Differences in Connection Strength between Mental Symptoms Might Be Explained by Differences in Variance: Reanalysis of Network Data Did Not Confirm Staging

A new way to conceptualize intolerance of uncertainty among adolescents: Embracing the network perspective

Intolerance of uncertainty (IU), a pivotal transdiagnostic risk factor in psychopathology, is defined as a dispositional incapacity to withstand uncertainty distress, driving maladaptive cognitive, emotional and behavioural reactions to uncertainty. However, the intricate interplay among these components, particularly in adolescents, remains underexplored; yet understanding this interplay is crucial for supporting mental health. To address this gap, we employed a network approach to conceptualize IU in 5672 non-clinical Chinese adolescents (Mage = 14.13 years, SDage = 1.96 years, range = 10-19 years, 46.6% boys), combining graphical Gaussian models (GGM) and directed acyclic graphs (DAG). Our analyses revealed a tripartite network comprising cognitive, behavioural and emotional components. Notably, 'frustration' and 'work with hindrance' emerged as key drivers, while 'catastrophizing belief' served as a critical bridge linking different components. These findings underscore the importance of alleviating uncertainty-induced frustration and enhancing coping skills for behavioural impediments to mitigate adolescent IU. Additionally, therapeutic interventions should prioritize modifying and re-evaluating catastrophizing beliefs related to uncertainty.

Early change in specific depression symptoms and later outcome in internet-delivered psychotherapy for depression: A cohort study and cross-lagged network analysis

BACKGROUND

Symptom reduction occurring early in depression treatment is associated with favourable post-treatment outcome, but it is not known how early reduction in specific depression symptoms affect treatment outcome. We aimed to determine the impact of symptom-specific change from pre-treatment to week four during internet-delivered CBT (ICBT) on overall and symptom-specific depression severity at post-treatment. We hypothesized that change in mood and emotional involvement would be most strongly associated with later overall depression severity.

METHODS

1300 participants with Major Depressive Disorder were followed over 12 weeks of ICBT using the self-report Montgomery-Åsberg Depression Rating Scale gauging nine symptoms. Linear models, informed by causal inference and cross-lagged network analysis methods, were used to estimate associations between early symptom-specific change and post-treatment depression severity, controlling for register-based and self-reported pre-treatment confounders.

RESULTS

Early reduction in all symptoms was associated with lower overall and symptom-specific depression severity post-ICBT. Seven symptoms showed similar associations between early change and overall depression severity post-treatment: mood (standardized beta [β] = 0.44), feelings of unease (β = 0.39), ability to concentrate (β = 0.46), initiative (β = 0.43), emotional involvement (β = 0.42), pessimism (β = 0.44), and zest for life (β = 0.42). Change in sleep (β = 0.27) and appetite (β = 0.27) had weaker associations with overall depression severity at post-treatment and were the only symptoms showing the hypothesized difference compared with mood and emotional involvement.

CONCLUSIONS

The impact of early symptom-specific reduction on post-treatment depression severity in ICBT for MDD may be similar across most symptoms, but less for the sleep and appetite symptoms, although causal interpretations rests on several assumptions.

Unpacking interplays between competitiveness, cooperativeness, and social comparison orientation: A network psychometric approach and replication

Emerging evidence highlights the role of social comparison in competition and cooperation, yet the dynamics among competitiveness, cooperativeness, and social comparison orientation (SCO) remain underexplored. In the present study, we sought to unravel these complex interplays employing graphical Gaussian models (GGMs) and characterize the network structures utilizing cross-sectional data derived from 1073 Chinese participants. A pre-registered independent sample (n = 1348) served to examine the replicability and robustness of our initial findings. Moreover, we explored the Bayesian networks (i.e., directed acyclic graphs) to delve into their probabilistic directed dependencies. Our analysis identified an interpersonal co-opetition network where SCO bridges competitiveness and cooperativeness, emphasizing the key influence of the desire to surpass others. We also found a significant gender effect on the levels of these orientations but not on the network patterns. Additionally, the replication sample offered validation for the resultant network structure and its key nodes. In sum, these findings provide a new insight into the symbiotic relationship between competitiveness and cooperativeness within the framework of social comparison, offering significant theoretical and practical implications.

Neuroticism is Associated With Greater Affective Variability at High Levels of Momentary Affective Well-Being, but With Lower Affective Variability at Low Levels of Momentary Affective Well-Being

OBJECTIVE

Research challenged the notion that neuroticism correlates with affective variability, suggesting that it may result from statistical artifacts due to the non-normal distribution of negative affect. We aim to advance this line of research by (a) introducing affect balance as a normally distributed measure of affective well-being and (b) examining current affect balance as a moderator of the relationship between neuroticism and affect balance variability.

METHOD

We meta-analyzed the results of 14 ambulatory assessment datasets (N = 2389 participants, N = 174,423 observations).

RESULTS

We found that while the associations between the mean and affective variability were large for negative affect, they were much smaller for affect balance. Moreover, the association between neuroticism and variability in negative affect was very small, yet medium-sized for affect balance. Importantly, the latter association depended on current affect levels: Participants high relative to low in neuroticism showed stronger subsequent fluctuations in affect balance when currently feeling better than usual, but weaker subsequent fluctuations in (and thus more persistent) affect balance when currently feeling worse than usual.

CONCLUSION

Increased variability should not be seen as a bad sign but may be a sign that an affective system is changing, which may be adaptive or maladaptive for an individual, depending on the initial state of the system.

Exploring patients with COPD self-care behaviours and self-efficacy and their interconnections: A network analysis

AIM

The aim of this study was to investigate self-care behaviours of patients with chronic obstructive pulmonary disease (COPD), understand their complex interconnections and identify key behaviours influencing self-care and self-efficacy.

DESIGN

An observational, cross-sectional study design.

SETTING(S)

The outpatient department of two tertiary hospital.

METHODS

A convenience sample of patients with a diagnosis of COPD were included (n = 222). Self-care behaviours were assessed using the Chinese version of self-care of chronic obstructive pulmonary disease inventory based on the Middle-Range Theory of Self-Care of Chronic Illness. Patient self-care efficacy was evaluated using Chinese version of Self-Care Self-Efficacy Scale in Chronic Obstructive Pulmonary Disease. Employing network analysis, associations between behaviours (nodes) and their interrelationships (edges) were deciphered. The study was reported following the STROBE checklist.

RESULTS

Among the spectrum of self-care behaviours, patients exhibited the most pronounced deficiencies in behaviours directed to enhancing breathing, monitoring extra-respiratory symptoms and problem-solving. Within the network, the most central emerging behaviour was the modification of prescribed therapy at the worsening of symptoms, as suggested by healthcare providers, which drives all self-care behaviours. The confidence in being able to do something to relieve symptoms, despite difficulties, was a bridging activator of self-care. The network structure underscored the prominence of self-care self-efficacy in driving self-care maintenance, monitoring, and management behaviours.

CONCLUSIONS

Education programs for COPD should prioritise enhancing breathing, symptom monitoring and problem-solving skills. The crucial self-care behaviour in COPD involves adjusting treatments in response to symptom changes. Patient confidence in symptom alleviation acts as a catalyst for self-care engagement. Targeted assessments addressing these aspects could enhance educational interventions, ultimately improving COPD patient outcomes.

REGISTRATION

This study has been registered in the Chinese Clinical Trials Registry (registration number: ChiCTR2200059764; registration date: 11 May 2022).

A network approach to subjective cognitive decline: Exploring multivariate relationships in neuropsychological test performance across Alzheimer's disease risk states

Subjective cognitive decline (SCD) is characterized by subjective concerns of cognitive change despite test performance within normal range. Although those with SCD are at higher risk for developing further cognitive decline, we still lack methods using objective cognitive measures that reliably distinguish SCD from cognitively normal aging at the group level. Network analysis may help to address this by modeling cognitive performance as a web of intertwined cognitive abilities, providing insight into the multivariate associations determining cognitive status. Following previous network studies of mild cognitive impairment (MCI) and Alzheimer's dementia (AD), the current study centered upon the novel visualization and analysis of the SCD cognitive network compared to cognitively normal (CN) older adult, MCI, and AD group networks. Cross-sectional neuropsychological data from CIMA-Q and COMPASS-ND cohorts were used to construct Gaussian graphical models for CN (n = 122), SCD (n = 207), MCI (n = 210), and AD (n = 79) groups. Group networks were explored in terms of global network structure, prominent edge weights, and strength centrality indices. CN and SCD group networks were contrasted using the Network Comparison Test. Results indicate that CN and SCD groups did not differ in univariate cognitive performance or global network structure. However, measures of strength centrality, principally in executive functioning and processing speed, showed a CN-SCD-MCI gradient where subtle differences within the SCD network suggest that SCD is an intermediary between CN and MCI stages. Additional results may indicate a distinctiveness of network structure in AD, a reversal in network influence between age and general cognitive status as clinical impairment increases, and potential evidence for cognitive reserve. Together, these results provide evidence that network-specific metrics are sensitive to cognitive performance changes across the dementia risk spectrum and can help to objectively distinguish SCD group cognitive performance from that of the CN group.

Cross-Informant Compatibility of Depression Symptoms in Children: A Network Approach

Utilizing multiple informants to assess children's depressive symptoms increases diagnostic accuracy, reliability, and validity of inferences. However, previous studies have found low to moderate agreement among informants. We applied network statistics to gain insight into children and their mothers' differential perceptions of depressive symptoms. The sample included children and mother dyads (n = 185) who applied to psychotherapy services at an outpatient university clinic. Mothers filled out the Child Behavior Checklist, which includes a depression subscale, and children filled out the Children's Depression Inventory. We computed association networks for thirteen depressive symptoms separately for children and mothers using the graphical LASSO. Sadness had the highest strength centrality in the networks of both children and mothers, but the pattern of connectivity and centrality of other symptoms differed. We discussed our findings within the framework of network theory.

Skewness and Staging: Does the Floor Effect Induce Bias in Multilevel AR(1) Models?

Multilevel autoregressive models are popular choices for the analysis of intensive longitudinal data in psychology. Empirical studies have found a positive correlation between autoregressive parameters of affective time series and the between-person measures of psychopathology, a phenomenon known as the staging effect. However, it has been argued that such findings may represent a statistical artifact: Although common models assume normal error distributions, empirical data (for instance, measurements of negative affect among healthy individuals) often exhibit the floor effect, that is response distributions with high skewness, low mean, and low variability. In this paper, we investigated whether-and to what extent-the floor effect leads to erroneous conclusions by means of a simulation study. We describe three dynamic models which have meaningful substantive interpretations and can produce floor-effect data. We simulate multilevel data from these models, varying skewness independent of individuals' autoregressive parameters, while also varying the number of time points and cases. Analyzing these data with the standard multilevel AR(1) model we found that positive bias only occurs when modeling with random residual variance, whereas modeling with fixed residual variance leads to negative bias. We discuss the implications of our study for data collection and modeling choices.

The network structure of psychopathological and resilient responses to the pandemic: A multicountry general population study of depression and anxiety

Commonly identified patterns of psychological distress in response to adverse events are characterized by resilience (i.e., little to no distress), delayed (i.e., distress that increases over time), recovery (i.e., distress followed by a gradual decrease over time), and sustained (i.e., distress remaining stable over time). This study aimed to examine these response patterns during the COVID-19 pandemic. Anxiety and depressive symptom data collected across four European countries over the first year of the pandemic were analyzed (N = 3,594). Participants were first categorized into groups based on the four described patterns. Network connectivity and symptom clustering were then estimated for each group and compared. Two thirds (63.6%) of the sample displayed a resilience pattern. The sustained distress network (16.3%) showed higher connectivity than the recovery network (10.0%) group, p = .031; however, the resilient network showed higher connectivity than the delayed network (10.1%) group, p = .016. Regarding symptom clustering, more clusters emerged in the recovery network (i.e., three) than the sustained network (i.e., two). These results replicate findings that resilience was the most common mental health pattern over the first pandemic year. Moreover, they suggest that high network connectivity may be indicative of a stable mental health response over time, whereas fewer clusters may be indicative of a sustained distress pattern. Although exploratory, the network perspective provides a useful tool for examining the complexity of psychological responses to adverse events and, if replicated, could be useful in identifying indicators of protection against or vulnerability to future psychological distress.

Longitudinal Association Between Posttraumatic Stress Symptoms and Depression Symptoms Among Chinese Adolescents During COVID-19: Evidence from Network Perspective

Although there are an increasing number of studies that have explored the mental health consequences of COVID-19 focusing on revealing risk factors, the longitudinal research examining the potential mechanism of the co-occurrence of posttraumatic stress symptoms (PTSS) and depression symptoms among adolescents were scarce. The present study identified the important comorbidity symptoms and explored longitudinal relationship of PTSS and depression symptoms from the network perspective. A two-wave investigation (4 months interval; T1 and T2) was conducted with a sample of 1225 Chinese adolescents. Cross-sectional network and cross-lagged panel network (CLPN) analyses were adopted. Results showed that comorbidity symptoms consisted of both overlapping and non-overlapping symptoms, including "Future foreshortening" at T1 and T2 from PTSS, and "Hard to get started" at T1 and "Not sleep well" at T2 from depression symptoms. Strong longitudinal pathways appeared from all PTSS to depression symptoms, among which the pathway from "Difficulty concentrating" to "Hard to get started" was the strongest. These findings suggest that the possible comorbidity between PTSS and depression symptoms is caused by their independent and related structures, and their longitudinal association. Clinical intervention for these symptoms may alleviate adolescents' psychological problems in the aftermath of traumatic events.

APOE ε4 positivity predicts centrality of episodic memory nodes in patients with mild cognitive impairment: A cohort-based, graph theory-informed study of cognitive networks

As network neuroscience can capture the systemic impact of APOE variability at a neuroimaging level, this study investigated the network-based cognitive endophenotypes of ε4-carriers and non-carriers across the continuum between normal ageing and Alzheimer's dementia (AD). We hypothesised that the impact of APOE-ε4 on cognitive functioning can be reliably captured by the measurement of graph-theory centrality. Cognitive networks were calculated in 8118 controls, 3482 MCI patients and 4573 AD patients, recruited in the National Alzheimer's Coordinating Center (NACC) database. Nodal centrality was selected as the neurofunctional readout of interest. ε4-carrier-vs.-non-carrier differences were tested in two independent NACC sub-cohorts assessed with either Version 1 or Version 2 of the Uniform Data Set neuropsychological battery. A significant APOE-dependent effect emerged from the analysis of the Logical-Memory nodes in MCI patients in both sub-cohorts. While non-carriers showed equal centrality in immediate and delayed recall, the latter was significantly less central among carriers (v1: bootstrapped confidence interval 0.107-0.667, p < 0.001; v2: bootstrapped confidence interval 0.018-0.432, p < 0.001). This indicates that, in carriers, delayed recall was, overall, significantly more weakly correlated with the other cognitive scores. These findings were replicated in the sub-groups of sole amnestic-MCI patients (n = 2971), were independent of differences in network communities, clinical severity or other demographic factors. No effects were found in the other two diagnostic groups. APOE-ε4 influences nodal properties of cognitive networks when patients are clinically classified as MCI. This highlights the importance of characterising the impact of risk factors on the wider cognitive network via network-neuroscience methodologies.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

The relationship of posttraumatic stress disorder and developmental trauma disorder with childhood psychopathology: A network analysis

OBJECTIVE

Potentially traumatic experiences are a major risk factor for mental disorders in children and adolescents. Posttraumatic psychopathology includes trauma-specific disorders such as posttraumatic stress disorder (PTSD) as well as other psychiatric disorders. Developmental Trauma Disorder (DTD) has been proposed as a developmentally sensitive diagnosis. We aimed to further illuminate the co-occurrence of psychiatric conditions with DTD and PTSD.

METHOD

In a convenience sample of families of 507 children and adolescents (mean age = 12.11 years old, SD = 2.92; 48.5% female), we assessed DTD, PTSD, and screened for psychiatric disorders. We estimated network models including DTD, PTSD and ten psychiatric conditions.

RESULTS

We found that DTD and PTSD share both common and differential comorbidity features on disorder-, domain-, and symptom-level. The differential comorbidity patterns of the DTD and PTSD domains placed DTD close to both externalizing and internalizing psychopathology while PTSD was primarily linked to internalizing conditions.

CONCLUSIONS

Our study provides evidence for the complex clinical presentation of posttraumatic psychopathology over and above PTSD in children. DTD and PTSD provide useful and distinct diagnostic categories for children who are also experiencing internalizing conditions, and DTD may be especially relevant for children who are experiencing externalizing psychopathology.

Network analysis of inflammation and symptoms in recent onset schizophrenia and the influence of minocycline during a clinical trial

Attempts to delineate an immune subtype of schizophrenia have not yet led to the clear identification of potential treatment targets. An unbiased informatic approach at the level of individual immune cytokines and symptoms may reveal organisational structures underlying heterogeneity in schizophrenia, and potential for future therapies. The aim was to determine the network and relative influence of pro- and anti-inflammatory cytokines on depressive, positive, and negative symptoms. We further aimed to determine the effect of exposure to minocycline or placebo for 6 months on cytokine-symptom network connectivity and structure. Network analysis was applied to baseline and 6-month data from the large multi-center BeneMin trial of minocycline (N = 207) in schizophrenia. Pro-inflammatory cytokines IL-6, TNF-α, and IFN-γ had the greatest influence in the inflammatory network and were associated with depressive symptoms and suspiciousness at baseline. At 6 months, the placebo group network connectivity was 57% stronger than the minocycline group, due to significantly greater influence of TNF-α, early wakening, and pathological guilt. IL-6 and its downstream impact on TNF-α, and IFN-γ, could offer novel targets for treatment if offered at the relevant phenotypic profile including those with depression. Future targeted experimental studies of immune-based therapies are now needed.

Increased affective reactivity among depressed individuals can be explained by floor effects: An experience sampling study

Experience sampling studies into daily-life affective reactivity indicate that depressed individuals react more strongly to both positive and negative stimuli than non-depressed individuals, particularly on negative affect (NA). Given the different mean levels of both positive affect (PA) and NA between patients and controls, such findings may be influenced by floor/ceiling effects, leading to violations of the normality and homoscedasticity assumptions underlying the used statistical models. Affect distributions in prior studies suggest that this may have particularly influenced NA-reactivity findings. Here, we investigated the influence of floor/ceiling effects on the observed PA- and NA-reactivity to both positive and negative events. Data came from 346 depressed, non-depressed, and remitted participants from the Netherlands Study of Depression and Anxiety (NESDA). In PA-reactivity analyses, no floor/ceiling effects and assumption violations were observed, and PA-reactivity to positive events, but not negative events, was significantly increased in the depressed and remitted groups versus the non-depressed group. However, NA-scores exhibited a floor effect in the non-depressed group and naively estimated models violated model assumptions. When these violations were accounted for in subsequent analyses, group differences in NA-reactivity that had been present in the naive models were no longer observed. In conclusion, we found increased PA-reactivity to positive events but no evidence of increased NA-reactivity in depressed individuals when accounting for violations of assumptions. The results indicate that affective-reactivity results are very sensitive to modeling choices and that previously observed increased NA-reactivity in depressed individuals may (partially) reflect unaddressed assumption violations resulting from floor effects in NA.

Examining changes in binge-eating disorder network centrality and structure in patients treated with cognitive-behavioral therapy versus interpersonal psychotherapy

OBJECTIVE

A sizeable minority of patients with binge-eating disorder (BED) do not fully respond to evidence-based treatments. Evidence to guide refinements of treatments is needed. Conceptualizing BED as arising from a network of symptom-to-symptom interactions allows for identification of the most strongly connected symptoms, which could inform intervention targets. This study estimated networks of BED features at pretreatment and posttreatment to assess whether cognitive behavioral therapy (CBT) and interpersonal psychotherapy (IPT) differentially impacted the interrelationships of BED symptoms/features.

METHODS

Participants were 392 adults (83% women, 88% white) with BED who received CBT (n = 236) or IPT (n = 156) and assessed at pretreatment and posttreatment. Networks were estimated across timepoints and treatments. Expected influence (EI) was calculated; symptoms with the highest EI have the most strong and frequent associations with other symptoms. We also assessed whether the symptoms with the highest and lowest EI predicted posttreatment remission indicators.

RESULTS

In the CBT and IPT networks, shape concern, weight concern, and eating concern had the highest EI at pretreatment and posttreatment. EI significantly increased from pretreatment to posttreatment for some symptoms in CBT but did not change for any symptoms in IPT. Shape concern significantly and positively predicted BED remission indicators in CBT and IPT.

CONCLUSIONS

CBT and IPT similarly impacted interrelations among BED features. Pretreatment EI predicted posttreatment remission indicators, indicating that pretreatment centrality could signal meaningful intervention targets. Clinical implications and avenues for future research are discussed including how personalized network analysis may advance the understanding of the clinical utility of centrality.

PUBLIC SIGNIFICANCE

Cognitive behavioral therapy and interpersonal therapy for binge-eating disorder, which are two leading evidence-based treatments for binge-eating disorder that are quite different in their models and approaches, similarly impacted interrelations among binge-eating disorder symptoms. In addition, the most strongly interconnected symptom predicted indicators of remission. Studying the interrelations among symptoms may provide new insight on how treatments impact symptom relationships and inform intervention targets.

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

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

The network structure of posttraumatic stress symptoms in war-affected children and adolescents

Background

It is unclear whether findings from previous network analyses of posttraumatic stress disorder (PTSD) symptoms among children and adolescents are generalizable to youth living in war-torn settings and whether there are differences in the structure and connectivity of symptoms between children and adolescents. This study examined the network structure of PTSD symptoms in a sample of war-affected youth and compared the symptom networks of children and adolescents.

Methods

The overall sample comprised 2007 youth (6-18 years old) living in Burundi, Democratic Republic of Congo, Iraq, Palestine, Tanzania, and Uganda amid or close to war and armed conflict. Youth reported their PTSD symptoms using a self-report questionnaire in Palestine and structured clinical interviews in all other countries. We computed the networks of the overall sample and of two sub-samples of 412 children (6-12 years) and 473 adolescents (13-18 years) and compared the structure and global connectivity of symptoms among children and adolescents.

Results

In both the overall sample and the sub-samples, re-experiencing and avoidance symptoms were most strongly connected. The adolescents' network had a higher global connectivity of symptoms than the children's network. Hyperarousal symptoms and intrusions were more strongly connected among adolescents compared to children.

Conclusion

The findings lend support to a universal concept of PTSD among youth characterized by core deficits in fear processing and emotion regulation. However, different symptoms may be particularly important in different developmental stages, with avoidance and dissociative symptoms dominating in childhood and intrusions and hypervigilance gaining importance in adolescence. Stronger symptom connections may render adolescents more vulnerable to the persistence of symptoms.

Exploring the associations between psychodynamic constructs and psychopathology: A network approach

Psychodynamic therapy effectively reduces symptomatology by focusing on underlying (unconscious) processes instead of symptoms. Nevertheless, the exact interrelationship between psychodynamic constructs and psychopathology remains unclear. This study uses network analysis to explore these associations. We computed a cross-sectional partial correlation network between psychodynamic constructs (i.e., personality functioning, interpersonal relations, and active and passive modes of intrapsychic conflicts according to the Operationalized Psychodynamic Diagnostics [OPD] system) and psychopathology (i.e., depression and somatization) in a naturalistic sample of 341 adults registering for psychodynamic outpatient therapy. We estimated node centrality, node predictability, and bridge symptoms and used community detection analysis. Bootstrap methods were applied to assess network stability. Psychodynamic constructs and psychopathology resulted in separate but connected clusters. Personality functioning emerged as the most influential node in the network and was bridging the clusters. The network was found to be highly stable, allowing reliable interpretations. The results offer important insights on how psychodynamic constructs relate to psychopathology, which can be used to inform treatment approaches. The findings suggest that personality functioning may be an important intervention target. However, future research is needed to include a broader range of diagnoses. In addition, longitudinal studies may clarify the direction of causality.

Auditing the research practices and statistical analyses of the group-level temporal network approach to psychological constructs: A systematic scoping review

Network analyses have become increasingly common within the field of psychology, and temporal network analyses in particular are quickly gaining traction, with many of the initial articles earning substantial interest. However, substantial heterogeneity exists within the study designs and methodology, rendering it difficult to form a comprehensive view of its application in psychology research. Since the field is quickly growing and since there have been many study-to-study variations in terms of choices made by researchers when collecting, processing, and analyzing data, we saw the need to audit this field and formulate a comprehensive view of current temporal network analyses. To systematically chart researchers' practices when conducting temporal network analyses, we reviewed articles conducting temporal network analyses on psychological variables (published until March 2021) in the framework of a scoping review. We identified 43 articles and present the detailed results of how researchers are currently conducting temporal network analyses. A commonality across results concerns the wide variety of data collection and analytical practices, along with a lack of consistency between articles about what is reported. We use these results, along with relevant literature from the fields of ecological momentary assessment and network analysis, to formulate recommendations on what type of data is suited for temporal network analyses as well as optimal methods to preprocess and analyze data. As the field is new, we also discuss key future steps to help usher the field's progress forward and offer a reporting checklist to help researchers navigate conducting and reporting temporal network analyses.

The network structure of the Special Interests Survey

Despite the prevalence of special interests (SIs) in autistic youth, research on SIs and how they are characterized is limited. Indeed, a significant challenge in identifying and classifying SIs lies in capturing the vast and diverse scope of potential interests in this population. The recently developed Special Interest Survey (SIS) is a caregiver-report measure to improve SI characterization by capturing a broad range of past and current SIs. In the present study, we performed a network analysis of the SIS to examine relations between SIs and identify distinct interest clusters. We analyzed data from 1992 caregivers of autistic youths who completed the SIS. The network of SIs was densely interconnected, characterized by six communities of interests: Fact-seeking, Engineering, Order-seeking, Object Attachment, Entertainment, and Scholarly Pursuits. Findings suggest that the structure of the observed network is likely to generalize to similar samples. Of all the SIs and their respective communities, behaviors related to Fact-seeking were identified as the most central, meaning that endorsement of these interests was most strongly related to co-endorsement of other SIs. These findings lay the groundwork for future work on SIs, such as improved assessment techniques and linkage of SIs to a broad range of demographic variables, youth characteristics, and autism symptoms.

A network approach to climate change anxiety and its key related features

Research has pointed to startling worldwide rates of people reporting considerable anxiety vis-à-vis climate change. Yet, uncertainties remain regarding how climate anxiety's cognitive-emotional features and daily life functional impairments interact with one another and with climate change experience, pro-environmental behaviors, and general worry. In this study, we apply network analyses to examine the associations among these variables in an international community sample (n = 874). We computed two network models, a graphical Gaussian model to explore network structure, potential communities, and influential nodes, and a directed acyclic graph to examine the probabilistic dependencies among the variables. Both network models pointed to the cognitive-emotional features of climate anxiety as a potential hub bridging general worry, the experience of climate change, pro-environmental behaviors, and the functional impairments associated with climate anxiety. Our findings offer data-driven clues for the field's larger quest to establish the foundations of climate anxiety.

Longitudinal symptom network structure in first-episode psychosis: a possible marker for remission

BACKGROUND

Network approach has been applied to a wide variety of psychiatric disorders. The aim of the present study was to identify network structures of remitters and non-remitters in patients with first-episode psychosis (FEP) at baseline and the 6-month follow-up.

METHODS

Participants (n = 252) from the Korean Early Psychosis Study (KEPS) were enrolled. They were classified as remitters or non-remitters using Andreasen's criteria. We estimated network structure with 10 symptoms (three symptoms from the Positive and Negative Syndrome Scale, one depressive symptom, and six symptoms related to schema and rumination) as nodes using a Gaussian graphical model. Global and local network metrics were compared within and between the networks over time.

RESULTS

Global network metrics did not differ between the remitters and non-remitters at baseline or 6 months. However, the network structure and nodal strengths associated with positive-self and positive-others scores changed significantly in the remitters over time. Unique central symptoms for remitters and non-remitters were cognitive brooding and negative-self, respectively. The correlation stability coefficients for nodal strength were within the acceptable range.

CONCLUSION

Our findings indicate that network structure and some nodal strengths were more flexible in remitters. Negative-self could be an important target for therapeutic intervention.

Character strengths and mental health as complex systems: a network analysis to identify bridge strengths

The network approach poses an alternative focus to understand psychological constructs as emerging from mutual interactions among indicators. Network psychometrics has been applied to psychopathology to unravel the connections between symptoms, but it can also be applied to the study of well-being. The role of character strengths in mental health is at the forefront of research attention. Previous findings suggest that heart character strengths are more predictive of mental health than mind character strengths. Nevertheless, researchers have rarely applied the network approach in this context. The present study examines, from the network approach, the connections between heart and mind character strengths and mental health. Building upon the dual-factor model of mental health, positive (i.e., happiness and life satisfaction) and negative indicators (i.e., depression) were included in the assessment of this construct. A sample of 597 Spanish undergraduates (M = 23.52; SD = 5.25; 75.6% females) completed cross-sectional self-report measures. Network analysis was used to estimate a network composed of two communities: character strengths and mental health. We used centrality analysis to calculate the importance of each node and bridge centrality to examine the interactions between the communities. The results indicated that the heart strengths of love, zest, hope, and gratitude reported the highest bridge strength centrality, suggesting that they played an intermediary role activating and deactivating components of mental health. Adopting the network approach to explore the connections between character strengths and mental health can help design focused intervention strategies in psychology.

Changes in specific and nonspecific psychopathology network structure after intensive cognitive behavior therapy in patients with anorexia nervosa

OBJECTIVE

This study aimed to compare eating disorder-specific and nonspecific clinical features in patients with anorexia nervosa before and after intensive enhanced cognitive behavior therapy (CBT-E) via network analysis.

METHODS

All consecutive patients admitted to intensive CBT-E were eligible, and the sample comprised patients aged ≥16 years who completed a 20-week intensive CBT-E program. Body mass index (BMI), Eating Disorder Examination Questionnaire and Brief Symptoms Inventory responses were gathered at baseline and end of treatment, and used to generate statistical networks of the connections between symptoms (nodes) and the strength and centrality thereof.

RESULTS

A total of 214 patients were included. Most nodes had relatively similar centrality compared to other nodes in the networks. "Eating concern" and "phobic anxiety" showed the greatest bridge centrality at both time points. No differences were found between baseline and the end of treatment in either global network or individual connection strengths.

CONCLUSION

These findings suggest that some clinical expressions not specific to eating-disorder psychopathology remain strongly connected in the generalized network of patients with anorexia nervosa after CBT-E. Future research should examine whether additional procedures specifically designed to target these symptoms should be integrated into this and other treatments.

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

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

The Mereology of Depression-Networks of Depressive Symptoms during the Course of Psychotherapy

(1) Background: Research has shown that it is important to examine depressive symptoms in the light of the mereology (the ratio between one symptom and the whole disorder). The goal of this study was to examine changes in the symptom interrelations of patients undergoing cognitive behavioral therapy treatment (CBT) via network analyses. (2) Method: Outpatients with depressive symptoms (N = 401) were assessed with the Beck Depression Inventory three times (pretreatment, after 12 sessions, and post-treatment) during CBT. Gaussian graphical models were used to estimate the relationships among symptoms. (3) Results: The severity of depressive symptoms significantly decreased over the course of therapy, but connectivity in the networks significantly increased. Communities of symptoms changed during treatment. The most central and predictable symptom was worthlessness at baseline and after 12 sessions, and loss of energy and self-dislike at post-treatment. (4) Conclusion: The results indicate that the severity of depressive symptoms decreased during cognitive behavior therapy, while network connectivity increased. Furthermore, the associations among symptoms and their centrality changed during the course of therapy. Future studies may investigate individual differences and their impact on the planning of psychotherapeutic treatment.

Change in eating-disorder psychopathology network structure in patients with binge-eating disorder: Findings from treatment trial with 12-month follow-up

OBJECTIVE

Effective treatments exist for binge-eating disorder (BED), although roughly 50% of patients fail to attain binge-eating abstinence. Evidence on how to refine treatments is lacking. Conceptualizing BED as arising from a network of symptom-to-symptom interactions allows for the identification of the most strongly connected symptoms, which could inform intervention targets. This study assessed how BED symptom centrality changed with behaviorally based weight-loss treatments (BBWLTs).

METHODS

Participants were 191 adult patients (71% female, 79% White) with BED with comorbid obesity participating in a randomized controlled trial testing 6-month BBWLTs for BED. Independent assessments of BED symptoms were performed at pretreatment, posttreatment, and 12 months after treatment. Strength centrality indicated how strongly and frequently symptoms were associated with each other in the network. Significant changes in centrality between timepoints were determined using permutation tests.

RESULTS

At pretreatment, overvaluation of shape/weight and preoccupation with shape/weight and food/eating had the highest strength centrality. At posttreatment and 12-month follow-up, dissatisfaction with shape/weight had the highest centrality, which significantly increased from pretreatment.

CONCLUSIONS

The relations among symptoms of BED are not static and change over time with treatment. BBWLTs do not appear to reduce connectivity of overvaluation of shape/weight (the most central BED symptom prior to treatment), but instead increase connectivity of dissatisfaction with shape/weight with other symptoms following treatment. The observed network structure of symptoms following BBWLTs resembles network analyses of people without eating disorders. Findings highlight the importance of understanding how treatments impact symptom relationships, not just symptom intensities. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Utilizing network analysis to understand the structure of depression in Chinese adolescents: Replication with three depression scales

Depression involves a heterogenous collection of symptoms. Network perspective views depressive symptoms as an interrelated network. The current study aimed to replicate network analyses on adolescent depression in three samples assessed with three instruments to examine the consistency of network structures and also examine the variance of networks between genders. Three samples of adolescents (total N = 4375, mean age = 15, 49.1% boys) were assessed with PHQ-9, SMFQ and CDI, respectively. Network analyses were carried out on depression symptoms. Network stability, node centrality and network comparisons between genders were examined. Three networks were reliably stable. Sadness and self-hatred were unanimously identified to be central symptoms of adolescent depression in three networks. In addition, fatigue, no good, everything wrong and loneliness also appeared to be central in specific networks. Among three depression networks, PHQ-9 network demonstrated gender difference in network structure. The current study is exploratory in nature. The differences in three networks can be due to various samples or different node inclusions. Further, the study is cross-sectional precluding causal interpretation and the samples are nonclinical. Besides "hallmark" symptom sadness, self-hatred was also identified unanimously in three networks, which demonstrated the significant role self-worth played in adolescent depression. The results also suggested that differences in node inclusion may have influence on the network structure.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12144-022-03201-z.

Network analysis of PGD, PTSD and insomnia symptoms in Chinese shidu parents with PGD

Background

Chinese shidu parents (bereaved parents over the age of 49 who have lost their only child) are potentially at a high risk of prolonged grief disorder (PGD), posttraumatic stress disorder (PTSD) and insomnia.

Objective

The current study aimed to estimate three network models in 310 shidu parents who met the ICD-11 criteria for PGD: (1) a PGD network to identify central symptoms; (2) a comorbidity network to explore bridge symptoms between PGD and PTSD; (3) a comorbidity network to examine the associations between PGD and insomnia symptoms.

Methods

The R-packages bootnet, qgraph and networktools were used to investigate the structure of network models and centrality indices of symptoms. In addition, robustness and significance analyses for the edge weights and the order of centrality were performed.

Results

Emotional pain and numbness emerged as the most central symptoms in the PGD network. In the PGD-PTSD comorbidity network, the highest bridge strength symptoms were inability to trust others (PGD) and feeling upset (PTSD). Inability to trust others (PGD), avoidance (PGD), and impairment of life quality (insomnia) were possible bridge symptoms connecting PGD and insomnia.

Conclusions

Reducing emotional pain and numbness may be a viable target in PGD interventions for shidu parents. Additionally, findings suggest that future studies could examine the role of inability to trust others and avoidance in PGD comorbidities.

HIGHLIGHTS

• Emotional pain and numbness were the most influential symptoms in shidu parents with PGD. The role of PGD symptoms of inability to trust others and avoidance in the comorbidities of PGD with PTSD and insomnia might be worthy of further study.

Examining the associations between PTSD symptoms and aspects of emotion dysregulation through network analysis

Despite the clearly established link between posttraumatic stress disorder (PTSD) and emotion dysregulation, little is known about how individual symptoms of PTSD and aspects of emotion dysregulation interrelate. The network approach to mental health disorders provides a novel framework for conceptualizing the association between PTSD and emotion dysregulation as a system of interacting nodes. In this study, we estimated the structural relations among PTSD symptoms and aspects of emotion dysregulation within a large sample of women who participated in a multi-site study of sexual revictimization (N = 463). We estimated expected influence to reveal differential associations among PTSD symptoms and aspects of emotion dysregulation. Further, we estimated bridge expected influence to identify influential nodes connecting PTSD symptoms and aspects of emotion dysregulation. Results highlighted the key role of concentration difficulties in expected influence and bridge expected influence. Findings highlight several PTSD symptoms and aspects of emotion dysregulation that may be targets for future intervention.

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.

The symptom network structure of posttraumatic stress disorder among Chinese firefighters

BACKGROUND

The purpose of this study was to explore the core and driving symptoms of posttraumatic stress disorder (PTSD) of firefighters before and after rescue. Identifying core symptoms of PTSD can help clinicians to understand what may be relevant targets for treatment.

METHODS

The study recruited 334 firefighters in a firefighter military school in September 2017 (T1). They were followed up 3 months later (T2), during which time they participated in real rescue activities. The network structure of DSM-5 PTSD was using regularized partial correlation models and a Bayesian approach computing directed acyclic graphs.

RESULTS

The most central symptom both in T1 and T2 was negative emotional state. Irritable or anger emerged as a key driver of other symptoms in traumatized firefighters.

CONCLUSIONS

Negative emotional state and irritable or anger might represent important symptoms within PTSD symptomatology and may offer key targets in PTSD treatment for firefighters.

Examining the impact of the COVID-19 pandemic through the lens of the network approach to psychopathology: Analysis of the Brazilian Longitudinal Study of Health (ELSA-Brasil) cohort over a 12-year timespan

Cohort studies have displayed mixed findings on changes in mental symptoms severity in 2020, when the COVID-19 pandemic outbreak started. Network approaches can provide additional insights by analyzing the connectivity of such symptoms. We assessed the network structure of mental symptoms in the Brazilian Longitudinal Study of Health (ELSA-Brasil) in 3 waves: 2008-2010, 2017-2019, and 2020, and hypothesized that the 2020 network would present connectivity changes. We used the Clinical Interview Scheduled-Revised (CIS-R) questionnaire to evaluates the severity of 14 common mental symptoms. Networks were graphed using unregularized Gaussian models and compared using centrality and connectivity measures. The predictive power of centrality measures and individual symptoms were also estimated. Among 2011 participants (mean age: 62.1 years, 58% females), the pandemic symptom 2020 network displayed higher overall connectivity, especially among symptoms that were related to general worries, with increased local connectivity between general worries and worries about health, as well as between anxiety and phobia symptoms. There was no difference between 2008 and 2010 and 2017-2019 networks. According to the network theory of mental disorders, external factors could explain why the network structure became more densely connected in 2020 compared to previous observations. We speculate that the COVID-19 pandemic and its innumerous social, economical, and political consequences were prominent external factors driving such changes; although further assessments are warranted.

The Network Structure of ICD-11 Disorders Specifically Associated with Stress: Adjustment Disorder, Prolonged Grief Disorder, Posttraumatic Stress Disorder, and Complex Posttraumatic Stress Disorder

INTRODUCTION

The ICD-11 includes a new grouping for "disorders specifically associated with stress" that contains revised descriptions of posttraumatic stress disorder (PTSD) and adjustment disorder (AjD) and new diagnoses in the form of complex PTSD (CPTSD) and prolonged grief disorder (PGD). These disorders are similar in that they each require a life event for the diagnosis; however, they have not yet been assessed together for validity within the same sample. We set out to test the distinctiveness of the four main ICD-11 stress disorders using a network analysis approach.

METHODS

A population-based, cross-sectional design. A nationally representative sample of adults from the Republic of Ireland aged 18 years and older (N = 1,020) completed standardized measures of PTSD, CPTSD, AjD, and PGD. A network analysis was conducted at the symptom level. Outcome measures included the International Trauma Questionnaire, the Inventory of Complicated Grief, and the International Adjustment Disorder Questionnaire.

RESULTS

Consistent with the taxonomic structure of the ICD-11, our results showed that although the four conditions clustered independently at the disorder level, the specific symptoms of PTSD, CPTSD, PGD, and AjD clustered together very strongly but more strongly than with symptoms of the other disorders. The majority (61%) of the variation in each symptom could be explained by its neighboring symptoms. The strongest transdiagnostically connecting symptom was "startle response."

DISCUSSION/CONCLUSION

Mental health professionals caring for people who have experienced a range of stressors and traumatic life events can be confident in diagnosing these conditions that have clear diagnostic boundaries. Interventions addressing stress-associated disorders should be based on diagnostic assessment to ensure close fit between symptoms and treatment.

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

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

Estimating the symptom structure of bipolar disorder via network analysis: Energy dysregulation as a central symptom

Using network analysis, we estimated the structure of relations among manic and depressive symptoms, respectively, in 486 patients (59% women; age: M = 37, SD = 12.1) with bipolar disorder prior to their entering a clinical trial. We computed three types of networks: (a) Gaussian graphical models (GGMs) depicting regularized partial correlations, (b) regression-based GGMs depicting nonregularized partial correlations, and (c) directed acyclic graphs (DAGs) via a Bayesian hill-climbing algorithm. Low energy and elevated energy were consistently identified as central nodes in the GGMs and as key parent nodes in the DAGs. Across analyses, pessimism about the future and depressed mood were the symptoms most strongly associated with suicidal thoughts and behavior. These exploratory analyses provide rich information about how bipolar disorder symptoms relate to one another, thereby furnishing a foundation for investigating how bipolar disorder symptoms may operate as a causal system. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Comparison of depression and anxiety symptom networks in reporters and non-reporters of lifetime trauma in two samples of differing severity

BACKGROUND

Reported trauma is associated with differences in the course and outcomes of depression and anxiety. However, no research has explored the association between reported trauma and patterns of clinically relevant symptoms of both depression and anxiety.

METHODS

We used network analysis to investigate associations between reported trauma and depression and anxiety symptom interactions in affected individuals from the Genetic Links to Anxiety and Depression (GLAD) Study (n = 17720), and population volunteers from the UK Biobank (n = 11120). Participants with current moderate symptoms of depression or anxiety were grouped into reporters and non-reporters of lifetime trauma. Networks of 16 depression and anxiety symptoms in the two groups were compared using the network comparison test.

RESULTS

In the GLAD Study, networks of reporters and non-reporters of lifetime trauma did not differ on any metric. In the UK Biobank, the symptom network of reporters had significantly greater density (7.80) than the network of non-reporters (7.05).

LIMITATIONS

The data collected in the GLAD Study and the UK Biobank are self-reported with validated or semi-validated questionnaires.

CONCLUSIONS

Reported lifetime trauma was associated with stronger interactions between symptoms of depression and anxiety in population volunteers. Differences between reporters and non-reporters may not be observed in individuals with severe depression and/or anxiety due to limited variance in the presentation of disorder.

Symptom networks in major depression do not diverge across sex, familial risk, and environmental risk

BACKGROUND

Major depression (MD) is a heterogeneous disorder in terms of its symptoms. Symptoms vary by presence of risk factors such as female sex, familial risk, and environmental adversity. However, it is unclear if these factors also influence interactions between symptoms. This study investigates if symptom networks diverge across sex, familial risk, and adversity.

METHODS

We included 9713 subjects from the general population who reported a lifetime episode of MD based on DSM-IV criteria. The survey assessed a wide set of symptoms, both from within the DSM criteria as well as other symptoms commonly experienced in MD. We compared symptom endorsement rates across sex, age at onset, family history and environmental adversity. We used the Network Comparison Test to test for symptom network differences across risk factors.

RESULTS

We found differences in symptom endorsement between groups. For instance, participants with an early onset of MD reported suicidal ideation nearly twice as often compared to participants with a later onset. We did not find any robust differences in symptom networks, which suggests that symptom networks do not diverge across sex, familial risk, and adversity.

LIMITATIONS

We estimated symptom networks of individuals during their worst lifetime episode of MD. Network differences might exist in a prodromal stage, while disappearing in full-blown MD (equifinality). Furthermore, as we used retrospective reports, results could be prone to recall bias.

CONCLUSIONS

Despite MD's heterogeneous symptomatology, interactions between symptoms are stable across risk factors and sex.

The network structure of core depressive symptom-domains in major depressive disorder following antidepressant treatment: a randomized clinical trial

BACKGROUND

Network analysis (NA) conceptualizes psychiatric disorders as complex dynamic systems of mutually interacting symptoms. Major depressive disorder (MDD) is a heterogeneous clinical condition, and very few studies to date have assessed putative changes in its psychopathological network structure in response to antidepressant (AD) treatment.

METHODS

In this randomized trial with adult depressed outpatients (n = 151), we estimated Gaussian graphical models among nine core MDD symptom-domains before and after 8 weeks of treatment with either escitalopram or desvenlafaxine. Networks were examined with the measures of cross-sectional and longitudinal structure and connectivity, centrality and predictability as well as stability and accuracy.

RESULTS

At baseline, the most connected MDD symptom-domains were fatigue-cognitive disturbance, whereas at week 8 they were depressed mood-suicidality. Overall, the most central MDD symptom-domains at baseline and week 8 were, respectively, fatigue and depressed mood; in contrast, the most peripheral symptom-domain across both timepoints was appetite/weight disturbance. Furthermore, the psychopathological network at week 8 was significantly more interconnected than at baseline, and they were also structurally dissimilar.

CONCLUSION

Our findings highlight the utility of focusing on the dynamic interaction between depressive symptoms to better understand how the treatment with ADs unfolds over time. In addition, depressed mood, fatigue, and cognitive/psychomotor disturbance seem to be central MDD symptoms that may be viable targets for novel, focused therapeutic interventions.

Investigating the structure of trait rumination in autistic adults: A network analysis

LAY ABSTRACT

Autistic adults are substantially more likely to develop depression than individuals in the general population, and recent research has indicated that certain differences in thinking styles associated with autism may play a role in this association. Rumination, the act of thinking about the same thing over and over without a functional outcome, is a significant risk factor for depression in both autistic and non-autistic adults. However, little is known about how different kinds of rumination relate to each other and to depressive symptoms in the autistic population specifically. To fill this gap in knowledge, we recruited a large online sample of autistic adults, who completed questionnaire measures of both the tendency to ruminate and symptoms of depression. By examining the interacting network of rumination and depression symptoms, this study was able to identify particular aspects of rumination-such as thinking repetitively about one's guilty feelings or criticizing oneself-that may be particularly important in maintaining these harmful thought patterns in autistic adults. Although further study is needed, it is possible that the symptoms identified as most "influential" in the network may be particularly good targets for future interventions for mood and anxiety disorders in the autistic population.

Does depression moderate the relationship between pain and suicidality in adolescence? A moderated network analysis

BACKGROUND

Whilst growing research suggests that pain is associated with suicidality in adolescence, it remains unclear whether this relationship is moderated by co-morbid depressive symptoms. The present study aimed to investigate whether the pain-suicidality association is moderated by depressive symptoms.

METHODS

We performed secondary analyses on cross-sectional, pre-intervention data from the 'My Resilience in Adolescence' [MYRIAD] trial (ISRCTN ref: 86619085; N=8072, 11-15 years). Using odds ratio tests and (moderated) network analyses, we investigated the relationship between pain and suicidality, after controlling for depression, anxiety, inhibitory control deficits and peer problems. We investigated whether depression moderates this relationship and explored gender differences.

RESULTS

Overall, 20% of adolescents reported suicidality and 22% reported pain, whilst nine percent of adolescents reported both. The experience of pain was associated with a four-fold increased risk of suicidality and vice versa (OR=4.00, 95%-CI=[3.54;4.51]), with no gender differences. This cross-sectional association remained significant after accounting for depression, anxiety, inhibitory control deficits and peer problems (aOR=1.39). Depression did not moderate the pain-suicidality association.

LIMITATIONS

The item-based, cross-sectional assessment of pain and suicidality precludes any conclusions about the direction of the effects and which aspects of suicidality and pain may drive this association.

CONCLUSIONS

Our findings underscore the need to consider pain as an independent risk correlate of suicidality in adolescents. Longitudinal research should examine how this relationship develops during adolescence. Clinically, our findings emphasise the need to assess and address suicidality in adolescents with pain, even in the absence of depressive symptoms.

A Network of Psychopathological, Cognitive, and Motor Symptoms in Schizophrenia Spectrum Disorders

Schizophrenia spectrum disorders (SSDs) are complex syndromes involving psychopathological, cognitive, and also motor symptoms as core features. A better understanding of how these symptoms mutually impact each other could translate into diagnostic, prognostic, and, eventually, treatment advancements. The present study aimed to: (1) estimate a network model of psychopathological, cognitive, and motor symptoms in SSD; (2) detect communities and explore the connectivity and relative importance of variables within the network; and (3) explore differences in subsample networks according to remission status. A sample of 1007 patients from a multisite cohort study was included in the analysis. We estimated a network of 43 nodes, including all the items from the Positive and Negative Syndrome Scale, a cognitive assessment battery and clinical ratings of extrapyramidal symptoms. Methodologies specific to network analysis were employed to address the study's aims. The estimated network for the total sample was densely interconnected and organized into 7 communities. Nodes related to insight, abstraction capacity, attention, and suspiciousness were the main bridges between network communities. The estimated network for the subgroup of patients in remission showed a sparser density and a different structure compared to the network of nonremitted patients. In conclusion, the present study conveys a detailed characterization of the interrelations between a set of core clinical elements of SSD. These results provide potential novel clues for clinical assessment and intervention.

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).

Symptom centrality and infrequency of endorsement identify adolescent depression symptoms more strongly associated with life satisfaction

Although depression symptoms are often treated as interchangeable, some symptoms may relate to adolescent life satisfaction more strongly than others. To assess this premise, we first conducted a network analysis on the Mood and Feelings Questionnaire (MFQ) in a large (N = 1,059), cross-sectional sample of community adolescents (age M = 14.72 ± 1.79). The most central symptoms of adolescent depression, as indexed by strength, were self-hatred, loneliness, sadness, and worthlessness while the least frequently endorsed symptoms were self-hatred, anhedonia, feeling like a bad person, and feeling unloved. Moreover, the more central a depression symptom was in the network (i.e., higher strength), the more variance it shared with life satisfaction (r = 0.59, 95% CI: 0.27, 0.76). How frequently a symptom was endorsed was negatively associated with the variance symptoms shared with life satisfaction (r = -0.48, 95% CI: -0.63, -0.21). Cross-validated, prediction focused models found central symptoms were expected to predict more out of fold variance in life satisfaction than peripheral symptoms and frequently endorsed symptoms, but not the least frequently endorsed symptoms. These findings show certain depression symptoms may be more strongly associated with life satisfaction in adolescence and these symptoms can be identified by multiple symptom-level metrics. Limitations include use of cross-sectional data and utilizing a community sample. Better understanding which symptoms of depression share more variance with important outcomes like life satisfaction could help us develop a more fine-grained understanding of adolescent depression.