Examining contemporaneous and temporal associations of real-time suicidal ideation using network analysis.

BACKGROUND: Suicidal ideation arises from a complex interplay of multiple interacting risk factors over time. Recently, ecological momentary assessment (EMA) has increased our understanding of factors associated with real-time suicidal ideation, as well as those predicting ideation at the level of hours and days. Here we used statistical network methods to investigate which cognitive-affective risk and protective factors are associated with the temporal dynamics of suicidal ideation. METHODS: The SAFE study is a longitudinal cohort study of 82 participants with current suicidal ideation who completed 4×/day EMA over 21 days. We modeled contemporaneous (t) and temporal (t + 1) associations of three suicidal ideation components (passive ideation, active ideation, and acquired capability) and their predictors (positive and negative affect, anxiety, hopelessness, loneliness, burdensomeness, and optimism) using multilevel vector auto-regression models. RESULTS: Contemporaneously, passive suicidal ideation was positively associated with sadness, hopelessness, loneliness, and burdensomeness, and negatively with happiness, calmness, and optimism; active suicidal ideation was positively associated with passive suicidal ideation, sadness, and shame; and acquired capability only with passive and active suicidal ideation. Acquired capability and hopelessness positively predicted passive ideation at t + 1, which in turn predicted active ideation; acquired capability was positively predicted at t + 1 by shame, and negatively by burdensomeness. CONCLUSIONS: Our findings show that systematic real-time associations exist between suicidal ideation and its predictors, and that different factors may uniquely influence distinct components of ideation. These factors may represent important targets for safety planning and risk detection.

A narrative systematic review of changes in mental health symptoms from before to during the COVID-19 pandemic.

The onset of the COVID-19 pandemic raised concerns regarding population-wide impacts on mental health. Existing work on the psychological impacts of disaster has identified the potential for multiple response trajectories, with resilience as likely as the development of chronic psychopathology. Early reviews of mental health during the pandemic suggested elevated prevalence rates of multiple forms of psychopathology, but were limited by largely cross-sectional approaches. We conducted a systematic review of studies that prospectively assessed pre- to peri-pandemic changes in symptoms of psychopathology to investigate potential mental health changes associated with the onset of the pandemic (PROSPERO #CRD42021255042). A total of 97 studies were included, covering symptom clusters including obsessive-compulsive disorder (OCD), post-traumatic stress disorder (PTSD), fear, anxiety, depression, and general distress. Changes in psychopathology symptoms varied by symptom dimension and sample characteristics. OCD, anxiety, depression, and general distress symptoms tended to increase from pre- to peri-pandemic. An increase in fear was limited to medically vulnerable participants, and findings for PTSD were mixed. Pre-existing mental health diagnoses unexpectedly were not associated with symptom exacerbation, except in the case of OCD. Young people generally showed the most marked symptom increases, although this pattern was reversed in some samples. Women in middle adulthood in particular demonstrated a considerable increase in anxiety and depression. We conclude that mental health responding during the pandemic varied as a function of both symptom cluster and sample characteristics. Variability in responding should therefore be a key consideration guiding future research and intervention.

Elemental psychopathology: distilling constituent symptoms and patterns of repetition in the diagnostic criteria of the DSM-5.

BACKGROUND: The DSM-5 features hundreds of diagnoses comprising a multitude of symptoms, and there is considerable repetition in the symptoms among diagnoses. This repetition undermines what we can learn from studying individual diagnostic constructs because it can obscure both disorder- and symptom-specific signals. However, these lost opportunities are currently veiled because symptom repetition in the DSM-5 has not been quantified. METHOD: This descriptive study mapped the repetition among the 1419 symptoms described in 202 diagnoses of adult psychopathology in section II of the DSM-5. Over a million possible symptom comparisons needed to be conducted, for which we used both qualitative content coding and natural language processing. RESULTS: In total, we identified 628 distinct symptoms: 397 symptoms (63.2%) were unique to a single diagnosis, whereas 231 symptoms (36.8%) repeated across multiple diagnoses a total of 1022 times (median 3 times per symptom; range 2-22). Some chapters had more repetition than others: For example, every symptom of every diagnosis in the bipolar and related disorders chapter was repeated in other chapters, but there was no repetition for any symptoms of any diagnoses in the elimination disorders, gender dysphoria or paraphilic disorders. The most frequently repeated symptoms included insomnia, difficulty concentrating, and irritability - listed in 22, 17 and 16 diagnoses, respectively. Notably, the top 15 most frequently repeating diagnostic criteria were dominated by symptoms of major depressive disorder. CONCLUSION: Overall, our findings lay the foundation for a better understanding of the extent and potential consequences of symptom overlap.

A Network Study of Family Affect Systems in Daily Life.

Adolescence is a time period characterized by extremes in affect and increasing prevalence of mental health problems. Prior studies have illustrated how affect states of adolescents are related to interactions with parents. However, it remains unclear how affect states among family triads, that is adolescents and their parents, are related in daily life. This study investigated affect state dynamics (happy, sad, relaxed, and irritated) of 60 family triads, including 60 adolescents (Mage = 15.92, 63.3% females), fathers and mothers (Mage = 49.16). The families participated in the RE-PAIR study, where they reported their affect states in four ecological momentary assessments per day for 14 days. First, we used multilevel vector-autoregressive network models to estimate affect dynamics across all families, and for each family individually. Resulting models elucidated how family affect states were related at the same moment, and over time. We identified relations from parents to adolescents and vice versa, while considering family variation in these relations. Second, we evaluated the statistical performance of the network model via a simulation study, varying the percentage missing data, the number of families, and the number of time points. We conclude with substantive and statistical recommendations for future research on family affect dynamics.

Introducing FRED: Software for Generating Feedback Reports for Ecological Momentary Assessment Data.

Ecological Momentary Assessment (EMA) is a data collection approach utilizing smartphone applications or wearable devices to gather insights into daily life. EMA has advantages over traditional surveys, such as increasing ecological validity. However, especially prolonged data collection can burden participants by disrupting their everyday activities. Consequently, EMA studies can have comparably high rates of missing data and face problems of compliance. Giving participants access to their data via accessible feedback reports, as seen in citizen science initiatives, may increase participant motivation. Existing frameworks to generate such reports focus on single individuals in clinical settings and do not scale well to large datasets. Here, we introduce FRED (Feedback Reports on EMA Data) to tackle the challenge of providing personalized reports to many participants. FRED is an interactive online tool in which participants can explore their own personalized data reports. We showcase FRED using data from the WARN-D study, where 867 participants were queried for 85 consecutive days with four daily and one weekly survey, resulting in up to 352 observations per participant. FRED includes descriptive statistics, time-series visualizations, and network analyses on selected EMA variables. Participants can access the reports online as part of a Shiny app, developed via the R programming language. We make the code and infrastructure of FRED available in the hope that it will be useful for both research and clinical settings, given that it can be flexibly adapted to the needs of other projects with the goal of generating personalized data reports.

Computational Psychiatry Needs Time and Context.

Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting that modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry.

Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies.

With progress in genome-wide association studies of depression, from identifying zero hits in ~16 000 individuals in 2013 to 223 hits in more than a million individuals in 2020, understanding the genetic architecture of this debilitating condition no longer appears to be an impossible task. The pressing question now is whether recently discovered variants describe the etiology of a single disease entity. There are a myriad of ways to measure and operationalize depression severity, and major depressive disorder as defined in the Diagnostic and Statistical Manual of Mental Disorders-5 can manifest in more than 10 000 ways based on symptom profiles alone. Variations in developmental timing, comorbidity and environmental contexts across individuals and samples further add to the heterogeneity. With big data increasingly enabling genomic discovery in psychiatry, it is more timely than ever to explicitly disentangle genetic contributions to what is likely 'depressions' rather than depression. Here, we introduce three sources of heterogeneity: operationalization, manifestation and etiology. We review recent efforts to identify depression subtypes using clinical and data-driven approaches, examine differences in genetic architecture of depression across contexts, and argue that heterogeneity in operationalizations of depression is likely a considerable source of inconsistency. Finally, we offer recommendations and considerations for the field going forward.

A topography of 21 phobic fears: network analysis in an epidemiological sample of adult twins.

BACKGROUND: Few factor analyses and no network analyses have examined the structure of DSM phobic fears or tested the specificity of the relationship between panic disorder and agoraphobic fears. METHODS: Histories of 21 lifetime phobic fears, coded as four-level ordinal variables (no fear to fear with major interference) were assessed at personal interview in 7514 adults from the Virginia Twin Registry. We estimated Gaussian Graphical Models on individual phobic fears; compared network structures of women and men using the Network Comparison Test; used community detection to determine the number and nature of groups in which phobic fears hang together; and validated the anticipated specific relationship between panic disorder and agoraphobia. RESULTS: All networks were densely and positively inter-connected; networks of women and men were structurally similar. Our most frequent and stable solution identified four phobic clusters: (i) blood-injection, (ii) social-agoraphobia, (iii) situational, and (iv) animal-disease. Fear of public restrooms and of diseases clustered with animal and not, respectively, social and blood-injury phobias. When added to the network, the three strongest connections with lifetime panic disorder were all agoraphobic fears: being in crowds, going out of the house alone, and being in open spaces. CONCLUSIONS: Using network analyses applied to a large epidemiologic twin sample, we broadly validated the DSM-IV typography but did not entirely support the distinction of agoraphobic and social phobic fears or the DSM placements for fears of public restrooms and diseases. We found strong support for the specificity of the relationship between panic disorder and agoraphobic fears.

Common measures or common metrics? A plea to harmonize measurement results.

OBJECTIVE: There is a great variety of measurement instruments to assess similar constructs in clinical research and practice. This complicates the interpretation of test results and hampers the implementation of measurement-based care. METHOD: For reporting and discussing test results with patients, we suggest converting test results into universally applicable common metrics. Two well-established metrics are reviewed: T scores and percentile ranks. Their calculation is explained, their merits and drawbacks are discussed, and recommendations for the most convenient reference group are provided. RESULTS: We propose to express test results as T scores with the general population as reference group. To elucidate test results to patients, T scores may be supplemented with percentile ranks, based on data from a clinical sample. The practical benefits are demonstrated using the published data of four frequently used instruments for measuring depression: the CES-D, PHQ-9, BDI-II and the PROMIS depression measure. DISCUSSION: Recent initiatives have proposed to mandate a limited set of outcome measures to harmonize clinical measurement. However, the selected instruments are not without flaws and, potentially, this directive may hamper future instrument development. We recommend using common metrics as an alternative approach to harmonize test results in clinical practice, as this will facilitate the integration of measures in day-to-day practice.

Psychological networks in clinical populations: investigating the consequences of Berkson's bias.

BACKGROUND: In clinical research, populations are often selected on the sum-score of diagnostic criteria such as symptoms. Estimating statistical models where a subset of the data is selected based on a function of the analyzed variables introduces Berkson's bias, which presents a potential threat to the validity of findings in the clinical literature. The aim of the present paper is to investigate the effect of Berkson's bias on the performance of the two most commonly used psychological network models: the Gaussian Graphical Model (GGM) for continuous and ordinal data, and the Ising Model for binary data. METHODS: In two simulation studies, we test how well the two models recover a true network structure when estimation is based on a subset of the data typically seen in clinical studies. The network is based on a dataset of 2807 patients diagnosed with major depression, and nodes in the network are items from the Hamilton Rating Scale for Depression (HRSD). The simulation studies test different scenarios by varying (1) sample size and (2) the cut-off value of the sum-score which governs the selection of participants. RESULTS: The results of both studies indicate that higher cut-off values are associated with worse recovery of the network structure. As expected from the Berkson's bias literature, selection reduced recovery rates by inducing negative connections between the items. CONCLUSION: Our findings provide evidence that Berkson's bias is a considerable and underappreciated problem in the clinical network literature. Furthermore, we discuss potential solutions to circumvent Berkson's bias and their pitfalls.

A computational network perspective on pediatric anxiety symptoms.

BACKGROUND: While taxonomy segregates anxiety symptoms into diagnoses, patients typically present with multiple diagnoses; this poses major challenges, particularly for youth, where mixed presentation is particularly common. Anxiety comorbidity could reflect multivariate, cross-domain interactions insufficiently emphasized in current taxonomy. We utilize network analytic approaches that model these interactions by characterizing pediatric anxiety as involving distinct, inter-connected, symptom domains. Quantifying this network structure could inform views of pediatric anxiety that shape clinical practice and research. METHODS: Participants were 4964 youths (ages 5-17 years) from seven international sites. Participants completed standard symptom inventory assessing severity along distinct domains that follow pediatric anxiety diagnostic categories. We first applied network analytic tools to quantify the anxiety domain network structure. We then examined whether variation in the network structure related to age (3-year longitudinal assessments) and sex, key moderators of pediatric anxiety expression. RESULTS: The anxiety network featured a highly inter-connected structure; all domains correlated positively but to varying degrees. Anxiety patients and healthy youth differed in severity but demonstrated a comparable network structure. We noted specific sex differences in the network structure; longitudinal data indicated additional structural changes during childhood. Generalized-anxiety and panic symptoms consistently emerged as central domains. CONCLUSIONS: Pediatric anxiety manifests along multiple, inter-connected symptom domains. By quantifying cross-domain associations and related moderation effects, the current study might shape views on the diagnosis, treatment, and study of pediatric anxiety.

Editorial Perspective: Prescribing measures: unintended negative consequences of mandating standardized mental health measurement.

In July 2020, two of the largest funders of mental health research worldwide - the National Institute of Mental Health (NIMH) and the Wellcome Trust - announced plans to standardize mental health measurement. Specifically, obtaining funding for research related to depression and anxiety will be conditional on using four specific measures. While we agree that there are obvious benefits to standardizing mental health measurement, some of which are discussed in the announcement by NIMH and Wellcome, here we focus on potential unintended negative consequences of this initiative: Lacking transferability across settings: scales were developed for specific settings (e.g. community, clinic) and purposes (e.g. intervention studies), and their properties might not be easily transferable between settings. Narrowing the scope of inquiry: individuals experience mental health difficulties in wide-ranging ways, and the narrow scope of the proposed scales risks limiting important insights for research and treatments. Lowering the threshold for robust evidence: empirical findings limited to a specific imperfect measure are less robust than if such evidence is (re)produced across multiple scales. Creating a two-tiered mental health science: arbitrarily conferring gold standard status on some imperfect measures over others will create an artificial two-tiered system leading to an impoverishment of mental health research. Recommendations for mitigating these negative consequences include the following: mandating a wider set of measures that have been validated for specific populations and research purposes, funding research assessing the measurement properties of scales across settings and purposes, stressing the limitations of mandated measures to avoid en masse application and replacement of measures across studies and health systems and creating speed bumps to ensure that any widespread adoption of mandated measures does not result in impoverishment of mental health science.

Network analysis of PTSD and depressive symptoms in 158,139 treatment-seeking veterans with PTSD.

BACKGROUND: In recent years, a new framework for analyzing and understanding posttraumatic stress disorder (PTSD) was introduced; the network approach. Up until now, network analysis studies of PTSD were largely conducted on small to medium sample sizes (N < 1,000), which might be a possible cause of variability in main findings. Moreover, only a limited number of network studies investigated comorbidity. METHODS: In this study, we utilized a large sample to conduct a network analysis of 17 symptoms of PTSD (DSM-IV), and compared it to the result of a second network consisting of symptoms of PTSD and depression (based on Patient Health Questionnaire-9 [PHQ-9]). Our sample consisted of 502,036 treatment-seeking veterans, out of which 158,139 had fully completed the assessment of symptoms of PTSD and a subsample of 32,841 with valid PCL and PHQ-9 that was administered within 14 days or less. RESULTS: Analyses found that in the PTSD network, the most central symptoms were feeling distant or cut off from others, followed by feeling very upset when reminded of the event, and repeated disturbing memories or thoughts of the event. In the combined network, we found that concentration difficulties and anhedonia are two of the five most central symptoms. CONCLUSION: Our findings replicate the centrality of intrusion symptoms in PTSD symptoms' network. Taking into account the large sample and high stability of the network structure, we believe our study can answer some of the criticism regarding stability of cross-sectional network structures.

Heterogeneity in major depression and its melancholic and atypical specifiers: a secondary analysis of STAR*D.

OBJECTIVES: The melancholic and atypical specifiers for a major depressive episode (MDE) are supposed to reduce heterogeneity in symptom presentation by requiring additional, specific features. Fried et al. (2020) recently showed that the melancholic specifier may increase the potential heterogeneity in presenting symptoms. In a large sample of outpatients with depression, our objective was to explore whether the melancholic and atypical specifiers reduced observed heterogeneity in symptoms. METHODS: We used baseline data from the Inventory of Depression Symptoms (IDS), which was available for 3,717 patients, from the Sequenced Alternatives to Relieve Depression (STAR*D) trial. A subsample met criteria for MDE on the IDS ("IDS-MDE"; N =2,496). For patients with IDS-MDE, we differentiated between those with melancholic, non-melancholic, non-melancholic, atypical, and non-atypical depression. We quantified the observed heterogeneity between groups by counting the number of unique symptom combinations pertaining to their given diagnostic group (e.g., counting the melancholic symptoms for melancholic and non-melancholic groups), as well as the profiles of DSM-MDE symptoms (i.e., ignoring the specifier symptoms). RESULTS: When considering the specifier and depressive symptoms, there was more observed heterogeneity within the melancholic and atypical subgroups than in the IDS-MDE sample (i.e., ignoring the specifier subgroups). The differences in number of profiles between the melancholic and non-melancholic groups were not statistically significant, irrespective of whether focusing on the specifier symptoms or only the DSM-MDE symptoms. The differences between the atypical and non-atypical subgroups were smaller than what would be expected by chance. We found no evidence that the specifier groups reduce heterogeneity, as can be quantified by unique symptom profiles. Most symptom profiles, even in the specifier subgroups, had five or fewer individuals. CONCLUSION: We found no evidence that the atypical and melancholic specifiers create more symptomatically homogeneous groups. Indeed, the melancholic and atypical specifiers introduce heterogeneity by adding symptoms to the DSM diagnosis of MDE.

On the Importance of Estimating Parameter Uncertainty in Network Psychometrics: A Response to Forbes et al. (2019).

In their recent paper, Forbes et al. (2019; FWMK) evaluate the replicability of network models in two studies. They identify considerable replicability issues, concluding that "current 'state-of-the-art' methods in the psychopathology network literature […] are not well-suited to analyzing the structure of the relationships between individual symptoms". Such strong claims require strong evidence, which the authors do not provide. FWMK identify low replicability by analyzing point estimates of networks; contrast low replicability with results of two statistical tests that indicate higher replicability, and conclude that these tests are problematic. We make four points. First, statistical tests are superior to the visual inspection of point estimates, because tests take into account sampling variability. Second, FWMK misinterpret the statistical tests in several important ways. Third, FWMK did not follow established recommendations when estimating networks in their first study, underestimating replicability. Fourth, FWMK draw conclusions about methodology, which does not follow from investigations of data, and requires investigations of methodology. Overall, we show that the "poor replicability "observed by FWMK occurs due to sampling variability and use of suboptimal methods. We conclude by discussing important recent simulation work that guides researchers to use models appropriate for their data, such as nonregularized estimation routines.

Investigating the Utility of Fixed-margin Sampling in Network Psychometrics.

Steinley, Hoffman, Brusco, and Sher (2017) proposed a new method for evaluating the performance of psychological network models: fixed-margin sampling. The authors investigated LASSO regularized Ising models (eLasso) by generating random datasets with the same margins as the original binary dataset, and concluded that many estimated eLasso parameters are not distinguishable from those that would be expected if the data were generated by chance. We argue that fixed-margin sampling cannot be used for this purpose, as it generates data under a particular null-hypothesis: a unidimensional factor model with interchangeable indicators (i.e., the Rasch model). We show this by discussing relevant psychometric literature and by performing simulation studies. Results indicate that while eLasso correctly estimated network models and estimated almost no edges due to chance, fixed-margin sampling performed poorly in classifying true effects as "interesting" (Steinley et al. 2017, p. 1004). Further simulation studies indicate that fixed-margin sampling offers a powerful method for highlighting local misfit from the Rasch model, but performs only moderately in identifying global departures from the Rasch model. We conclude that fixed-margin sampling is not up to the task of assessing if results from estimated Ising models or other multivariate psychometric models are due to chance.

Systems all the way down: embracing complexity in mental health research.

In this editorial for the collection on complexity in mental health research, we introduce and summarize the inaugural contributions to this collection: a series of theoretical, methodological, and empirical papers that aim to chart a path forward for investigating mental health in all its complexity. A central theme emerges from these contributions: if we are to make genuine progress in explaining, predicting, and treating mental illness, we must study the systems from which psychopathology emerges. As the articles in this collection make clear, the systems that give rise to psychopathology encompass a host of components across biological, psychological, and social levels of analysis, intertwined in a web of complex interactions. The task of advancing our understanding of these systems will be a challenging one. Yet, this challenge presents a unique opportunity. From physics to ecology, there is a rapidly evolving body of interdisciplinary research dedicated to investigating complex systems. This work provides clear guidance for psychiatric research, opportunities for collaboration, and a set of tools and concepts from which we can draw in our efforts to understand mental health, helping us move toward our ultimate aim of improving the prevention and treatment of psychopathology.

Dysfunctional posttraumatic cognitions, posttraumatic stress and depression in children and adolescents exposed to trauma: a network analysis.

BACKGROUND: The latest version of the International Classification of Diseases (ICD-11) proposes a posttraumatic stress disorder (PTSD) diagnosis reduced to its core symptoms within the symptom clusters re-experiencing, avoidance and hyperarousal. Since children and adolescents often show a variety of internalizing and externalizing symptoms in the aftermath of traumatic events, the question arises whether such a conceptualization of the PTSD diagnosis is supported in children and adolescents. Furthermore, although dysfunctional posttraumatic cognitions (PTCs) appear to play an important role in the development and persistence of PTSD in children and adolescents, their function within diagnostic frameworks requires clarification. METHODS: We compiled a large international data set of 2,313 children and adolescents aged 6 to 18 years exposed to trauma and calculated a network model including dysfunctional PTCs, PTSD core symptoms and depression symptoms. Central items and relations between constructs were investigated. RESULTS: The PTSD re-experiencing symptoms strong or overwhelming emotions and strong physical sensations and the depression symptom difficulty concentrating emerged as most central. Items from the same construct were more strongly connected with each other than with items from the other constructs. Dysfunctional PTCs were not more strongly connected to core PTSD symptoms than to depression symptoms. CONCLUSIONS: Our findings provide support that a PTSD diagnosis reduced to its core symptoms could help to disentangle PTSD, depression and dysfunctional PTCs. Using longitudinal data and complementing between-subject with within-subject analyses might provide further insight into the relationship between dysfunctional PTCs, PTSD and depression.

Dynamic Network Analysis of Negative Emotions and DSM-5 Posttraumatic Stress Disorder Symptom Clusters During Conflict.

Investigating dynamic associations between specific negative emotions and PTSD symptom clusters may provide novel insights into the ways in which PTSD symptoms interact with, emerge from, or are reinforced by negative emotions. The present study estimated the associations among negative emotions and the four DSM-5 PTSD symptom clusters (intrusions, avoidance, negative alterations in cognitions and mood [NACM], and arousal) in a sample of Israeli civilians (n = 96) during the Israel-Gaza War of July-August 2014. Data were collected using experience sampling methodology, with participants queried via smartphone about PTSD symptoms and negative emotions twice a day for 30 days. We used a multilevel vector auto-regression model to estimate temporal and contemporaneous temporal networks. Contrary to our hypothesis, in the temporal network, PTSD symptom clusters were more predictive of negative emotions than vice versa, with arousal emerging as the strongest predictor that negative emotions would be reported at the next measurement point; fear and sadness were also strong predictors of PTSD symptom clusters. In the contemporaneous network, negative emotions exhibited the strongest associations with the NACM and arousal PTSD symptom clusters. The negative emotions of sadness, stress, fear, and loneliness had the strongest associations to the PTSD symptom clusters. Our findings suggest that arousal has strong associations to both PTSD symptoms and negative emotions during ongoing trauma and highlights the potentially relevant role of arousal for future investigations in primary or early interventions.