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The Diagnostic and Statistical Manual of Mental Disorders – Fifth Edition (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. This descriptive study mapped the repetition among the 1,419 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. 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. Overall, our findings lay the foundation for a better understanding of the extent and potential consequences of symptom overlap.

Research in the last decade has expressed considerable optimism about the clinical potential of psychedelics for the treatment of mental disorders. This optimism is reflected in a considerable increase in research papers, investments by pharmaceutical companies, patents, media coverage, as well as political and legislative changes. However, psychedelic science is facing serious challenges that threaten the validity of core findings and raise doubt regarding clinical efficacy and safety. In this paper, we introduce the 10 most pressing challenges, grouped into easy, moderate, and hard problems. We show how these problems threaten internal validity (treatment effects are due to factors unrelated to the treatment), external validity (lack of generalizability), construct validity (unclear working mechanism) or statistical conclusion validity (conclusions do not follow from the data and methods). These problems tend to co-occur in psychedelic studies, strongly limiting conclusions that can be drawn about the safety and efficacy of psychedelic therapy. We provide a roadmap for tackling these challenges in the next decade of psychedelic science, and share a checklist that researchers, journalists, funders, policy makers, and other stakeholders can use to assess the quality of psychedelic science. Addressing today’s problems is necessary to find out whether the optimism regarding the potential of psychedelics has been warranted and to avoid history repeating itself.

Quantitative, empirical approaches to establishing the structure of psychopathology hold promise to improve on traditional psychiatric classification systems. The Hierarchical Taxonomy of Psychopathology (HiTOP) is a framework that summarizes the substantial and growing body of quantitative evidence on the structure of psychopathology. To achieve its aims, HiTOP must incorporate emerging research in a systematic, ongoing fashion. In this paper, we introduce the conceptual background, organizing principles, core assumptions, and procedures for revising the HiTOP framework. Informed by strengths and shortcomings of previous classification systems, the proposed revisions protocol is a formalized system focused around three pillars: 1) prioritizing systematic evaluation of quantitative evidence by a set of transparent criteria and processes, 2) balancing stability with flexibility, and 3) promoting inclusion over gatekeeping in all aspects of the process. We detail how the revisions protocol will be applied in practice, including the scientific and administrative aspects of the process. Additionally, we describe areas of the HiTOP structure that will be a focus of early revisions and outline challenges for the revisions protocol moving forward. The proposed revisions protocol will ensure that the HiTOP framework reflects the current state of scientific knowledge on the structure of psychopathology and fulfills its potential to advance clinical research and practice.

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.

Mental disorders are highly prevalent, and among the leading causes of global disease burden. To respond in a timely and effective manner, a strong understanding of the structure of psychopathology and its development is critical. We compared the ability of two competing frameworks, the dynamic mutualism theory and the common cause theory, to explain the development of individual differences in psychopathology. We formalized these theories into statistical models, and applied them to two domains of psychopathology, at two different developmental periods, using two large developmental cohorts: the p factor (i.e. general psychopathology) from early to late adolescence (N = 1,482), and major depressive disorder in middle adulthood and old age (N = 6,443). The development of the p factor was better explained by a mutualistic account. In contrast, the evidence for the development of major depression was more ambiguous. Our results support a multicausal approach to understanding psychopathology and showcase the value of translating theories into testable statistical models for understanding developmental processes in clinical sciences.

Several studies have identified relationships between cognitive functioning and posttraumatic stress disorder (PTSD). Here, we aimed to elucidate the nature of this relationship by investigating cross-sectional associations between subjective cognitive functioning (SCF) and 1) the PTSD sum score, 2) symptom domains, and 3) individual symptoms. We also investigated temporal stability by testing whether results replicated over a 3-year period. We estimated partial correlation networks of DSM-5 PTSD symptoms (at baseline) and SCF (at baseline and follow-up, respectively), using data from the National Health and Resilience in Veterans Study (NHRVS; N = 1,484; Mdn = 65 years). The PTSD sum score was negatively associated with SCF. SCF was consistently negatively associated with the PTSD symptom domains ‘marked alterations in arousal and reactivity’ and ‘negative alterations in cognitions and mood’, and showed robust relations with the symptoms ‘having difficulty concentrating’ and ‘trouble experiencing positive feelings’. Results largely replicated at the 3-year follow-up, suggesting that PTSD symptoms may play a causal role in the development or maintenance of SCF. We discuss the importance of examining links between specific PTSD domains and symptoms with SCF—relations obfuscated by focusing on PTSD diagnoses or sum scores—as well as investigating mechanisms underlying these relations.

Over the past decade, the idiographic approach has received significant attention in clinical psychology, incentivizing the development of novel approaches to estimate statistical models, such as personalized networks. Although the notion of such networks aligns well with the way clinicians think and reason, there are currently several barriers to implementation that limit their clinical utility. To address these issues, we introduce the Prior Elicitation Module for Idiographic System Estimation (PREMISE), a novel approach that formally integrates case formulations with personalized network estimation via prior elicitation and Bayesian inference. PREMISE tackles current implementation barriers of personalized networks; incorporating clinical information into personalized network estimation systematically allows theoretical and data-driven integration, supporting clinician and patient collaboration when building a dynamic understanding of the patient's psychopathology. To illustrate its potential, we estimate clinically informed networks for a patient suffering from obsessive-compulsive disorder. We discuss open challenges in selecting statistical models for PREMISE, as well as specific future directions for clinical implementation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Letter to the editor regarding “A Novel, Brief, Fully Automated Intervention to Extend the Antidepressant Effect of a Single Ketamine Infusion: A Randomized Clinical Trial”

Adolescence is a time period characterized by extremes in affect and increasing prevalence of mental health problems. During this time, the family remains a crucial source of both support and stress. 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, fathers and mothers. The families participated in the RE-PAIR study, where they reported their affect states in four ecological momentary assessments per day for 14 days. We performed two studies. 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, for both positive and negative affect, such as the reciprocal relation of irritation between adolescents and their parents, 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.

Depression is common, debilitating, often chronic, and affects young people disproportionately. Given that only 50% of patients improve under initial treatment, experts agree that prevention is the most effective way to change depression’s global disease burden. The biggest barrier to successful prevention is to identify those at risk for depression in the near future. To close this gap, this protocol paper introduces the WARN-D study, our effort to build a personalized early warning system for depression. To develop the system, we follow around 2000 students over 2 years. Stage 1 comprises an extensive baseline assessment in which we collect a broad set of predictors for depression. Stage 2 lasts 3 months and zooms into participants’ daily experiences that may predict depression; we use smartwatches to collect digital phenotype data such as sleep and activity, and we use a smartphone app to query participants about their experiences 4 times a day and once every Sunday. In Stage 3, we follow participants for 21 months, assessing transdiagnostic outcomes (including stress, functional impairment, anxiety, and depression) as well as additional predictors for future depression every 3 months. Collected data will be utilized to build a personalized prediction model for depression onset. Overall, WARN-D will function similar to a weather forecast, with the core difference that one can only run or seek shelter from a thunderstorm, while depression may be successfully prevented before it occurs.

Network psychometric models are often estimated using a single indicator for each node in the network, thus failing to consider potential measurement error. In this study, we investigate the impact of measurement error on cross-sectional network models. First, we conduct a simulation study to evaluate the performance of models based on single indicators as well as models that utilize information from multiple indicators per node, including average scores, factor scores, and latent variables. Our results demonstrate that measurement error impairs the reliability and performance of network models, especially when using single indicators. The reliability and performance of network models improves substantially with increasing sample size and when using methods that combine information from multiple indicators per node. Second, we use empirical data from the STAR*D trial (n = 3,731) to further evaluate the impact of measurement error. In the STAR*D trial, depression symptoms were assessed via three questionnaires, providing multiple indicators per symptom. Consistent with our simulation results, we find that when using sub-samples of this dataset, the discrepancy between the three single-indicator networks (one network per questionnaire) diminishes with increasing sample size. Together, our simulated and empirical findings provide evidence that measurement error can hinder network estimation when working with smaller samples and offers guidance on methods to mitigate measurement error.

As evidenced by our exchange with Bader and Moshagen (2022), the degree to which model fit indices can and should be used for the purpose of model selection remains a contentious topic. Here, we make three core points. First, we discuss the common misconception about fit statistics' abilities to identify the "best model," arguing that mechanical application of model fit indices contributes to faulty inferences in the field of quantitative psychopathology. We illustrate the consequences of this practice through examples in the literature. Second, we highlight the parsimony-adjacent concept of fitting propensity, which is not accounted for by commonly used fit statistics. Finally, we present specific strategies to overcome interpretative bias and increase generalizability of study results and stress the importance of carefully balancing substantive and statistical criteria in model selection scenarios. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Mental disorders are among the leading causes of global disease burden. To respond effectively, a strong understanding of the structure of psychopathology is critical. We empirically compared two competing frameworks, dynamic-mutualism theory and common-cause theory, that vie to explain the development of psychopathology. We formalized these theories in statistical models and applied them to explain change in the general factor of psychopathology (p factor) from early to late adolescence ( N = 1,482) and major depression in middle adulthood and old age ( N = 6,443). Change in the p factor was better explained by mutualism according to model-fit indices. However, a core prediction of mutualism was not supported (i.e., predominantly positive causal interactions among distinct domains). The evidence for change in depression was more ambiguous. Our results support a multicausal approach to understanding psychopathology and showcase the value of translating theories into testable statistical models for understanding developmental processes in clinical sciences.

Background This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data. Methods Individual patient data from all six eligible randomised controlled trials were used to develop (k = 3, n = 1722) and test (k = 3, n = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1–3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3–4 months. Results Models 1–7 all outperformed the null model and model 8. Model performance was very similar across models 1–6, meaning that differential weights applied to the baseline sum scores had little impact. Conclusions Any of the modelling techniques (models 1–7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.

The core ideas of a 10-year research program ‘New Science of Mental Disorders’ are outlined. This research program moves away from the disorder-based ‘one-model-fits-all’ approach to treating mental disorders, and adopts the network approach to psychopathology as its foundation of research. Its core assumption is that dynamically interacting symptoms constitute the disorder. Our goal is to further develop the network approach by studying (1) dynamic networks of symptoms and other variables (i.e., elements) in a large number of individuals with a wide range of mental disorders from a transdiagnostic perspective (network-based diagnosis; mapping), including both Ecological Momentary Assessment (EMA) and digital phenotyping, (2) the transdiagnostic mechanisms reflecting potential causal relations among elements of the networks by performing experimental (pre-)clinical studies (zooming), and (3) the effectiveness of personalised network-informed interventions (targeting). Challenges to overcome in this research program are discussed, which relate to data collection (e.g., selection of EMA variables) and data analyses (e.g., power considerations), the development and application of network-informed diagnoses and network-informed interventions (e.g., what characteristic(s) of the network to target in interventions), and the implementation in clinical practice (e.g., train therapists in the use of networks in therapy).

Statistical network models describing multivariate dependency structures in psychological data have gained increasing popularity. Such comparably novel statistical techniques require specific guidelines to make them accessible to the research community. In this literature, researchers have previously provided tutorials guiding the estimation of networks and their accuracy. However, there is currently little guidance in determining what parts of the analyses and results should be documented in a scientific report. A lack of such reporting standards may foster researcher degrees of freedom and could provide fertile ground for questionable reporting practices. Here, we introduce reporting standards for network analyses in cross-sectional data, along with a tutorial and two examples. The presented guidelines are aimed at researchers as well as the broader scientific community, such as reviewers and journal editors evaluating scientific work. We conclude by discussing how the network literature specifically can benefit from such guidelines for reporting and transparency.

ObjectiveEating disorder (ED) symptoms and transdiagnostic vulnerability characteristics play a crucial role in the aetiology and maintenance of EDs. Over the last decade, researchers have started to model complex interrelations between symptoms using network models, but the literature is limited in that it has focused solely on symptoms and investigated‐specific disorders while ignoring transdiagnostic aspects of mental health.MethodThis study tackles these challenges by investigating network relations among core ED symptoms, comorbid clinical symptoms (depression and anxiety) and empirically supported vulnerability and protective mechanisms (personality traits, maladaptive cognitive schemata, perfectionism and resilience) in a sample of 2302 treatment‐seeking ED patients. We estimated a regularized partial correlation network to obtain conditional dependence relations among all variables. We estimated node centrality (interconnectivity) and node predictability (the overall magnitude of symptom inter‐relationships).ResultsThe findings indicate a central role of overvigilance, excessive focus on inhibiting emotions and feelings, interoceptive awareness and perfectionism.ConclusionsThese results suggest that excessive control of bodily aspects by dietary restraint (possibly through inhibition) and interoceptive awareness may be important constructs that warrant future research in understanding vulnerability in EDs. We provide all code and data via the Open Science Framework.

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.

In this article, we define questionable measurement practices (QMPs) as decisions researchers make that raise doubts about the validity of the measures, and ultimately the validity of study conclusions. Doubts arise for a host of reasons, including a lack of transparency, ignorance, negligence, or misrepresentation of the evidence. We describe the scope of the problem and focus on how transparency is a part of the solution. A lack of measurement transparency makes it impossible to evaluate potential threats to internal, external, statistical-conclusion, and construct validity. We demonstrate that psychology is plagued by a measurement schmeasurement attitude: QMPs are common, hide a stunning source of researcher degrees of freedom, and pose a serious threat to cumulative psychological science, but are largely ignored. We address these challenges by providing a set of questions that researchers and consumers of scientific research can consider to identify and avoid QMPs. Transparent answers to these measurement questions promote rigorous research, allow for thorough evaluations of a study’s inferences, and are necessary for meaningful replication studies.

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

For many students, the COVID-19 pandemic caused once-in-a-lifetime disruptions of daily life. In March 2020, during the beginning of the outbreak in the Netherlands, we used ecological momentary assessment to follow 80 undergraduate students four times per day for 14 days to assess mental health, social contact, and COVID-19-related variables. Despite rapidly increasing rates of infections and deaths, we observed decreases in anxiety, loneliness, and COVID-19-related concerns, especially in the first few days. Other mental health variables, such as stress levels, remained stable, whereas depressive symptoms increased. Despite social-distancing measures implemented by the Dutch government halfway through our study, students showed no changes in the frequency of in-person social activities. Dynamic network models identified potential vicious cycles between mental health variables and being alone, which predicted concerns about COVID-19 and was followed by further mental health problems. Findings and implications are discussed in detail.

Psychotherapy is an effective treatment for many common mental health problems, but the mechanisms of action and processes of change are unclear, perhaps driven by the focus on a single diagnosis which does not reflect the heterogeneous symptom experiences of many patients. The objective of this study was to better understand therapeutic change, by illustrating how symptoms evolve and interact during psychotherapy. Data from 113,608 patients from psychological therapy services who completed depression and anxiety symptom measures across three to six therapy sessions were analysed. A panel graphical vector-autoregression model was estimated in a model development sample (N = 68,165) and generalizability was tested in a confirmatory model, fitted to a separate (hold-out) sample of patients (N = 45,443). The model displayed an excellent fit and replicated in the confirmatory holdout sample. First, we found that nearly all symptoms were statistically related to each other (i.e. dense connectivity), indicating that no one symptom or association drives change. Second, the structure of symptom interrelations which emerged did not change across sessions. These findings provide a dynamic view of the process of symptom change during psychotherapy and give rise to several causal hypotheses relating to structure, mechanism, and process.

Although a growing body of research has documented racial/ethnic disparities in depressive symptoms in the United States, the precise magnitude of these differences is not known. We conducted a systematic review and meta-analysis of individual participant data to (1) estimate the average difference of depressive symptoms between Whites and racial/ethnic minorities, as well as differences between (i.e., Asian American, African American, Latinxs, Multiracial, Native American, other race) and within (i.e., Latinx: Central American, Cuban American, Mexican American, Puerto Rican, other Latinx) minority groups, and (2) determine if moderators account for these differences. We screened 2,425 nationally-representative studies from the Inter-university Consortium for Political and Social Research (ICPSR), and identified 127 datasets of studies conducted from 1971 to 2018. We included 73 datasets from 26 nationally-representative studies (N = 2,116,853). The average absolute difference was d = 0.09, 95% CI [0.07, 0.12] between White and minority participants; was d = 0.07, 95% CI [0.06, 0.09] between minority participants; and d = 0.10, 95% CI [0.06, 0.15] within minority Latinx participants. Increases in socioeconomic status exacerbated these disparities. Psychometric analyses showed that measure reliability was related to larger differences. We discuss the implications of these findings.

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. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Posttraumatic stress disorder assessments typically require individuals to provide an aggregate report on the frequency or severity of symptoms they have experienced over a particular time period. Yet retrospective aggregate assessments are susceptible to memory recall and retrieval difficulties. This study examined the correspondence between a month of real-time experience sampling methodology (ESM) reports of traumatic stress symptoms and a retrospective assessment of past-month traumatic stress symptoms for that same period. Participants were a convenience community sample (n=96) from Southern and Central Israel exposed to rocket fire during the Israel-Gaza July-Aug 2014 conflict. Participants provided ESM reports on traumatic stress symptoms twice a day for 30 days via smartphone. Average ESM scores, rather than peak or most recent reports, were most highly correlated with retrospective assessments. For individual symptoms, concentration difficulties had the highest correspondence between ESM and retrospective reports, while amnesia had the lowest correspondence. Regression analysis found that average ESM scores and younger age significantly predicted past-month retrospective assessments of PTSD symptoms. Additionally, previously experiencing more types of trauma predicted PTSD symptoms, but did not moderate the relationship between ESM and retrospective assessments. These findings have implications for assessment.

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.

Purpose Putative causal relations among depressive symptoms in forms of network structures have been of recent interest, with prior studies suggesting that high connectivity of the symptom network may drive the disease process. We examined in detail the network structure of depressive symptoms among participants with and without depressive disorders (DD; consisting of major depressive disorder (MDD) and dysthymia) at two time points. Methods Participants were from the nationally representative Health 2000 and Health 2011 surveys. In 2000 and 2011, there were 5998 healthy participants (DD−) and 595 participants with DD diagnosis (DD+). Depressive symptoms were measured using the 13-item version of the Beck Depression Inventory (BDI). Fused Graphical Lasso was used to estimate network structures, and mixed graphical models were used to assess network connectivity and symptom centrality. Network community structure was examined using the walktrap-algorithm and minimum spanning trees (MST). Symptom centrality was evaluated with expected influence and participation coefficients. Results Overall connectivity did not differ between networks from participants with and without DD, but more simple community structure was observed among those with DD compared to those without DD. Exploratory analyses revealed small differences between the samples in the order of one centrality estimate participation coefficient. Conclusions Community structure, but not overall connectivity of the symptom network, may be different for people with DD compared to people without DD. This difference may be of importance when estimating the overall connectivity differences between groups with and without mental disorders.

Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain–behavior relationships essential for general intelligence in a large (behavioral, N = 805, cortical volume, N = 246, fractional anisotropy, N = 165) developmental (ages 5–18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role ‘between’ brain and behavior. We discuss implications and possible avenues for future studies.

BackgroundDepression is commonly perceived as a single underlying disease with a number of potential treatment options. However, patients with major depression differ dramatically in their symptom presentation and comorbidities, e.g. with anxiety disorders. There are also large variations in treatment outcomes and associations of some anxiety comorbidities with poorer prognoses, but limited understanding as to why, and little information to inform the clinical management of depression. There is a need to improve our understanding of depression, incorporating anxiety comorbidity, and consider the association of a wide range of symptoms with treatment outcomes.MethodIndividual patient data from six RCTs of depressed patients (total n = 2858) were used to estimate the differential impact symptoms have on outcomes at three post intervention time points using individual items and sum scores. Symptom networks (graphical Gaussian model) were estimated to explore the functional relations among symptoms of depression and anxiety and compare networks for treatment remitters and those with persistent symptoms to identify potential prognostic indicators.ResultsItem-level prediction performed similarly to sum scores when predicting outcomes at 3 to 4 months and 6 to 8 months, but outperformed sum scores for 9 to 12 months. Pessimism emerged as the most important predictive symptom (relative to all other symptoms), across these time points. In the network structure at study entry, symptoms clustered into physical symptoms, cognitive symptoms, and anxiety symptoms. Sadness, pessimism, and indecision acted as bridges between communities, with sadness and failure/worthlessness being the most central (i.e. interconnected) symptoms. Connectivity of networks at study entry did not differ for future remitters vs. those with persistent symptoms.ConclusionThe relative importance of specific symptoms in association with outcomes and the interactions within the network highlight the value of transdiagnostic assessment and formulation of symptoms to both treatment and prognosis. We discuss the potential for complementary statistical approaches to improve our understanding of psychopathology.

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.

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.

This study investigates the Contingencies of Self-Worth Scale (CSWS) in a sample of 680 university students from a network perspective. We estimated regularized partial correlations among seven CSWS domains: family support, competition, appearance, God's love, academic competence, virtue and other's approval. Competition – academic competence and competition – appearance represent the strongest connections in the network. Mean node predictability (shared variance with surrounding nodes) is 0.25. Appearance and academic competence were the most central (i.e., interconnected) domains in the network. Future studies should explore the network structure of self-worth in other healthy adult samples, and also in people with psychopathology. We provide the anonymized dataset as well as the full code in the supplementary materials to ensure complete reproducibility of the results.