False alarm? A comprehensive reanalysis of "Evidence that psychopathology symptom networks have limited replicability" by Forbes, Wright, Markon, and Krueger (2017)

Exploring the Effects of Sampling Variability, Scale Variability, and Node Aggregation on the Consistency of Estimated Networks

Work surrounding the replicability and generalizability of network models has increased in recent years, prompting debate on whether network properties can be expected to be consistent across samples. To date, certain methodological practices may have contributed to observed inconsistencies, including use of single-item indicators and non-identical measurement tools. The current study used a resampling approach to disentangle the effects of sampling variability from scale variability when assessing network replicability in empirical data. Additionally, we explored whether consistencies in network characteristics were improved when more items were aggregated to estimate node scores, which we hypothesized should yield more representative measures of latent constructs. Overall, using different scales produced more variability in network properties than using different samples, but these discrepancies were markedly reduced with larger samples and greater node aggregation. Findings underscored the impact of aggregating items when estimating nodes: Multi-item indicators led to denser networks, higher network sensitivity, greater estimates of global strength, and greater levels of consistency in network properties (e.g., edge weights, centrality scores). Taken together, variability in network properties across samples may arise from poor measurement conditions; additionally, variability may reflect properties of the true network model and/or the measurement instrument. All data and syntax are openly available online (https://osf.io/m37q2/).

New diagnosis in psychiatry: beyond heuristics

BACKGROUND

Diagnosis in psychiatry faces familiar challenges. Validity and utility remain elusive, and confusion regarding the fluid and arbitrary border between mental health and illness is increasing. The mainstream strategy has been conservative and iterative, retaining current nosology until something better emerges. However, this has led to stagnation. New conceptual frameworks are urgently required to catalyze a genuine paradigm shift.

METHODS

We outline candidate strategies that could pave the way for such a paradigm shift. These include the Research Domain Criteria (RDoC), the Hierarchical Taxonomy of Psychopathology (HiTOP), and Clinical Staging, which all promote a blend of dimensional and categorical approaches.

RESULTS

These alternative still heuristic transdiagnostic models provide varying levels of clinical and research utility. RDoC was intended to provide a framework to reorient research beyond the constraints of DSM. HiTOP began as a nosology derived from statistical methods and is now pursuing clinical utility. Clinical Staging aims to both expand the scope and refine the utility of diagnosis by the inclusion of the dimension of timing. None is yet fit for purpose. Yet they are relatively complementary, and it may be possible for them to operate as an ecosystem. Time will tell whether they have the capacity singly or jointly to deliver a paradigm shift.

CONCLUSIONS

Several heuristic models have been developed that separately or synergistically build infrastructure to enable new transdiagnostic research to define the structure, development, and mechanisms of mental disorders, to guide treatment and better meet the needs of patients, policymakers, and society.

Network analysis of the hair-based nine hormones from four neuroendocrine systems

INTRODUCTION

The stress response maintains the homeostasis of the body's internal environment and normal physiological activities, involving several neuroendocrine systems, such as the HPA axis, the HPG axis, the endocannabinoid system, and the melatonin system. However, studies on the intricate interactions among the four neuroendocrine systems are lacking, and it is not clear how these interactions are affected by demographic variables. The aim of this study was to investigate the network characteristics of hormonal networks comprising nine hormones from four neuroendocrine systems and how they were affected by demographic variables.

METHODS

252 healthy current students were recruited from Southeast University, China. The concentrations of nine hormones in their hair were measured by LC/MS methods, and hormonal network was constructed. Network analysis was used to characterize the interrelationships between hormones or neuroendocrine systems, central hormones, bridge hormones, hormonal network characteristics, and their changes in response to demographic variables.

RESULTS

Complex interactions between the HPA axis, the HPG axis, the ECS and the melatonin system formed a sparse and stable network, with cortisol and cortisone being the central hormones and melatonin as the bridge hormone. Demographic variables did not affect the overall characteristics of the network or the central hormone, but a number of specific connections in the network changed and the bridge hormones became cortisone and progesterone.

CONCLUSION

The interactions between the four stress-related neuroendocrine systems were relatively stable and were centered and initiated by the HPA axis. Demographic variables did not affect the overall structure of the network, but influenced local features of the network, such as edge weights and bridge centrality.

Coexisting patterns of posttraumatic stress disorder and depression symptoms in college students who experienced childhood maltreatment: Different types of maltreatment exposure

BACKGROUND

Childhood maltreatment is often associated with comorbid posttraumatic stress disorder (PTSD) and depression, but the impact of different types of maltreatment on this comorbidity is not well understood.

METHODS

Using network analysis, we examined differences in comorbidity patterns of PTSD and depression symptoms among college students who experienced different forms of childhood maltreatment. We selected a subsample of 2968 students (Mage = 19.38, SD = ±1.45) who reported exposure to childhood maltreatment from a larger sample of 5231 students.

RESULTS

This study showed that symptoms of negative emotions and cognitive change, intrusive symptoms, and increased alertness might play a significant role in the diagnosis and prognosis of comorbid PTSD and depression. The most central nodes in the network of physical maltreatment were flashbacks, and irritability, whereas the most central nodes in the network of emotional and compound trauma, were low mood and sadness. Moreover, network structure and strength differed significantly between maltreatment types, and differences in specific symptom associations were also observed.

CONCLUSION

Network analysis provides insights into which symptoms contribute to the development of comorbidities in individuals with different childhood maltreatment types, as well as how specific symptoms are interconnected in the network. This information can aid in developing targeted and effective interventions for different maltreatment forms.

Network analysis of adolescent non-suicidal self-injury subgroups identified through latent profile analysis

BACKGROUND

Non-suicidal self-injury (NSSI) is common among adolescents and frequently co-occurs with depression. Understanding the distinct patterns of NSSI behaviors, along with their associated risk and protective factors, is crucial for developing effective interventions.

AIM

To classify NSSI behaviors and examine interactions between risk and resilience factors in Chinese adolescents.

METHODS

A cross-sectional study involving 3967 Chinese students (51.7% female, mean age 13.58 ± 2.24 years) who completed questionnaires on parenting styles, bullying, childhood maltreatment, depression, resilience, and NSSI. Latent profile analysis (LPA) was used to identify NSSI subtypes, and network analysis explored interactions between risk and resilience factors.

RESULTS

Three NSSI subtypes were identified: NSSI with depression (18.8%), NSSI without depression (12.3%), and neither (68.9%). Bullying was the central risk factor across subtypes, while emotional control and family support were key protective factors. Statistical analyses showed significant differences between groups (P < 0.001).

CONCLUSION

This study identified three NSSI subtypes among Chinese adolescents. Bullying emerged as a central risk factor, while emotional control and family support were key protective factors. Targeting these areas may help reduce NSSI behaviors in this population.

A Note on Ising Network Analysis with Missing Data

The Ising model has become a popular psychometric model for analyzing item response data. The statistical inference of the Ising model is typically carried out via a pseudo-likelihood, as the standard likelihood approach suffers from a high computational cost when there are many variables (i.e., items). Unfortunately, the presence of missing values can hinder the use of pseudo-likelihood, and a listwise deletion approach for missing data treatment may introduce a substantial bias into the estimation and sometimes yield misleading interpretations. This paper proposes a conditional Bayesian framework for Ising network analysis with missing data, which integrates a pseudo-likelihood approach with iterative data imputation. An asymptotic theory is established for the method. Furthermore, a computationally efficient Pólya-Gamma data augmentation procedure is proposed to streamline the sampling of model parameters. The method's performance is shown through simulations and a real-world application to data on major depressive and generalized anxiety disorders from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC).

Topological properties of psychopathological networks of healthy and disordered individuals across mental disorders

The identification of psychopathological markers has been the focus of several scientific fields. The results were inconsistent due to lack of a clear nosology. Network analysis, focusing on the interactions between symptoms, provided important insights into the nosology of mental disorders. These interactions originate several topological properties that could constitute markers of psychopathology. One of these properties is network connectivity, which has been explored in recent years. However, the results have been inconsistent, and the topological properties of psychopathological networks remain largely unexplored and unknown. We compared several topological properties (i.e., connectivity, average path length, assortativity, average degree, modularity, global clustering) of psychopathological networks of healthy and disordered participants across depression (N = 2830), generalized anxiety (N = 13,463), social anxiety (N = 12,814), and obsessive-compulsive disorder (N = 16,426). Networks were estimated using Bayesian Gaussian Graphical Models. The Janson-Shannon measure of divergence was used to identify differences between the network properties. Network connectivity distinguished healthy and disordered participants' networks in all disorders. However, in depression and generalized anxiety, network connectivity was higher in healthy participants. The presence and number of motifs also distinguished the networks of healthy and disordered participants. Other topological properties (i.e., modularity, clustering, average path length and average degree) seem to be disorder-specific. The psychopathological significance of network connectivity must be clarified. Some topological properties of psychopathological networks are promising markers of psychopathology and may contribute to clarifying the nosology of mental disorders.

Comorbidity among symptoms of internet gaming disorder, social withdrawal, and depression in 3430 young people in Hong Kong: A network analysis

BACKGROUND

The present study aimed to examine the comorbidity among symptoms of internet gaming disorder (IGD), social withdrawal, and depression using the network perspective.

METHODS

An online survey recruited 3430 young people in Hong Kong (mean age = 19.4 years, 80.5 % male) via gaming channels in 2019. The participants completed the 9-item IGD Scale, Hikikomori Questionnaire, and Patient Health Questionnaire-9. Network analysis was conducted using R to estimate the central symptoms of IGD and depression in individual networks and identified the bridge symptoms in combined network of IGD, social withdrawal, and depressive symptoms.

RESULTS

All network models showed high stability. 'Withdrawal', 'Loss of control', and 'Tolerance' were the central IGD symptoms, while 'Depressed mood' and 'Self-blame/guilt' were the central depressive symptoms. The bridge symptoms were 'Gaming as escape or mood relief' from IGD cluster, 'Depressed mood' and 'Self-blame/guilt' from depression cluster, and 'Marked social isolation at home' and 'Significant distress due to social isolation' from social withdrawal cluster. The combined network showed no significant differences in network structure and global strength across gender and age groups.

LIMITATIONS

The cross-sectional sample only indicated undirected associations between the symptoms in the three clusters and could not model the intra-individual variation.

CONCLUSIONS

The present study provided the first results on the comorbidity among IGD, social withdrawal, and depression at a symptom level among Chinese young people via network analysis. The bridge symptoms highlight potential targets for interventions of comorbidity among the disorders.

Core features of callous-unemotional traits: a cross-cultural comparison of youth in four countries

With considerable debate concerning the impact of culture on the expression of callous-unemotional (CU) traits, it is unclear whether the core features of CU traits generalize to youth across cultures. This study aimed to examine whether cultural differences are reflected in the core features of CU traits and the associations among these features. Network analysis was employed to identify the core features and to examine the network structure of CU traits operationalized by the Inventory of Callous Unemotional traits (ICU) in four community youth samples from different nations (Australia, N = 190; the UK, N = 437; the USA, N = 330; China, N = 503). The item "Apologizes to people" was identified as a cross-cultural core feature in the ICU network with a greater centrality of this item compared to others in all four samples. In addition, some items were identified as culture-specific core features in the network, differing in their centrality across samples. The network structures of the youth self-report ICU items were moderately similar across samples, while the structures of parent-report items showed substantial differences. These findings have important implications for cross-cultural research on CU traits as well as practical implications for screening and treatment. The core features of ICU appear to be generalizable in youth across cultures, although cultural-specific manifestations should be noted.

Mapping of the network connection between sleep quality symptoms, depression, generalized anxiety, and burnout in the general population of Peru and El Salvador

BACKGROUND

A meta-analysis of randomized controlled trials has suggested a bidirectional relationship between sleep problems and mental health issues. Despite these findings, there is limited conclusive evidence on the relationship between sleep quality, depression, anxiety, and burnout.

OBJECTIVE

The current study aimed to evaluate the relationships between sleep quality symptoms, anxiety, depression, and burnout in samples of adult individuals from two Latin American countries, Peru and El Salvador, through network analysis and to identify key symptoms that reinforce the correlation and intensify the syndromes.

METHODS

A total of 1012 individuals from El Salvador and Peru participated, with an average age of 26.5 years (SD = 9.1). Symptom networks were constructed for both countries based on data from the Jenkins Sleep Scale, Patient Health Questionnaire-2, General Anxiety Disorder-2, and a single burnout item.

RESULTS

The results indicated that Depressed Mood, Difficulty Falling Asleep, and Nervousness were the most central symptoms in a network in the participating countries. The strongest conditional associations were found between symptoms belonging to the same construct, which were similar in both countries. Thus, there is a relationship between Nervousness and Uncontrollable Worry, Anhedonia and Depressed Mood, and Nighttime Awakenings and Difficulty in Staying Asleep. It was observed that burnout is a bridge symptom between both countries and presents stronger conditional associations with Tiredness on Awakening, Depressed Mood, and Uncontrollable Worry. Other bridge symptoms include a Depressed Mood and Nervousness. The network structure did not differ between the participants from Peru and El Salvador.

CONCLUSION

The networks formed by sleep quality, anxiety, depression, and burnout symptoms play a prominent role in the comorbidity of mental health problems among the general populations of Peru and El Salvador. The symptom-based analytical approach highlights the different diagnostic weights of these symptoms. Treatments or interventions should focus on identifying central and bridge symptoms.

How Coparenting Is Linked to Depression among Chinese Young Girls and Boys: Evidence from a Network Analysis

This study aimed to explore the relationship between parental coparenting and depression among Chinese young adolescents and potential gender differences via network analysis. Thus, 793 fourth-grade students (girls: 281 (35.40%), Mage = 9.99 years, SD = 0.59 years) were recruited from three primary schools in Northern China. The young adolescents rated their depression and perceived paternal and maternal coparenting. Network analysis was used to detect the central nodes and bridge mechanisms among coparenting and depressive components. The results indicated that paternal and maternal consistency as well as maternal conflict were the most central components in the coparenting-depression network. Paternal consistency, maternal conflict and paternal disparagement in coparenting, as well as somatic complaints and positive affect in adolescents' depression, exhibited high bridge strengths, suggesting those constructs served as vital bridges to connect the two subnetworks. Moreover, paternal consistency showed a higher bridge strength in the boys' network than the girls' one, whereas the edge linking adolescents' positive affect to paternal disparagement and integrity was stronger in the girls' network. This study contributes to the understanding of associations between parental coparenting and young adolescents' depression and offered insights into targeted interventions for early adolescent depression by enhancing parental coparenting.

Network analysis of anxiety and depressive symptoms during the COVID-19 pandemic in older adults in the United Kingdom

The health crisis caused by COVID-19 in the United Kingdom and the confinement measures that were subsequently implemented had unprecedented effects on the mental health of older adults, leading to the emergence and exacerbation of different comorbid symptoms including depression and anxiety. This study examined and compared depression and anxiety symptom networks in two specific quarantine periods (June-July and November-December) in the older adult population in the United Kingdom. We used the database of the English Longitudinal Study of Aging COVID-19 Substudy, consisting of 5797 participants in the first stage (54% women) and 6512 participants in the second stage (56% women), all over 50 years of age. The symptoms with the highest centrality in both times were: "Nervousness (A1)" and "Inability to relax (A4)" in expected influence and predictability, and "depressed mood (D1"; bridging expected influence). The latter measure along with "Irritability (A6)" overlapped in both depression and anxiety clusters in both networks. In addition, a the cross-lagged panel network model was examined in which a more significant influence on the direction of the symptom "Nervousness (A1)" by the depressive symptoms of "Anhedonia (D6)", "Hopelessness (D7)", and "Sleep problems (D3)" was observed; the latter measure has the highest predictive capability of the network. The results report which symptoms had a higher degree of centrality and transdiagnostic overlap in the cross-sectional networks (invariants) and the cross-lagged panel network model of anxious and depressive symptomatology.

Centrality statistics of symptom networks of schizophrenia: a systematic review

The network theory of psychological disorders posits that systems of symptoms cause, or are associated with, the expression of other symptoms. Substantial literature on symptom networks has been published to date, although no systematic review has been conducted exclusively on symptom networks of schizophrenia, schizoaffective disorder, and schizophreniform (people diagnosed with schizophrenia; PDS). This study aims to compare statistics of the symptom network publications on PDS in the last 21 years and identify congruences and discrepancies in the literature. More specifically, we will focus on centrality statistics. Thirty-two studies met the inclusion criteria. The results suggest that cognition, and social, and occupational functioning are central to the network of symptoms. Positive symptoms, particularly delusions were central among participants in many studies that did not include cognitive assessment. Nodes representing cognition were most central in those studies that did. Nodes representing negative symptoms were not as central as items measuring positive symptoms. Some studies that included measures of mood and affect found items or subscales measuring depression were central nodes in the networks. Cognition, and social, and occupational functioning appear to be core symptoms of schizophrenia as they are more central in the networks, compared to variables assessing positive symptoms. This seems consistent despite heterogeneity in the design of the studies.

Symptom networks analysis among people with Meniere's disease: Application for nursing care

Objectives

This study aimed to explore and visualize the relationships among multiple symptoms in patients with Meniere's disease (MD) and aid clinical nurses in the design of accurate, individualized interventions.

Methods

This study included 790 patients with MD at the Eye and ENT Hospital of Fudan University from October 2014 to December 2021. A self-designed symptom checklist was used to assess 15 MD-related symptoms and construct contemporaneous networks with all 15 symptoms in R software. Qgraph package and Fruchterman-Reingold layout were used for network visualization. Bootstrapping methods were performed to assess network accuracy and stability, and three centrality indices were adopted to describe relationships among symptoms.

Results

Symptom networks showed good accuracy and stability. "Anxiety and nervousness"(98.2%), "aural fullness"(84.4%) and "tinnitus"(82.7%) were the common symptom in MD patients, while "tinnitus", "aural fullness" and "decline in word recognition", were more serious. MD patients with longer disease duration had higher prevalence and severity for all symptoms (P < 0.05). Symptom networks showed good accuracy and stability. "Decline in word recognition," "fatigue," and "anxiety and nervousness" were at the center of the symptom networks, which had the largest strength values and closeness. "Decline in word recognition," "headache," and "spatial discrimination and poor orientation" were the symptoms with the highest betweenness with the strongest bridging effect. The ≥1-year disease group exhibited higher centralities for "drop attack" and "anxiety and nervousness," and a lower centrality for "headache" compared with the <1-year disease group.

Conclusions

The symptom networks of MD patients with varying disease durations were revealed. Clinicians and nurses must provide precision interventions tailored to modifying symptom severity and centrality. Nursing interventions should focus on word recognition issues and associated discomfort in MD patients with multiple symptoms.

Evaluating potential effects of distress symptoms' interventions on suicidality: Analyses of in silico scenarios

BACKGROUND

Complexity science perspectives like the network approach to psychopathology have emerged as a prominent methodological toolkit to generate novel hypotheses on complex etiologies surrounding various mental health problems and inform intervention targets. Such approach may be pivotal in advancing early intervention of suicidality among the younger generation (10-35 year-olds), the increasing burden of which needs to be reversed within a limited window of opportunity to avoid massive long-term repercussions. However, the network approach currently lends limited insight into the potential extent of proposed intervention targets' effectiveness, particularly for target outcomes in comorbid conditions.

METHODS

This paper proposes an in silico (i.e., computer-simulated) intervention approach that maps symptoms' complex interactions onto dynamic processes and analyzes their evolution. The proposed methodology is applied to investigate potential effects of changes in 1968 community-dwelling individuals' distress symptoms on their suicidal ideation. Analyses on specific subgroups were conducted. Results were also compared with centrality indices employed in typical network analyses.

RESULTS

Findings concur with symptom networks' centrality indices in suggesting that timely deactivating hopelessness among distressed individuals may be instrumental in preventing distress to develop into suicidal ideation. Additionally, however, they depict nuances beyond those provided by centrality indices, e.g., among young adults, reducing nervousness and tension may have similar effectiveness as deactivating hopeless in reducing suicidal ideation.

LIMITATIONS

Caution is warranted when generalizing findings here to the general population.

CONCLUSION

The proposed methodology may help facilitate timely agenda-setting in population mental health measures, and may also be augmented for future co-creation projects.

Comorbidity among depression, anxiety and stress symptoms in naturalistic clinical samples: A cross-cultural network analysis

Comorbidity between depression and anxiety is well-established across various settings and cultures. We approached comorbidity from the network psychopathology perspective and examined the depression, anxiety/autonomic arousal and stress/tension symptoms in naturalistic clinical samples from Serbia, Italy and Croatia. This was a multisite study in which regularized partial correlation networks of the symptoms, obtained via self-reports on the Depression Anxiety and Stress Scales-21 (DASS-21) in three cross-cultural, clinical samples (total N = 874), were compared with respect to centrality, edge weights, community structure and bridge centrality. A moderate degree of similarity in a number of network indices across the three networks was observed. While negative mood emerged to be the most central node, stress/tension nodes were the most likely bridge symptoms between depressive and anxiety/autonomic arousal symptoms. We demonstrated that the network structure and features in mixed clinical samples were similar across three different languages and cultures. The symptoms such as agitation, restlessness and inability to relax functioned as bridges across the three symptom communities explored in this study. Important theoretical and clinical implications were derived.

Identifying the bridge between depression and mania: A machine learning and network approach to bipolar disorder

OBJECTIVES

Although the cyclic nature of bipolarity is almost by definition a network system, no research to date has attempted to scrutinize the relationship of the two bipolar poles using network psychometrics. We used state-of-the-art network and machine learning methodologies to identify symptoms, as well as relations thereof, that bridge depression and mania.

METHODS

Observational study that used mental health data (12 symptoms for depression and 12 for mania) from a large, representative Canadian sample (the Canadian Community Health Survey of 2002). Complete data (N = 36,557; 54.6% female) were analysed using network psychometrics, in conjunction with a random forest algorithm, to examine the bidirectional interplay of depressive and manic symptoms.

RESULTS

Centrality analyses pointed to symptoms relating to emotionality and hyperactivity as being the most central aspects of depression and mania, respectively. The two syndromes were spatially segregated in the bipolar model and four symptoms appeared crucial in bridging them: sleep disturbances (insomnia and hypersomnia), anhedonia, suicidal ideation, and impulsivity. Our machine learning algorithm validated the clinical utility of central and bridge symptoms (in the prediction of lifetime episodes of mania and depression), and suggested that centrality, but not bridge, metrics map almost perfectly onto a data-driven measure of diagnostic utility.

CONCLUSIONS

Our results replicate key findings from past network studies on bipolar disorder, but also extend them by highlighting symptoms that bridge the two bipolar poles, while also demonstrating their clinical utility. If replicated, these endophenotypes could prove fruitful targets for prevention/intervention strategies for bipolar disorders.

Central psychological symptoms from a network analysis of patients with anxiety, somatoform or personality disorders before psychotherapy

BACKGROUND

Cross-sectional network analysis examines the relationships between symptoms to explain how they constitute disorders. Up to now, research focuses mostly on depression, posttraumatic stress disorder, and rarely assesses larger networks of various symptoms measured with instruments independent of classifications. Studies on large groups of psychotherapy patients are also rare.

METHODS

Analyzing triangulated maximally filtered graph (TMFG) networks of 62 psychological symptoms reported by 4616 consecutive nonpsychotic adults in 1980-2015.

RESULTS

Case-dropping and nonparametric bootstrap proved the accuracy, stability and reliability of networks in patients' sex-, age-, and time of visit divided subgroups. Feeling that others are prejudiced against the patient was the most central symptom, followed by catastrophic fears, feeling inferior and underestimated. Sadness, panic, and sex-related complaints were less central than we expected. All analysed symptoms were connected, and we found only small sex-related differences between subsamples' networks. No differences were observed for time of visit and age of patients.

LIMITATION

Analyses were cross-sectional and retrospective, not allowing examination of directionality or causality. Further, data are at the between-person level; thus, it is unknown whether the network remains constant for any person over time. One self-report checklist and building binary network method may bias results. Our results indicate how symptoms co-occured before psychotherapy, not longitudinally. Our sample included public university hospital patients, all White-Europeans, predominantly females and university students.

CONCLUSIONS

Hostile projection, catastrophic fears, feeling inferior and underestimated were the most important psychological phenomena reported before psychotherapy. Exploring these symptoms would possibly lead to enhancement of treatments.

Gender network analysis of the Eating Disorder Examination-Questionnaire (EDE-Q7) in Peruvian adults

Introduction

Background: network assessment of eating disorder (ED)-related symptomatology from a gender perspective is an important topic of study; however, there is limited research in the Latin American context. Objective: this study aimed to explore the patterns of association of the components of the Eating Disorder Examination-Questionnaire (EDE-Q7) according to gender, using two simultaneous network models in 890 Peruvian adults (63.51 % were women; mean age: 26.40). Methods: two graphs considering the gender factor were made using the R package qgrap and the merged LASSO graph. Results: higher network centrality measures were obtained for items related to body image dissatisfaction and overvaluation in women; while in the men's network, the items of food restriction and overestimation of weight were the most central symptoms. Conclusion: both network models were invariant and showed no significant differences in both structure and connections.

Heterogeneity in Individual Network Analysis: Reality or Illusion?

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

Adolescent depression beyond DSM definition: a network analysis

Calls for refining the understanding of depression beyond diagnostic criteria have been growing in recent years. We examined the prevalence and relevance of DSM and non-DSM depressive symptoms in two Brazilian school-based adolescent samples with two commonly used scales, the Patient Health Questionnaire (PHQ-A) and the Mood and Feelings Questionnaire (MFQ). We analyzed cross-sectional data from two similarly recruited samples of adolescents aged 14-16 years, as part of the Identifying Depression Early in Adolescence (IDEA) study in Brazil. We assessed dimensional depressive symptomatology using the PHQ-A in the first sample (n = 7720) and the MFQ in the second sample (n = 1070). We conducted network analyses to study symptom structure and centrality estimates of the two scales. Additionally, we compared centrality of items included (e.g., low mood, anhedonia) and not included in the DSM (e.g., low self-esteem, loneliness) in the MFQ. Sad mood and worthlessness items were the most central items in the network structure of the PHQ-A. In the MFQ sample, self-hatred and loneliness, two non-DSM features, were the most central items and DSM and non-DSM items in this scale formed a highly interconnected network of symptoms. Furthermore, analysis of the MFQ sample revealed DSM items not to be more frequent, severe or interconnected than non-DSM items, but rather part of a larger network of symptoms. A focus on symptoms might advance research on adolescent depression by enhancing our understanding of the disorder.

Inter-relationships among psychopathology, cognition, and real-life functioning in early and late phase schizophrenia: A network analysis approach

Many illness-related factors contribute to the reduction of the real-life functioning observed in people with schizophrenia (SZ). These include the psychopathological dimensions of the disorder such as positive, negative, disorganization, and depressive symptoms as well as impairment in neurocognition, social cognition, and metacognition. The associations between some of these variables change with the duration of illness (DOI), but this aspect was not explored with a network approach. This study aimed at describing and comparing the inter-relationships between psychopathological, cognitive, and functioning variables in early (DOI ≤ 5 years) and late (DOI > 5 years) phase SZ with network analyses and at assessing which variables were more strictly and directly associated with the real-life functioning. A network representation of the relationships between variables and the calculation of centrality indices were performed within each group. The two groups were compared with a network comparison test. Seventy-five patients with early and ninety-two with late phase SZ were included. No differences in the global network structure and strength were found between the two groups. In both groups, visual learning and disorganization exhibited high centrality indices and disorganization, negative symptoms, and metacognition were directly and strongly associated with real-life functioning. In conclusion, regardless of the DOI, a rehabilitation aimed at improving visual learning and disorganization (i.e., the most central variables) might reduce the strength of the associations that compose the network and therefore indirectly facilitate functional recovery. Simultaneously, therapeutic interventions targeting disorganization and metacognition might directly improve real-life functioning.

Depression networks: a systematic review of the network paradigm causal assumptions

The network paradigm for psychiatric disorder nosology was proposed based on the hypothesis that mental disorders are caused by networks of symptoms that are themselves causally related. Researchers have widely applied and integrated this paradigm to examine a variety of mental disorders, particularly depression. Existing studies generally focus on the correlation structure of symptoms, inferring causal relationships. Thus, presumption of causality may not be justified. The goal of this review was to examine the assumptions necessary for causal inference in network studies of depression. Specifically, we examined whether and how network studies address common violations of causal assumptions (i.e. no measurement error, exchangeability, and positivity). Of the 41 studies reviewed, five (12%) studies discussed sources of confounding unrelated to measurement error; none discussed positivity; and five conducted post-hoc analysis for measurement error. Depression network studies, in principle, are conducted under the assumption that symptom relationships are causal. Yet, in practice, studies seldomly discussed or adequately tested assumptions required to infer causality. Researchers continue to design studies that are unable to support the credibility of the network paradigm for the study of depression. There is a critical need to ensure scientific efforts cease to perpetuate problematic designs and findings to a potentially unsubstantiated paradigm.

Preventing comorbidity between distress and suicidality: a network analysis

Suicidality among individuals between 10 and 35 years of age may be poised to exert massive burdens on society through decreased economic productivity and increased incidence of chronic physical conditions in the individuals' later years, thereby necessitating early prevention of suicide. While research suggests that the pathway to suicidality may begin from episodes of psychological distress, such pathway may involve complex interplays between intermediary psychiatric symptoms and external stimuli that are not easily delineated through conventional means. This study applies the network approach to psychopathology to elucidate this complexity. Comorbidity between psychological distress and suicidality in 1968 community-dwelling individuals is analyzed with regularized partial correlation networks to identify their bridge symptoms and links. Temporal relationships between symptoms are analyzed through temporal symptom network formed from 453 individuals who completed subsequent follow-up surveys. Network analysis shows that feelings of hopelessness and the presence of suicidal ideation are the strongest bridge symptoms in the comorbidity symptom network, and form the only prominent link that bridges psychological distress and suicidality. Effects of sleep troubles, anxiety, and poor social relationships on suicidal ideation appear to be mediated by hopelessness. The same observations hold among individuals with and without diagnoses of psychiatric disorders, as well as young people (10-24 year-olds) and young adults (25-35 year-olds). The edge between hopelessness and suicidal ideation remains the strongest bridge link after controlling for effects of symptoms from the previous time point. Findings here provide an evidence base for both professional training in caregiving professions as well as gatekeeper training in community members to emphasize more on how to effectively recognize hopelessness, and instill hope, in young people and young adults for various types of distress.

Network analysis of body-related complaints in patients with neurotic or personality disorders referred to psychotherapy

Background

Psychopathology theory and clinical practice require the most complex knowledge about patients' complaints. In patients seeking for psychotherapy, body-related symptoms often complicate treatment.

Aim

This study aimed at examining connections between body-related symptoms, and identification of symptoms which may be responsible for emergency and sustaining of anxiety, somatoform and personality disorders with the use of network analysis.

Methods

In our retrospective research we used data from a sample of 4616 patients of the Department of Psychotherapy, University Hospital in Cracow, diagnosed with anxiety, somatoform or personality disorders. We constructed the Triangulated Maximally Filtered Graph (TMFG) networks of 44 somatoform symptoms endorsed in the symptom checklist "O" (SCL-O) and identified the most central symptoms within the network for all patients and in subgroups of women vs. men, older vs. younger, and diagnosed in 1980-2000 vs. 2000-2015. We used bootstrap to determine the accuracy and stability of five networks' parameters: strength, expected influence, eigenvector, bridge strength and hybrid centrality.

Results

The most central symptoms within the overall network, and in six subnetworks were dyspnea and migratory pains. We identified some gender-related differences, but no differences were observed for the age and time of diagnosis.

Conclusions

Self-reported dyspnea and migratory pains are potential important targets for treatment procedures.

Bayesian Uncertainty Estimation for Gaussian Graphical Models and Centrality Indices

In the network approach to psychopathology, psychological constructs are conceptualized as networks of interacting components (e.g., the symptoms of a disorder). In this network view, interest is on the degree to which symptoms influence each other, both directly and indirectly. These direct and indirect influences are often captured with centrality indices, however, the estimation method often used with these networks, the frequentist graphical LASSO (GLASSO), has difficulty estimating (uncertainty in) these measures. Bayesian estimation might provide a solution, as it is better suited to deal with bias in the sampling distribution of centrality indices. This study therefore compares estimation of symptom networks with Bayesian GLASSO- and Horseshoe priors to estimation using the frequentist GLASSO using extensive simulations. Results showed that the Bayesian GLASSO performed better than the Horseshoe, and that the Bayesian GLASSO outperformed the frequentist GLASSO with respect to bias in edge weights, centrality measures, correlation between estimated and true partial correlations, and specificity. Sensitivity was better for the frequentist GLASSO, but performance of the Bayesian GLASSO is usually close. With respect to uncertainty in the centrality measures, the Bayesian GLASSO shows good coverage for strength and closeness centrality, but uncertainty in betweenness centrality is estimated less well.

Etiological Networks of Attention-Deficit/Hyperactivity Disorder during Childhood and Adolescence

OBJECTIVE

The purpose of the current study was to use network analysis techniques to parse relations between attention-deficit/hyperactivity disorder (ADHD) symptom domains, domains of executive function, and temperament traits.

METHODS

Participants were 420 children aged 6-17 years (55% boys). The majority of the participants were Caucasian (72.86%) and 50% of the sample met diagnostic criteria for ADHD. Both parents and teachers provided ratings of participants' ADHD symptom severity. Parents completed questionnaires pertaining to participants' temperament traits, and participants completed well-validated laboratory measures of executive function.

RESULTS

Results suggested effortful control as demonstrating the strongest relations with ADHD, particularly the parent-reported inattentive symptom domain. Additionally, negative effects appeared to demonstrate weaker but still notable relations primarily with the parent-reported hyperactive/impulsive symptom domain. Measures of executive function did not appear to demonstrate relations with any measures of ADHD symptoms or temperament traits. The results were generally replicated in a distinct sample (n = 732, 7-13 years, 63% boys, 81% White), although differences emerged pertaining to the role of surgency (i.e., related to the hyperactive/impulsive symptom domain in the replication but not the primary sample).

CONCLUSIONS

Overall, findings provided support for the primary role of effortful control, as well as secondary roles for negative affect and surgency, as key risk markers for the characterization of ADHD. Additional exploration of the overlap between temperament and executive function, as pertaining to ADHD, may help clarify heterogeneity in phenotypes and suggest priorities for targeted interventions outside of traditional symptoms.

Childhood anxious/withdrawn behaviour and later anxiety disorder: a network outcome analysis of a population cohort

BACKGROUND

Several previous studies have identified a continuity between childhood anxiety/withdrawal and anxiety disorder (AD) in later life. However, not all children with anxiety/withdrawal problems will experience an AD in later life. Previous studies have shown that the severity of childhood anxiety/withdrawal accounts for some of the variability in AD outcomes. However, no studies to date have investigated how variation in features of anxiety/withdrawal may relate to continuity prognoses. The present research addresses this gap.

METHODS

Data were gathered as part of the Christchurch Health and Development Study, a 40-year population birth cohort of 1265 children born in Christchurch, New Zealand. Fifteen childhood anxiety/withdrawal items were measured at 7-9 years and AD outcomes were measured at various interviews from 15 to 40 years. Six network models were estimated. Two models estimated the network structure of childhood anxiety/withdrawal items independently for males and females. Four models estimated childhood anxiety/withdrawal items predicting adolescent AD (14-21 years) and adult AD (21-40 years) in both males and females.

RESULTS

Approximately 40% of participants met the diagnostic criteria for an AD during both the adolescent (14-21 years) and adult (21-40 years) outcome periods. Outcome networks showed that items measuring social and emotional anxious/withdrawn behaviours most frequently predicted AD outcomes. Items measuring situation-based fears and authority figure-specific anxious/withdrawn behaviour did not consistently predict AD outcomes. This applied across both the male and female subsamples.

CONCLUSIONS

Social and emotional anxious/withdrawn behaviours in middle childhood appear to carry increased risk for AD outcomes in both adolescence and adulthood.

Psychological impact of COVID-19: A cross-lagged network analysis from the English Longitudinal Study of Aging COVID-19 database

Background

The COVID-19 pandemic and its subsequent health restrictions had an unprecedented impact on mental health, contributing to the emergence and reinforcement of various psychopathological symptoms. This complex interaction needs to be examined especially in a vulnerable population such as older adults.

Objective

In the present study we analyzed network structures of depressive symptoms, anxiety, and loneliness from the English Longitudinal Study of Aging COVID-19 Substudy over two waves (Months of June-July and November-December 2020).

Methods

For this purpose, we use measures of centrality (expected and bridge-expected influence) in addition to the Clique Percolation method to identify overlapping symptoms between communities. We also use directed networks to identify direct effects between variables at the longitudinal level.

Results

UK adults aged >50 participated, Wave 1: 5,797 (54% female) and Wave 2: 6,512 (56% female). Cross-sectional findings indicated that difficulty relaxing, anxious mood, and excessive worry symptoms were the strongest and similar measures of centrality (Expected Influence) in both waves, while depressive mood was the one that allowed interconnection between all networks (bridge expected influence). On the other hand, sadness and difficulty sleeping were symptoms that reflected the highest comorbidity among all variables during the first and second waves, respectively. Finally, at the longitudinal level, we found a clear predictive effect in the direction of the nervousness symptom, which was reinforced by depressive symptoms (difficulties in enjoying life) and loneliness (feeling of being excluded or cut off from others).

Conclusion

Our findings suggest that depressive, anxious, and loneliness symptoms were dynamically reinforced as a function of pandemic context in older adults in the UK.

Developmental changes in the structure of shyness and internalizing symptoms from early to middle childhood: A network analysis

Shyness is a temperamental trait that shares considerable conceptual overlap with aspects of internalizing problems, creating difficulties in operationalizing and assessing these two constructs and their association. This study addresses these issues by employing network analyses. Participants were, white, N = 555 children (M_age  = 52.45 months, SD = 15.96, 55% girls) followed longitudinally over 4 years (2016-2010) in Norway. Teachers rated child shyness and assessed children's internalizing symptoms. Results suggest that two behavioral shyness traits were the most central aspects of shyness. The centrality of these aspects was robust across age. The most influential symptom connecting internalizing symptoms with shyness was "unhappy." Shyness became more differentiated with development, and associations between anxiety-related symptoms and shyness increased as children entered formal schooling.

Psychopathological networks in psychosis: Changes over time and clinical relevance. A long-term cohort study of first-episode psychosis

BACKGROUND

First-episode psychosis is a critical period for early interventions to reduce the risk of poor outcomes and relapse as much as possible. However, uncertainties about the long-term outcomes of symptomatology remain to be ascertained.

METHODS

The aim of the present study was to use network analysis to investigate first-episode and long-term stages of psychosis at three levels of analysis: micro, meso and macro. The sample was a cohort of 510 patients with first-episode psychoses from the SEGPEP study, who were reassessed at the long-term follow-up (n = 243). We used the Comprehensive Assessment of Symptoms and History for their assessments and lifetime outcome variables of clinical relevance.

RESULTS

Our results showed a similar pattern of clustering between first episodes and long-term follow-up in seven psychopathological dimensions at the micro level, 3 and 4 dimensions at the meso level, and one at the macro level. They also revealed significant differences between first-episode and long-term network structure and centrality measures at the three levels, showing that disorganization symptoms have more influence in long-term stabilized patients.

CONCLUSIONS

Our findings suggest a relative clustering invariance at all levels, with the presence of two domains of disorganization as the most notorious difference over time at micro level. The severity of disorganization at the follow-up was associated with a more severe course of the psychosis. Moreover, a relative stability in global strength of the interconnections was found, even though the network structure varied significantly in the long-term follow-up. The macro level was helpful in the integration of all dimensions into a common psychopathology factor, and in unveiling the strong relationships of psychopathological dimensions with lifetime outcomes, such as negative with poor functioning, disorganization with high antipsychotic dose-years, and delusions with poor adherence to treatment. These results add evidence to the hierarchical, dimensional and longitudinal structure of psychopathological symptoms and their clinical relevance in first-episode psychoses.

Generalized network psychometrics of eating-disorder psychopathology

OBJECTIVE

As network models of eating disorder (ED) psychopathology become increasingly popular in modeling symptom interconnectedness and identifying potential treatment targets, it is necessary to contextualize their performance against other methods of modeling ED psychopathology and to evaluate potential ways to optimize and capitalize on their use. To accomplish these goals, we used generalized network psychometrics to estimate and compare latent variable models and network models, as well as hybrid models.

METHOD

We tested the structure of the Eating Pathology Symptoms Inventory (EPSI) and Eating Disorder Examination-Questionnaire (EDE-Q) in Recovery Record, Inc. mobile phone application users (N = 6856).

RESULTS

Although all models fit well, results favored a hybrid latent variable and network framework, which showed that ED symptoms fit best when modeled as higher-order constructs, rather than direct symptom-to-symptom connections, and when the relationships between those constructs are described as a network. Hybrid models in which latent factors were modeled as nodes within a network showed that EPSI Purging, Binge Eating, Cognitive Restraint, Body Dissatisfaction, and Excessive Exercise had high importance in the network. EDE-Q Eating Concern and Shape Concern were also important nodes. Results showed that the EPSI network was highly stable and replicable, whereas the EDE-Q network was not.

DISCUSSION

Integrating latent variable and network model frameworks enables tests of centrality to identify important latent variables, such as purging, that may promote the spread of ED psychopathology throughout a network, allowing for the identification of future treatment targets.

Cross-sectional and longitudinal AUD symptom networks: They tell different stories

Modern theoretical models of Alcohol Use Disorder (AUD) highlight the different functional roles played by various mechanisms associated with different symptoms. Symptom network models (SNMs) offer one approach to modeling AUD symptomatology in a way that could reflect these processes and provide important information on the progression and persistence of disorder. However, much of the research conducted using SNMs relies on cross-sectional data, which has raised questions regarding the extent they reflect dynamic processes. The current study aimed to (a) examine symptom networks of AUD and (b) compare the extent to which cross-sectional network models had similar structures and interpretations as longitudinal network models. 17,360 participants from Wave 1 (2001-2002) and Wave 2 (2003-2004) of the National Epidemiological Survey on Alcohol and Related Conditions (NESARC) were used to model cross-sectional and longitudinal AUD symptom networks. The cross-sectional analyses demonstrate high replicability across waves and central symptoms consistent with other cross-sectional studies on addiction networks. The longitudinal network shared much less similarity than the cross-sectional networks and had a substantially different structure. Given the increasing attention given to the network perspective in psychopathology research, the results of this study raise concerns about interpreting cross-sectional symptom networks as representative of temporal changes occurring within a psychological disorder. We conclude that the psychological symptom network literature should be bolstered with additional research on longitudinal network models.

Examining the generalizability of research findings from archival data

The extent to which results from complex datasets generalize across contexts is critically important to numerous scientific fields as well as to practitioners who rely on such analyses to guide important strategic decisions. Our initiative systematically investigated whether findings from the field of strategic management would emerge in new time periods and new geographies. Original findings that were statistically reliable in the first place were typically obtained again in novel tests, suggesting surprisingly little sensitivity to context. For some social scientific areas of inquiry, results from a specific time and place can be a meaningful guide as to what will be observed more generally.

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples.

The network structure of cognitive deficits in first episode psychosis patients

Network analysis is an important conceptual and analytical approach in mental health research. However, few studies have used network analysis to examine the structure of cognitive performance in psychotic disorders. We examined the network structure of the cognitive scores of a sample of 207 first-episode psychosis (FEP) patients and 188 healthy controls. Participants were assessed using a battery of 10 neuropsychological tests. Fourteen cognitive scores encompassing six cognitive domains and premorbid IQ were selected to perform the network analysis. Many similarities were found in the network structure of FEP patients and healthy controls. Verbal memory, attention, working memory and executive function nodes were the most central nodes in the network. Nodes in both groups corresponding to the same tests tended to be strongly connected. Verbal memory, attention, working memory and executive function were central dimensions in the cognitive network of FEP patients and controls. These results suggest that the interplay between these core dimensions is essential for demands to solve complex tasks, and these interactions may guide the aims of cognitive rehabilitation. Network analysis of cognitive dimensions might have therapeutic implications that deserve further research.

Using network intervention analysis to explore associations between participant expectations of and difficulties with cognitive behavioural therapy for insomnia and clinical outcome: A proof of principle study

BACKGROUND

Research about predictors of response to cognitive behaviour therapy for insomnia (CBT-I) is ongoing. We examined any whether pre-intervention expectations or post-intervention appraisals of difficulties in utilizing face to face (FtF) or digital (dCBT-I) versions of the therapy were associated with outcome.

METHODS

Self-rating data were extracted on 101 adult participants in a recent randomized controlled trial of FtF versus dCBT-I. Network intervention analyses were used to explore any associations between expectations of CBT-I at response at 9 weeks and between post-intervention ratings of difficulties, modality of therapy and response at 9-weeks and at 6-months.

RESULTS

Anticipated and actual difficulties in employing sleep restriction techniques predicted response in all network models. Modality of therapy played a more overt role in the 9-week outcome network, with FtF therapy more robustly associated with response. However, the direct association between FtF therapy and response was not found in the 6-month outcome network. Notable predictors of poor outcome at 9-weeks and 6-month follow-up were difficulties in accommodating CBT-I into work and daily routines and applying the rules of CBT-I.

CONCLUSIONS

This network intervention analysis highlights that self-confidence and ability in undertaking sleep restriction is a key active ingredient of CBT-I. Also, benefits and gains from access to the FtF version of this multi-component therapy were more apparent in the short than the longer term. However, it is important that findings from this proof of principle study are confirmed in further studies.

The Puzzle of Functional Recovery in Schizophrenia-Spectrum Disorders-Replicating a Network Analysis Study

BACKGROUND AND HYPOTHESIS

Recovery from psychosis is a complex phenomenon determined by an array of variables mutually impacting each other in a manner that is not fully understood. The aim of this study is to perform an approximated replication of a previous network analysis study investigating how different clinical aspects-covering psychopathology, cognition, personal resources, functional capacity, and real-life functioning-are interrelated in the context of schizophrenia-spectrum disorders.

STUDY DESIGN

A sample of 843 subjects from a multisite cohort study, with the diagnosis of a schizophrenia-spectrum disorder, was used to estimate a network comprising 27 variables. The connectivity and relative importance of the variables was examined through network analysis. We used a quantitative and qualitative approach to infer replication quality.

STUDY RESULTS

Functional capacity and real-life functioning were central and bridged different domains of the network, in line with the replicated study. Neurocognition, interpersonal relationships, and avolition were also key elements of the network, in close relation to aspects of functioning. Despite significant methodological differences, the current study could substantially replicate previous findings.

CONCLUSIONS

Results solidify the network analysis approach in the context of mental disorders and further inform future studies about key variables in the context of recovery from psychotic disorders.

Dependence Graphs Based on Association Rules to Explore Delusional Experiences

Methods to estimate dependence graphs among variables, have quickly gained popularity in psychopathology research. To date, multiple methods have been proposed but recent studies report several drawbacks impacting on the validity of the conclusions as it is argued that assumptions and conditions underlying the methods commonly used and the nature of the data is lacking alignment. A particularly important issue is that underlying dynamics potentially present in heterogeneous datasets are disregarded, as the methods focus on the variables but not on individuals. This work also argues that the networks may lack relevant components as current methods ignore connections beyond pairwise interactions between individual symptoms. This study addresses these issues with a novel method for constructing dependence graphs based on applying Association Rules to binary records, which is often the type of records in the psychopathology domain. To demonstrate the benefits, we examine 12 delusional experiences in a sample of 1423 subjects with psychotic disorders. We show that by extracting Association Rules using an algorithm called apriori, in addition to facilitating an intuitive interpretation, previously unseen relevant dependencies are revealed from higher order interactions among psychotic experiences in subgroups of patients.

What do measures of self-report interoception measure? Insights from a systematic review, latent factor analysis, and network approach

Recent conceptualizations of interoception suggest several facets to this construct, including "interoceptive sensibility" and "self-report interoceptive scales", both of which are assessed with questionnaires. Although these conceptual efforts have helped move the field forward, uncertainty remains regarding whether current measures converge on their measurement of a common construct. To address this question, we first identified -via a systematic review- the most cited questionnaires of interoceptive sensibility. Then, we examined their correlations, their overall factorial structure, and their network structure in a large community sample (n = 1003). The results indicate that these questionnaires tap onto distinct constructs, with low overall convergence and interrelationships between questionnaire items. This observation mitigates the interpretation and replicability of findings in self-report interoception research. We call for a better match between constructs and measures.

A bibliometric analysis of 10 years of research on symptom networks in psychopathology and mental health

Psychopathology networks consist of aspects (e.g., symptoms) of mental disorders (nodes) and the connections between those aspects (edges). This article aims to analyze the research literature on network analysis in psychopathology and mental health for the last ten years. Statistical descriptive analysis was complemented with two bibliometric techniques: performance analysis and co-word analysis. There is an increase in publications that has passed from 1 article published in 2010 to 172 papers published in 2020. The 398 articles in the sample have 1,910 authors in total, being most of them occasional contributors. The Journal of Affective Disorders is the one with the highest number of publications on network analysis in psychopathology and mental health, followed by the Journal of Abnormal Psychology and Psychological Medicine stand out. The present study shows that this perspective in psychopathology and mental health is a recent field of study, but with solid advances in recent years from a wide variety of researchers, mainly from USA and Europe, who have extensively studied symptom networks in depression, anxiety, and post-traumatic stress disorders. However, gaps are identified in other psychological behaviors such as suicide, populations such as the elderly, and gender studies.

A Network Analysis Approach to Understanding the Relationship Between Childhood Trauma and Wellbeing Later in Life

Though childhood maltreatment negatively affects later in life functioning, current interventions do little to mitigate this impact. This ineffectiveness may be exacerbated by deficit-focused models which focus primarily on mental illness, ignoring other indicators of healthy functioning. This paper presents two studies that examine the relationships between childhood maltreatment and later in life functioning, including indicators of mental illness and mental health. In Study 1, network analysis was used as an exploratory tool to examine how childhood maltreatment relates to later in life wellbeing. Study 2 used a different sample of adults to provide a confirmatory test of the network obtained in Study 1 given remaining concerns about the replicability of networks from network analysis. Study 1 included a subset of participants from the Midlife Development in the United States Study 2 (MIDUS 2) Biomarker Project 4, 2004-2009. Study 2 included individuals from the MIDUS Refresher Biomarker Project 4, 2012-2016. Network comparison tests demonstrated that the networks generally replicated as they did not significantly vary in structure, global strength, or measures of strength centrality. In both studies, emotional forms of maltreatment (i.e., emotional abuse, emotional neglect) emerged as particularly influential in the networks. Childhood maltreatment impacts the ability to thrive in adulthood, beyond its impact on diagnosable mental illness, and also affects positive functioning. A stronger focus on emotional abuse and emotional neglect is warranted within maltreatment intervention and education initiatives, as is an emphasis on the impact of maltreatment on positive functioning in adulthood.

Psychometric Properties of the Neuropsychiatric Inventory: A Review

Neuropsychiatric symptoms cause a significant burden to individuals with neurocognitive disorders and their families. Insights into the clinical associations, neurobiology, and treatment of these symptoms depend on informant questionnaires, such as the commonly used Neuropsychiatric Inventory (NPI). As with any scale, the utility of the NPI relies on its psychometric properties, but the NPI faces unique challenges related to its skip-question and scoring formats. In this narrative review, we examined the psychometric properties of the NPI in a framework including properties pertinent to construct validation, and health-related outcome measurement in general. We found that aspects such as test-retest and inter-rater reliability are major strengths of the NPI in addition to its flexible and relatively quick administration. These properties are desired in clinical trials. However, the reported properties appear to cover only some of the generally examined psychometric properties, representing perhaps necessary but insufficient reliability and validity evidence for the NPI. The psychometric data seem to have significant gaps, in part because small sample sizes in the relevant studies have precluded more comprehensive analyses. Regarding construct validity, only one study has examined structural validity with the NPI subquestions. Measurement error was not assessed in the reviewed studies. For future validation, we recommend using data from all subquestions, collecting larger samples, paying specific attention to construct validity and formulating hypotheses a priori. Because the NPI is an outcome measure of interest in clinical trials, examining measurement error could be of practical importance.

Seeking bridge symptoms of anxiety, depression, and sleep disturbance among the elderly during the lockdown of the COVID-19 pandemic-A network approach

BACKGROUND

Besides physical changes, elderly adults are prone to have mental disorders such as anxiety, depression, and sleep disturbance, and the pandemic of COVID-19 worsened the situation. However, internal relationships and co-occurrence of psychopathologies were scarcely examined. Therefore, in the current study, through network analysis, we inspected relationships among symptoms of depression, anxiety, and sleep disturbance and identified key symptoms that espoused the disease.

METHODS

We asked 1,302 elderly adults to fill in Patient Health Questionnaire-2 (depressive symptoms), the Generalized Anxiety Disorder-2 (anxiety symptoms), and the Youth Self-rating Insomnia Scale (sleep disturbance) and then constructed three networks for elderly adults, male elderly, and female elderly. Via network analysis, we accomplished four goals. First, we identified symptom with the highest centrality (i.e., strength) index for each network; then, we found the strongest correlation (i.e., edges) in each network; thirdly, we confirmed specific nodes that could bridge anxiety, depression, and sleep disturbance; the last was to compare networks based on genders. Network stability and accuracy tests were performed.

RESULTS

Networks of elderly adults, male elderly, and female elderly were stable, accurate, and intelligible. Among all networks, "Nervousness"- "Excessive worry" (GAD-1- GAD-2) had the strongest correlation, and "Nervousness" (GAD-1) had the highest strength and bridge strength value. When we made a comparison between female elderly's and male elderly's networks, except for the significant difference in the mean value of "Difficulty initiating sleep" (YSIS-3), the findings showed that the two networks were similar. Network stability and accuracy proved to be reliable.

CONCLUSIONS

In networks of anxiety, depression, and sleep disturbance, anxiety played a conspicuous role in comorbidity, which could be a target for practical intervention and prevention.

Work Addiction and Work Engagement: a Network Approach to Cross-Cultural Data

This study aimed to investigate direct relationships of work addiction symptoms with dimensions of work engagement. We used three samples in which work addiction was measured with the Bergen Work Addiction Scale and work engagement was measured with the Utrecht Work Engagement Scale. One sample comprised responses from working Norwegians (n1 = 776), and two samples comprised responses from working Poles (n2 = 719; n3 = 715). We jointly estimated three networks using the fused graphic lasso method. Additionally, we estimated the stability of each network, node centrality, and node predictability and quantitatively compared all networks. The results showed that absorption and mood modification could constitute a bridge between work addiction and work engagement. It suggests that further investigation of properties of absorption and mood modification might be crucial for answering the question of how engaged workers become addicted to work.

Which symptoms of depression and anxiety are most strongly associated with happiness? A network analysis of Indian and Kenyan adolescents

BACKGROUND

Network analyses have been applied to understand the relationships between individual symptoms of depression and anxiety. However, little is known about which symptoms are most strongly associated with "positive" indicators of mental health, such as happiness. Furthermore, few studies have examined symptom networks in participants from low- and middle-income countries.

METHODS

To address these gaps, we applied network analyses in a sample of Indian adolescents (Study 1; n=1080) and replicated these analyses in a pre-registered study with Kenyan adolescents (Study 2; n=2176). Participants from both samples completed the same measures of depressive symptoms, anxiety symptoms, and happiness.

RESULTS

Feeling sad and feeling like a failure had the strongest (negative) associations with happiness items. These two symptoms, as well as worrying and feeling nervous, had the strongest associations with other symptoms of depression and anxiety. Symptoms of depression and anxiety formed a single cluster, which was distinct from a cluster of happiness items. Main findings were consistent across the two samples, suggesting a cross-culturally robust pattern.

LIMITATIONS

We used cross-sectional data, and we administered scales assessing a limited subset of symptoms and happiness items.

CONCLUSIONS

Our findings support the idea that some symptoms of depression and anxiety are more strongly associated with happiness. These findings contribute to a body of literature emphasizing the advantages of symptom-level analyses. We discuss how efforts to understand associations between individual symptoms and "positive" mental health indicators, like happiness, could have theoretical and practical implications for clinical psychological science.

A network examination of risky behaviours in a state-level and national epidemiological sample of high school students

AIM

Engagement in risky behaviours, including substance use, disordered eating, suicidal behaviour, and peer victimization/violence, during adolescence is becoming increasingly prevalent. These risky behaviours are highly comorbid and associated with long-term consequences for health, relationships, and socioeconomic status, representing an important public health concern. Past research has primarily investigated risky behaviours in adolescence using latent variable models, which are based on assumptions that may limit insight into the complex reality of these behaviours.

METHODS

The current study uses network analysis to examine adolescent substance use, disordered eating, suicide risk, and peer victimization/violence in a national (N = 29 008) and state-level (Kentucky; N = 3455) epidemiological dataset. We calculated central and bridge symptoms and compared network structure based on demographic factors (race, sex, grade) and sample (state vs. nation).

RESULTS

The most central symptoms were suicidal ideation and attempts, stimulant drug use, and prescription drug misuse. The most central bridge symptoms were depression, methamphetamine use, peer violence, and suicide attempts. There were no differences in network structure between samples or across demographic factors in the Kentucky sample. There were differences in network structure across sex and race in the national dataset.

CONCLUSIONS

These findings suggest stimulant use, suicidal ideation, depression, and peer violence may contribute to the high rates and co-occurrence of risky behaviours in adolescence. Based on network theory, these symptoms may represent important targets for intervention. Due to network differences, special considerations may be necessary to adapt such interventions to meet the needs of students from different backgrounds.

Detecting Idiographic Personality Change

Personality changes across the lifespan, but strong evidence regarding the mechanisms responsible for personality change remains elusive. Studies of personality change and life events, for example, suggest that personality is difficult to change. But there are two key issues with assessing personality change. First, most change models optimize population-level, not individual-level, effects, which ignores heterogeneity in patterns of change. Second, optimizing change as mean-levels of self-reports fails to incorporate methods for assessing personality dynamics, such as using changes in variances of and correlations in multivariate time series data that often proceed changes in mean-levels, making variance change detection a promising technique for the study of change. Using a sample of N = 388 participants (total N = 21,790) assessed weekly over 60 weeks, we test a permutation-based approach for detecting individual-level personality changes in multivariate time series and compare the results to event-based methods for assessing change. We find that a non-trivial number of participants show change over the course of the year but that there was little association between these change points and life events they experienced. We conclude by highlighting the importance in idiographic and dynamic investigations of change.