysis approach

BACKGROUND

The dimensional symptom structure of classes of affective psychoses, and more specifically the relationships between affective and mood symptoms, has been poorly researched. Here, we examined these questions from a network analysis perspective.

METHODS

Using Exploratory Graph Analysis (EGA) and network centrality parameters, we examined the dimensionality and network structure of 28 mood and psychotic symptoms in subjects diagnosed with schizoaffective disorder (n=124), psychotic bipolar disorder (n=345) or psychotic depression (n=245), such as in the global sample of affective psychoses.

RESULTS

EGA identified four dimensions in subjects with schizoaffective or bipolar disorders (depression, mania, positive and negative) and three dimensions in subjects with psychotic depression (depression, psychosis and activation). The item composition of dimensions and the most central symptoms varied substantially across diagnoses. The most central (i.e., interconnected) symptoms in schizoaffective disorder, psychotic bipolar disorder and psychotic depression were hallucinations, delusions and depressive mood, respectively. Classes of affective psychoses significantly differed in terms of network structure but not in network global strength.

LIMITATIONS

The cross-sectional nature of this study precludes conclusions about the causal dynamics between affective and psychotic symptoms.

CONCLUSION

EGA is a powerful tool for examining the dimensionality and network structure of symptoms in affective psychoses showing that both the interconnectivity pattern between affective and psychotic symptoms and the most central symptoms vary across classes of affective psychoses. The findings outline the value of specific diagnoses in explaining the relationships between mood and affective symptoms.

A Network Analysis of Major Depressive Disorder Symptoms and Age- and Gender-Related Differences in People over 65 in a Madrid Community Sample (Spain)

Major depressive disorder (MDD) is one of the most prevalent conditions among mental disorders in individuals over 65 years. People over 65 who suffer from MDD are often functionally impaired, chronically physically ill, and express cognitive problems. The concordance between a clinician-assessed MDD diagnosis in a primary care setting and MDD assessed with a structured clinical interview in older adults is only approximately 18%. Network analysis may provide an alternative statistical technique to better understand MDD in this population by a dimensional approach to symptomatology. The aim of this study was to carry out a network analysis of major depressive disorder (MDD) in people over 65 years old. A symptom network analysis was conducted according to age and gender in 555 people over 65, using a sample from the MentDis_ICF65+ Study. The results revealed different networks for men and women, and for the age groups 65–74 and 75–84. While depressive mood stood out in women, in men the network was more dispersed with fatigue or loss of energy and sleep disturbances as the main symptoms. In the 65–74 age group, the network was complex; however, in the 75–84 age group, the network was simpler with sleep disturbances as the central symptom. The gaps between the networks indicate the different characteristics of MDD in the elderly, with variations by gender and age, supporting the idea that MDD is a complex dynamic system that has unique characteristics in each person, rather than a prototypical classification with an underlying mental disorder. These unique characteristics can be taken into account in the clinical practice for detection and intervention of MDD.

The relationship between demoralization and depressive symptoms among patients from the general hospital: network and exploratory graph analysis

INTRODUCTION

Depression and demoralization are highly prevalent among individuals with physical illnesses but their relationship is still unclear.

OBJECTIVE

To examine the relationship between clinical features of depression and demoralization with the network approach to psychopathology.

METHODS

Participants were recruited from the medical wards of a University Hospital in Italy. The Demoralization Scale (DS) was used to assess demoralization, while the Patient Health Questionnaire-9 (PHQ-9) to assess depressive symptoms. The structure of the depression-demoralization symptom network was examined and complemented by the analysis of topological overlap and Exploratory Graph Analysis (EGA) to identify the most relevant groupings (communities) of symptoms and their connections. The stability of network models was estimated with bootstrap procedures and results were compared with factor analysis.

RESULTS

Life feeling pointless, low mood/discouragement, hopelessness and feeling trapped were among the most central features of the network. EGA identified four communities: (1) Neurovegetative Depression, (2) Loss of purpose, (3) Frustrated Isolation and (4) Low mood and morale. Loss of purpose and low mood/morale were largely connected with other communities through anhedonia, hopelessness and items related to isolation and lack of emotional control. Results from EGA displayed good stability and were comparable to those from factor analysis.

LIMITATIONS

Cross-sectional design; sample heterogeneity CONCLUSIONS: Among general hospital inpatients, features of depression and demoralization are independent, with the exception of low mood and self-reproach. The identification of symptom groupings around entrapment and helplessness may provide a basis for a dimensional characterization of depressed/demoralized patients, with possible implications for treatment.

Why we should move from reductionism and embrace a network approach to parental burnout

Network science has allowed varied scientific fields to investigate and visualize complex relations between many variables, and psychology research has begun to adopt a network perspective. In this paper, we consider how leaving behind reductionist approaches and instead embracing a network perspective can advance the field of parental burnout. Although research into parental burnout is in its early stages, we argue that a network approach to parental burnout could set the scene for radically new vistas in parental burnout research. We claim that such an approach can allow simultaneous investigations (and clear visualizations) of many variables related to parental burnout and their interactions, integrates smoothly with prior family systems theories, and prioritizes dynamic research questions. We likewise discuss potential future clinical applications, such as interventions targeting central nodes and treatment personalized to a specific family's network system. We also review practical considerations, limitations, and future directions for researchers interested in applying a network approach to parental burnout research.

On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology

BACKGROUND

In the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms' causal influence within a network. Accordingly, centrality measures have been used in numerous network-based cross-sectional studies to identify specific treatment targets, based on the assumption that deactivating highly central nodes would proliferate to other nodes in the network, thereby collapsing the network structure and alleviating the overall psychopathology (i.e., the centrality hypothesis).

METHODS

Here, we summarize three types of evidence pertaining to the centrality hypothesis in psychopathology. First, we discuss the validity of the theoretical assumptions underlying the centrality hypothesis in psychopathology. We then summarize the methodological aspects of extant studies using centrality measures as predictors of symptom change following treatment, while delineating their main findings and several of their limitations. Finally, using a specific dataset of 710 treatment-seeking patients with posttraumatic stress disorder (PTSD) as an example, we empirically examine node centrality as a predictor of therapeutic change, replicating the approach taken by previous studies, while addressing some of their limitations. Specifically, we investigated whether three pre-treatment centrality indices (strength, predictability, and expected influence) were significantly correlated with the strength of the association between a symptom's change and the change in the severity of all other symptoms in the network from pre- to post-treatment (Δnode-Δnetwork association). Using similar analyses, we also examine the predictive validity of two simple non-causal node properties (mean symptom severity and infrequency of symptom endorsement).

RESULTS

Of the three centrality measures, only expected influence successfully predicted how strongly changes in nodes/symptoms were associated with change in the remainder of the nodes/symptoms. Importantly, when excluding the amnesia node, a well-documented outlier in the phenomenology of PTSD, none of the tested centrality measures predicted symptom change. Conversely, both mean symptom severity and infrequency of symptom endorsement, two standard non-network-derived indices, were found to be more predictive than expected influence and remained significantly predictive also after excluding amnesia from the network analyses.

CONCLUSIONS

The centrality hypothesis in its current form is ill-defined, showing no consistent supporting evidence in the context of cross-sectional, between-subject networks.

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.

The relations among worry, meta-worry, intolerance of uncertainty and attentional bias for threat in men at high risk for generalized anxiety disorder: a network analysis

BACKGROUND

Improving the psychotherapies for generalized anxiety disorder (GAD) is dependent on a deeper understanding of the relations between GAD and its associated cognitive factors. In the present study, we investigate how the core feature of GAD (i.e., worry) and its associated cognitive factors, such as meta-worry, intolerance of uncertainty, and attention bias towards threat, relate to each other in men at high risk for GAD.

METHODS

We used network analysis to explore the relations among these variables in a cross-sectional sample of 122 men at high risk for generalized anxiety disorder. Specifically, we computed the expected influence and predictability of each variable.

RESULTS

In the final network, we found that worry and meta-worry had the highest expected influence and predictability. In contrast, attention bias towards threat showed the lowest expected influence and predictability. The estimates of the expected influence of the nodes were stable (correlation stability coefficient = 0.52).

CONCLUSIONS

The present study is the first to investigate the relations among worry, meta-worry, intolerance of uncertainty, and attention bias towards threat in men at high risk for generalized anxiety disorder. These findings indicate that worry and meta-worry may play important roles in the present network. The implications for clinical interventions and future studies are discussed.

Bridging maladaptive social self-beliefs and social anxiety: a network perspective

The Clark and Wells (1995) model of social anxiety disorder postulates that three types of maladaptive social self-beliefs (high standard, conditional, and unconditional beliefs) play a crucial role in the development of fear and avoidance of social-evaluative situations-i.e., the hallmark symptoms of social anxiety disorder. In this project, we examined associations between the three types of maladaptive social self-beliefs and fear and avoidance of social-evaluative situations in a nonclinical community sample (n = 389). We used network analysis to estimate functional relations among aspects of maladaptive self-beliefs, fear, and avoidance and computed two different network models, a graphical Gaussian model (GGM) and a directed acyclic graph (DAG). Each model estimates edges and the importance of nodes in different ways. Both GGM and DAG pointed to fear and conditional beliefs as especially potent bridges between maladaptive social self-beliefs and social anxiety in our nonclinical sample. Altogether, these results offer data-driven heuristics in the field's larger, ongoing effort to illuminate pathways at play in the development of social anxiety. We situate this study within novel network approaches for developing theory-driven models and tests of the instigation and interactions of maladaptive social self-beliefs and social anxiety. However, because this is the first study to combine GGM and DAG in social anxiety research, we also discussed the caveats to this approach to help to usher the field forward.

A complex systems approach to the study of change in psychotherapy

BACKGROUND

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

DISCUSSION

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

CONCLUSIONS

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

An exploration of the DSM-5 posttraumatic stress disorder symptom latent variable network

BACKGROUND

Both the latent variable model and the network model have been widely used to conceptualize mental disorders. However, it has been pointed out that there is no clear dichotomy between the two models, and a combination of these two model could enable a better understanding of psychopathology. The recently proposed latent network model (LNM) has provided a statistical framework to enable this combination. Evidence has shown that posttraumatic stress disorder (PTSD) could be a suitable candidate disorder to study the combined model. In the current study, we initiated the first investigation of the latent network of PTSD symptoms.

METHODS

The latent network of DSM-5 PTSD symptoms was estimated in 1196 adult survivors of China's 2008 Wenchuan earthquake. Validation testing of the latent network was conducted in a replication sample of children and adolescent who experienced various trauma types. PTSD symptoms were measured by the PTSD Checklist for DSM-5 (PCL-5). The latent network was estimated using the seven-factor hybrid model of DSM-5 PTSD symptoms, analysed using the R package lvnet.

RESULTS

The latent network model demonstrated good fit in both samples. A strong weighted edge between the intrusion and avoidance dimensions was identified (regularized partial correlation = 0.75). The externalizing behaviour dimension demonstrated the highest centrality in the latent network.

CONCLUSIONS

This study is the first to investigate the latent network of DSM-5 PTSD symptoms. Results suggest that both latent symptom dimension and associations between the dimensions should be considered in future PTSD studies and clinical practices.

The Symptom and Domain Structure of Psychotic Disorders: A Network Analysis Approach

Little is understood about the symptom network structure of psychotic disorders. In the current study, we aimed to examine the network structure of psychotic symptoms in a broad and transdiagnostic sample of subjects with psychotic disorders (n = 2240) and to determine whether network structure parameters vary across demographic, sampling method and clinical variables. Gaussian graphical models were estimated for 73 psychotic symptoms assessed using the Comprehensive Assessment of Symptoms and History. A 7-cluster solution (reality distortion, disorganization, catatonia, diminished expressivity, avolition/anhedonia, mania, and depression) best explained the underlying symptom structure of the network. Symptoms with the highest centrality estimates pertained to the disorganization and, to a lesser extent, negative domains. Most bridge symptoms pertained to the disorganization domain, which had a central position within the network and widespread connections with other psychopathological domains. A comparison of networks in subgroups of subjects defined by premorbid adjustment levels, treatment response, and course pattern significantly influenced both network global strength and network structure. The sampling method and diagnostic class influenced network structure but not network global strength. Subgroups of subjects with less densely connected networks had poorer outcomes or more illness severity than those with more densely connected networks. The network structure of psychotic features emphasizes the importance of disorganization symptoms as a central domain of psychopathology and raises the possibility that interventions that target these symptoms may prove of broad use across psychopathology. The network structure of psychotic disorders is dependent on the sampling method and important clinical variables.

The network approach to psychopathology: a review of the literature 2008-2018 and an agenda for future research

The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, we turn our attention to the next decade of the network approach and propose critical avenues for future research in each of these domains. We argue that this program of research will be best served by working toward two overarching aims: (a) the identification of robust empirical phenomena and (b) the development of formal theories that can explain those phenomena. We recommend specific steps forward within this broad framework and argue that these steps are necessary if the network approach is to develop into a progressive program of research capable of producing a cumulative body of knowledge about how specific mental disorders operate as causal systems.

The network approach to posttraumatic stress disorder: a systematic review

Background: The empirical literature of network analysis studies of posttraumatic stress symptoms (PTSS) has grown rapidly over the last years. Objective: We aimed to assess the characteristics of these studies, and if possible, the most and least central symptoms and the strongest edges in the networks of PTSS. Method: The present systematic review, conducted in PsycInfo, Medline, and Web of Science, synthesizes findings from 20 cross-sectional PTSS network studies that were accepted for publication between January 2010 and November 2018 (PROSPERO ID: CRD42018112825). Results: Results indicated that the network studies investigated a broad range of samples and that most studies used similar analytic approaches including stability analysis. Only strength centrality was generally adequately stable. Amnesia was consistently reported to have lowest strength, while there was substantial heterogeneity regarding which nodes had highest strength centrality. The strongest edge weights were typically within each DSM-IV/DSM-5 PTSD symptom cluster. Conclusions: Hypothesis-driven studies are needed to determine whether the heterogeneity in networks resulted from differences in samples or whether they are the product of underlying methodological reasons.

Relation between PTSD symptom clusters and positive memory characteristics: A network perspective

Positive memory characteristics relate to posttraumatic stress disorder (PTSD) severity. We utilized a network approach to examine relations between PTSD clusters (intrusions, avoidance, negative alterations in cognitions and mood [NACM], alterations in arousal and reactivity [AAR]) and positive memory characteristics (count, accessibility, valence, vividness, coherence, time perspective, sensory details). We identified differential relations between PTSD clusters and positive memory characteristics, and central/bridging symptoms. Participants were an Amazon Mechanical Turk-recruited sample of 206 individuals (M_age = 35.36; 61.20% females). We estimated a regularized Gaussian Graphic Model comprising four nodes representing the PTSD clusters and six nodes representing positive memory characteristics. Regarding cross-community relations, AAR (highest node strength) was negatively associated with positive memory count, valence, coherence, and accessibility; avoidance was positively and negatively associated with positive memory vividness and count respectively. The NACM-AAR and intrusion-avoidance edges were significantly stronger than most edges. From the PTSD community, AAR and avoidance had the highest bridge strength and bridge expected influence respectively; from the positive memory community, coherence and vividness had the highest bridge strength and bridge expected influence respectively. Results indicate the potential pivotal role of AAR, avoidance, coherence, and vividness in the PTSD-positive memory relation, which renders them assessment/treatment targets pending further investigation.

The Differential Role of Central and Bridge Symptoms in Deactivating Psychopathological Networks

The network model of psychopathology suggests that central and bridge symptoms represent promising treatment targets because they may accelerate the deactivation of the network of interactions between the symptoms of mental disorders. However, the evidence confirming this hypothesis is scarce. This study re-analyzed a convenience sample of 51 cross-sectional psychopathological networks published in previous studies addressing diverse mental disorders or clinically relevant problems. In order to address the hypothesis that central and bridge symptoms are valuable treatment targets, this study simulated five distinct attack conditions on the psychopathological networks by deactivating symptoms based on two characteristics of central symptoms (degree and strength), two characteristics of bridge symptoms (overlap and bridgeness), and at random. The differential impact of the characteristics of these symptoms was assessed in terms of the magnitude and the extent of the attack required to achieve a maximum impact on the number of components, average path length, and connectivity. Only moderate evidence was obtained to sustain the hypothesis that central and bridge symptoms constitute preferential treatment targets. The results suggest that the degree, strength, and bridgeness attack conditions are more effective than the random attack condition only in increasing the number of components of the psychopathological networks. The degree attack condition seemed to perform better than the strength, bridgeness, and overlap attack conditions. Overlapping symptoms evidenced limited impact on the psychopathological networks. The need to address the basic mechanisms underlying the structure and dynamics of psychopathological networks through the expansion of the current methodological framework and its consolidation in more robust theories is stressed.

Diagnostic Issues of Depressive Disorders from Kraepelinian Dualism to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

Because the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) was mainly influenced by the neo-Kraepelinian approach, its categorical approach to defining mental disorders has been criticized from the viewpoint of etiological neutrality. In the context of bridging the gap between "presumed etiologies-based symptomatology" and "identifiable pathophysiological etiologies," the content in 5th edition, the DSM-5, has been revised to incorporate a combination of categorical and dimensional approaches. The most remarkable change of note regarding the diagnostic classification of depressive disorders in the DSM-5 is the splitting of mood disorders into bipolar disorders and depressive disorders, which is in accordance with the deconstruction of the Kraepelinian dualism for psychoses. The transdiagnostic specifiers "with mixed features," "with psychotic features," and "with anxious distress" are introduced to describe the relationships of depressive disorders with bipolar disorders, schizophrenia, and generalized anxiety disorder, respectively, in a dimensional manner. The lowering of the diagnostic threshold for major depressive disorder (MDD) may be caused by the addition of "hopelessness" to the subjective descriptors of depressive mood and the elimination of "bereavement exclusion" from the definition of MDD. Since the heterogeneity of MDD is equivalent to the Wittgensteinian "games" analogy, the different types of MDD are related not by a single essential feature but rather by "family resemblance." Network analyses of MDD symptoms may therefore need further review to elucidate the connections among interrelated symptoms and other clinical elements.

Does mindfulness change the mind? A novel psychonectome perspective based on Network Analysis

If the brain is a complex network of functionally specialized areas, it might be expected that mental representations could also behave in a similar way. We propose the concept of ‘psychonectome’ to formalize the idea of psychological constructs forming a dynamic network of mutually dependent elements. As a proof-of-concept of the psychonectome, networks analysis (NA) was used to explore structural changes in the network of constructs resulting from a psychological intervention. NA was applied to explore the effects of an 8-week Mindfulness-Based Stress Reduction (MBSR) program in healthy participants (N = 182). Psychological functioning was measured by questionnaires assessing five key domains related to MBSR: mindfulness, compassion, psychological well-being, psychological distress and emotional-cognitive control. A total of 25 variables, covering the five constructs, were considered as nodes in the NA. Participants significantly improved in most of the psychological questionnaires. More interesting from a network perspective, there were also significant changes in the topological relationships among the elements. Expected influence and strength centrality indexes revealed that mindfulness and well-being measures were the most central nodes in the networks. The nodes with highest topological change after the MBSR were attentional control, compassion measures, depression and thought suppression. Also, cognitive appraisal, an adaptive emotion regulation strategy, was associated to rumination before the MBSR program but became related to mindfulness and well-being measures after the program. Community analysis revealed a strong topological association between mindfulness, compassion, and emotional regulation, which supports the key role of compassion in mindfulness training. These results highlight the importance of exploring psychological changes from a network perspective and support the conceptual advantage of considering the interconnectedness of psychological constructs in terms of a ‘psychonectome’ as it may reveal ways of functioning that cannot be analyzed through conventional analytic methods.

Depressive Symptom Network Associated With Comorbid Anxiety in Late-Life Depression

Background: Depression and anxiety are the most common comorbid psychiatric disorders in the elderly. Psychiatrists have been reporting worsened depression symptoms and prognosis by comorbid anxiety symptoms. However, it is still unclear how anxiety affects the course of depression in the elderly. The aims of this study are (1) to identify the symptom network in late-life depression (LLD), and (2) to examine the role of anxiety in LLD with a network perspective. Methods: The study analyzed 776 community-based participants who were clinically diagnosed with depression and enrolled in Suwon Geriatric Mental Health Center. Network analysis was used to investigate the relationships between the symptoms of the Montgomery-Åsberg Depression Rating Scale (MADRS). The depression sample was divided into groups of low and high anxiety according to the Beck Anxiety Index. Propensity score matching (PSM) was used to minimize the effects of depression severity on the network. Network comparison test (NCT) were carried out to compare the global connectivity, global strength, and specific edge strength between the two subgroups. Results: Reported sadness, pessimistic thinking, and suicidal ideation are the core symptoms of LLD in terms of node strength. The MADRS sum score [mean (SD) 28.10 (9.19) vs 20.08 (7.11); P < .01] was much higher in the high anxiety group. The NCT before PSM showed the high anxiety group had significantly higher global strength (P < .01). However, the NCT after PSM did not reveal any statistical significance both in global structure (P = .46) and global strength (P = .26). A comparison between centrality indices showed a higher node strength of vegetative symptoms in the high anxiety group and this also remained after PSM. Conclusion: Based on the statistical analysis, anxiety worsens the severity of depression in the elderly. However, NCT after PSM revealed comorbid anxiety does not change the global structure and strength of the depression symptom network. Therefore, anxiety may affect LLD in a way of worsening the severity, rather than changing psychopathology. Additionally, the study revealed the centrality of vegetative symptoms was low in LLD but increased substantially in patients with comorbid anxiety.