Exposure to descriptions of traumatic events narrows one's concept of trauma.

The concept of "trauma" was originally used by psychiatrists to describe horrific events such as rape and torture that characteristically provoke extreme emotional distress. Both colloquially and clinically, the concept of psychological trauma has broadened considerably. Although many clinical scientists have expressed concern about the broadening of the concept of trauma, it remains unclear how this concept expansion occurs. We present two experiments in which American adults (N = 276 and N = 267) sequentially classified descriptions of events (e.g., "broke a leg in a bicycle accident") as either "trauma" or "not trauma." In the first experiment, we manipulated the frequency of severe events (i.e., severe events became less and less common). In the second experiment, we manipulated the range of events (i.e., participants viewed only severe or only nonsevere events). Together, the findings suggest that an individual's frame of reference for the severity of events plays a role in narrowing or broadening the concept of trauma. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Clarifying Relations Between ADHD and Functional Impairment in Adulthood: Utilization of Network and Machine Learning Approaches.

Using network analysis and random forest regression, this study identified attention-deficit/hyperactivity disorder (ADHD) symptoms most important for indicating impairment in various functional domains. Participants comprised a nationally representative sample of 1249 adults in the United States. Bridge symptoms were identified as those demonstrating unique relations with impairment domains that, in total, were stronger than those involving other symptoms. Results suggested three inattentive (i.e., difficulty organizing; does not follow through; makes careless mistakes) and one hyperactive (difficulty engaging in leisure activities) bridge symptoms. Random forest regression results supported bridge symptoms as most important (compared to other symptoms) for predicting global and specific impairment domains. Hyperactive/impulsive symptoms appeared more strongly related to impairment in women, whereas difficulty organizing and easily distracted appeared more related to impairment in men. Clarification of bridge symptoms may help identify core characteristics of ADHD in adulthood and specify screening and intervention targets to reduce risk for related impairment.

Sexual Orientation Complexity and Psychosocial/Health Outcomes.

Individuals whose sexual attraction or behavior varies from others' expectations based on their sexual identity were initially described as exhibiting sexual orientation discordance. This conceptualization has been challenged as inaccurate and value-laden, and "branchedness" has been suggested as a value-neutral description. Using a United States national sample of 4,530 participants from the 2013-2014 Center for Collegiate Mental Health database, we challenge the empirical distinctness of the phenomenon of sexual orientation discordance by 1) replicating previous work that indicates that branched individuals evidence unique psychosocial and health outcomes relative to non-branched individuals and 2) using stepwise regression to demonstrate that these differences in outcomes can be accounted for by variation in sexual attraction, behavior, and identity and that "discordance" between indicators failed to explain additional variation in outcomes. We encourage researchers to adopt non-normative language and conceptualizations in their study of sexual orientation complexity and branchedness.

Does broadening one's concept of trauma undermine resilience?

Objective: The term "trauma" seems to have expanded from a narrow usage (referring exclusively to extreme events such as rape and warfare) to a broad usage (encompassing almost any event that results in emotional distress). Today, individuals vary widely in the extent to which their personal "trauma concept" is relatively narrow or broad. In this study, we explore whether this variation is important to individuals' actual experience when facing a stressful event. Method: Participants were randomized to a set of "Narrow" or "Broad" belief induction tasks. They then watched a short film clip involving a mutilated corpse and completed self-report response measures. Days following the task, participants reported event-related symptoms. Results: Individuals with broader beliefs about trauma experienced more intense negative emotions and were more likely to report viewing the film clip as a personal trauma. Moreover, those who saw the film clip as a personal trauma reported more event-related distress (e.g., intrusions, nightmares) in the days after they watched it. We found limited support for causality, with the experimental manipulation showing a significant direct effect on personal trauma concepts but only indirect effects on other outcomes. Conclusion: Broader personal trauma concepts were related to increased vulnerability in a trauma film paradigm. While some evidence suggests causality, it remains possible that at least part of the effect is explained by a third variable causing both broad concepts and vulnerability (e.g., high trait anxiety). (PsycInfo Database Record (c) 2022 APA, all rights reserved).

An Answer to "So What?" Implications of Network Theory for Research and Practice.

Research and practice in psychiatry and clinical psychology have been guided by differing schools of thought over the years. Recently, the network theory of psychopathology has arisen as a framework for thinking about mental health. Network theory challenges three common assumptions: psychological problems are caused by disease entities that exist independently of their signs and symptoms, classification and diagnosis of psychological problems should follow a medical model, and psychological problems are caused by diseases or aberrations in the brain. Conversely, network theory embraces other assumptions that are well accepted in clinical practice (e.g., the interaction of thoughts, behaviors, and emotions, as posited in cognitive-behavioral therapies) and integrates those assumptions into a coherent framework for research and practice. In this article, the authors review developments in network theory by focusing on anxiety-related conditions, discuss future areas for change, and outline implications of network theory for research and clinical practice.

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

BACKGROUND: Network analyses have been applied to understand the relationships between individual symptoms of depression and anxiety. However, little is known about which symptoms are most strongly associated with "positive" indicators of mental health, such as happiness. Furthermore, few studies have examined symptom networks in participants from low- and middle-income countries. METHODS: To address these gaps, we applied network analyses in a sample of Indian adolescents (Study 1; n=1080) and replicated these analyses in a pre-registered study with Kenyan adolescents (Study 2; n=2176). Participants from both samples completed the same measures of depressive symptoms, anxiety symptoms, and happiness. RESULTS: Feeling sad and feeling like a failure had the strongest (negative) associations with happiness items. These two symptoms, as well as worrying and feeling nervous, had the strongest associations with other symptoms of depression and anxiety. Symptoms of depression and anxiety formed a single cluster, which was distinct from a cluster of happiness items. Main findings were consistent across the two samples, suggesting a cross-culturally robust pattern. LIMITATIONS: We used cross-sectional data, and we administered scales assessing a limited subset of symptoms and happiness items. CONCLUSIONS: Our findings support the idea that some symptoms of depression and anxiety are more strongly associated with happiness. These findings contribute to a body of literature emphasizing the advantages of symptom-level analyses. We discuss how efforts to understand associations between individual symptoms and "positive" mental health indicators, like happiness, could have theoretical and practical implications for clinical psychological science.

What's in a trauma? Using machine learning to unpack what makes an event traumatic.

What differentiates a trauma from an event that is merely upsetting? Wildly different definitions of trauma have been used in both formal (psychiatric) and informal (cultural, colloquial) settings. Yet there is a dearth of empirical work examining the features of events that individuals use to define an event as a 'trauma.' First, a group of qualitative coders classified features (e.g., actual physical injury, loss of possessions) of 600 event descriptions (e.g., "was verbally harassed by a boss," "watched a video of an adult being shot and killed"). Next, across two studies, machine learning was used to predict whether individuals rated event descriptions as 'trauma' or 'traumatic' in over 100,000 judgment tasks. In Study 1, examining continuous ratings from 'not at all traumatic' to 'extremely traumatic,' a cross-validated LASSO regression with polynomial features provided the best out-of-sample predictions (r2 = 0.76), outperforming ridge regression, support vector regression, and linear regression. In Study 2, using binary judgments, a random forest model accurately predicted out-of-sample individual responses (AUC = 0.96), outperforming a neural network and an AdaBoost ensemble classifier. The most important event features across the two studies were actual death, threat of death, and the presence of a human perpetrator. The most important human features in predicting judgments were political orientation and gender.

A Network Analysis of DSM-5 Posttraumatic Stress Disorder Symptoms and Event Centrality.

The centrality of a traumatic event to one's autobiographical memory has been associated with posttraumatic stress disorder (PTSD) symptom severity. In the present study, we investigated the associations between specific features of event centrality (EC), as measured using the Centrality of Event Scale, and specific symptoms of PTSD. We computed a cross-sectional graphical lasso network of PTSD symptoms and specific features of EC in a sample of trauma-exposed individuals (n = 451), many of whom met the clinical threshold for a PTSD diagnosis. The graphical lasso revealed intrusive memories, negative trauma-related feelings, and the perception that the traumatic event was central to one's identity to be influential nodes. Viewing the future through the lens of one's trauma exposure was the EC feature most strongly linked to PTSD. Among all PTSD symptoms, blaming oneself or others for the traumatic event showed the strongest link to EC. The network was stable, allowing for reliable interpretations. Future longitudinal research is needed to clarify the associations among EC features and PTSD symptoms over time.

Posttraumatic stress or posttraumatic growth? Using network analysis to explore the relationships between coping styles and trauma outcomes.

Trauma can produce posttraumatic stress disorder (PTSD), but may also foster positive outcomes, such as posttraumatic growth. Individual differences in coping styles may contribute to both positive and negative sequelae of trauma. Using network analytic methods, we investigated the structure of PTSD symptoms, elements of growth, and coping styles in bereaved survivors of a major earthquake in China. Hypervigilance and difficulty concentrating were identified as the most central symptoms in the PTSD network, whereas establishing a new path in life, feeling closer to others, and doing better things with life ranked highest on centrality in the posttraumatic growth network. Direct connections between PTSD symptoms and elements of growth were low in magnitude in our sample. Our final network, which included PTSD symptoms, growth elements, and coping styles, suggests that adaptive and active coping styles, such as positive reframing, are positively related to elements of growth, but not appreciably negatively related to PTSD symptoms. Conversely, maladaptive coping styles are positively related to PTSD symptoms, but are not negatively associated with growth. Future longitudinal studies could shed light on the direction of causality in these relationships and their clinical utility.

Mood symptoms and impairment due to substance use: A network perspective on comorbidity.

BACKGROUND: Mood disorders and problematic substance use co-occur and confer reciprocal risk for each other. Few studies use analytic approaches appropriate for testing whether specific features of one disorder confer risk for the other. METHODS: 445 participants (59.8% female, Mean age = 20.3 years) completed measures of depression and hypo/mania symptoms and substance use-related impairment; 330 had complete data at follow-up. Of these, 28% reported a history of depression, 4% of bipolar spectrum disorder, 11% of substance use disorder, and 55% reported substance-related impairment. Symptoms and domains of substance-related impairment were modeled in cross-sectional and cross-lagged panel network models. RESULTS: Impulsive and interpersonal impairment were most highly comorbid with mood symptoms. Suicidal ideation, sadness, decreased need for sleep, and guilt were the symptoms most highly comorbid with impairment. Interpersonal impairment due to substance use was the strongest cross-construct predictor of mood symptoms and suicidal ideation was most predictive of impairment. Social, intrapersonal, and physical impairment due to substance use were most predicted by previous mood symptoms and decreased need for sleep, guilt, and euphoria were most strongly predicted by past impairment. LIMITATIONS: Measures do not assess all mood symptoms, participants with low reward sensitivity were excluded, only self-report measures were used, and some variables were single-items. CONCLUSIONS: Components of these syndromes that confer cross-construct risk might not be the same components that are predicted by the other construct. The bidirectional relationship between mood symptoms and problematic substance use might be better conceptualized at the element, rather than diagnostic, level.

Bridge Centrality: A Network Approach to Understanding Comorbidity.

Recently, researchers in clinical psychology have endeavored to create network models of the relationships between symptoms, both within and across mental disorders. Symptoms that connect two mental disorders are called "bridge symptoms." Unfortunately, no formal quantitative methods for identifying these bridge symptoms exist. Accordingly, we developed four network statistics to identify bridge symptoms: bridge strength, bridge betweenness, bridge closeness, and bridge expected influence. These statistics are nonspecific to the type of network estimated, making them potentially useful in individual-level psychometric networks, group-level psychometric networks, and networks outside the field of psychopathology such as social networks. We first tested the fidelity of our statistics in predicting bridge nodes in a series of simulations. Averaged across all conditions, the statistics achieved a sensitivity of 92.7% and a specificity of 84.9%. By simulating datasets of varying sample sizes, we tested the robustness of our statistics, confirming their suitability for network psychometrics. Furthermore, we simulated the contagion of one mental disorder to another, showing that deactivating bridge nodes prevents the spread of comorbidity (i.e., one disorder activating another). Eliminating nodes based on bridge statistics was more effective than eliminating nodes high on traditional centrality statistics in preventing comorbidity. Finally, we applied our algorithms to 18 group-level empirical comorbidity networks from published studies and discussed the implications of this analysis.

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

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

Network Trees: A Method for Recursively Partitioning Covariance Structures.

In many areas of psychology, correlation-based network approaches (i.e., psychometric networks) have become a popular tool. In this paper, we propose an approach that recursively splits the sample based on covariates in order to detect significant differences in the structure of the covariance or correlation matrix. Psychometric networks or other correlation-based models (e.g., factor models) can be subsequently estimated from the resultant splits. We adapt model-based recursive partitioning and conditional inference tree approaches for finding covariate splits in a recursive manner. The empirical power of these approaches is studied in several simulation conditions. Examples are given using real-life data from personality and clinical research.

Age-related differences in borderline personality disorder symptom networks in a transdiagnostic sample.

BACKGROUND: Naturalistic longitudinal studies of Borderline Personality Disorder (BPD) indicate that symptoms improve over time. In the present study, we applied network theory to the question of how BPD symptom networks may differ as a function of age. METHODS: In a transdiagnostic sample of 5,212 patients presenting for acute psychiatric treatment, we administered a measure of BPD symptoms and then used a novel machine learning technique to test the hypothesis that symptom networks would significantly differ across the age of participants. RESULTS: Results supported two significant differences in the BPD symptom network that emerged at age 46. In older participants, the relationships among symptoms of non-suicidal self-injury/suicide and emptiness was weaker, yet the relationship between anger and relationship problems was stronger. No differences emerged for relationships between all other symptoms. LIMITATIONS: Given the cross-sectional nature of this study, the potential influence of cohort effects cannot be ruled out. CONCLUSIONS: These findings support the utility of network theory for elucidating potential pathways by which the relationships between symptoms of BPD may differ as a function of age in treatment-seeking individuals. In parallel, results of this study support the highly central role of strong emotions in BPD regardless of age.

Network Structure of Physical, Cognitive, and Emotional Symptoms at Preseason Baseline in Student Athletes with Attention-Deficit/ Hyperactivity Disorder.

OBJECTIVE: Preexisting attention-deficit/hyperactivity disorder (ADHD) may be a risk factor for worse outcome following sport-related concussion. We used a statistical and psychometric approach known as network analysis to examine the architecture of physical, cognitive, and emotional symptoms at preseason baseline among student athletes with ADHD. METHOD: A cohort of 44,527 adolescent student athletes completed baseline preseason testing with ImPACT® between 2009 and 2015. A subsample of athletes reporting a diagnosis of ADHD and at least one symptom were included in this study (N = 3,074; 14-18 years old, 32.7% girls). All participants completed the 22-item Post-Concussion Symptom Scale at preseason baseline. RESULTS: Student athletes reported high frequencies of difficulty concentrating (boys/girls = 50.7%/59.4%), emotional symptoms (nervousness: boys/girls = 30.2%/51.0%; irritability: boys/girls = 23.6%/34.8%; sadness: boys/girls = 21.4%/39.7%), sleep/arousal-related symptoms (trouble falling asleep: boys/girls = 39.5%/49.4%; sleeping less than usual: boys/girls = 36.2%/43.4%; and fatigue: boys/girls = 29.8%/36.4%), and headaches (boys/girls = 27.6%/39.0%) during preseason baseline testing. The most central symptoms included dizziness, which was related to multiple somatic symptoms, and increased emotionality, which was related to a cluster of emotional symptoms. Girls reported symptoms at a greater frequency than boys, and there was evidence for variance in the global strength of the symptom network across gender, but not specific intersymptom relationships. CONCLUSION: In the absence of injury, symptoms that commonly occur after concussion interact and potentially reinforce each other among student athletes with ADHD at preseason. Symptoms common in ADHD (i.e., difficulty concentrating) are not necessarily the most central within the symptom network. These findings may inform more precise interventions for athletes with ADHD and prolonged recovery following concussion.

Identifying bridge pathways between eating disorder symptoms and suicidal ideation across three samples.

People with eating disorders (ED) have elevated rates of suicidal thoughts and behaviors. We used network analysis to identify symptoms that bridge EDs and suicidality, as well as central symptoms within the network, among 3 groups of adults. Participants were either (a) clinical psychiatric outpatients without current EDs (n = 538), (b) clinical psychiatric outpatients with a lifetime suicide attempt (n = 166), or (c) people with current EDs (n = 238). Networks were jointly estimated among groups. Within the Suicide Attempt and ED groups, interoceptive deficits and pain tolerance emerged as important bridge symptoms, whereas feeling inadequate was an important bridge symptom in the Clinical Outpatient group. Within all groups, having thoughts of killing oneself and feeling inadequate were central, meaning that they were most strongly connected to all other symptoms in the networks. Further, results indicate that a similar problem-interoceptive impairment-may link ED symptoms and suicidality both within people with EDs and people with suicide attempts. Overall, these findings are consistent with the notion that EDs and suicidality may bidirectionally influence one another, possibly through shared mechanisms (e.g., interoceptive impairment) or direct pathways (e.g., suicidal ideation being strongly connected to ED symptoms), though prospective work is needed to test these possibilities. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Trigger warnings and resilience in college students: A preregistered replication and extension.

Trigger warnings notify people that content they are about to engage with may result in adverse emotional consequences. An experiment by Bellet, Jones, and McNally (2018) indicated that trigger warnings increased the extent to which trauma-naïve crowd-sourced participants see themselves and others as emotionally vulnerable to potential future traumas but did not have a significant main effect on anxiety responses to distressing literature passages. However, they did increase anxiety responses for participants who strongly believed that words can harm. In this article, we present a preregistered replication of this study in a college student sample, using Bayesian statistics to estimate the success of each effect's replication. We found strong evidence that none of the previously significant effects replicated. However, we found substantial evidence that trigger warnings' previously nonsignificant main effect of increasing anxiety responses to distressing content was genuine, albeit small. Interpretation of the findings, implications, and future directions are discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Architecture of Physical, Cognitive, and Emotional Symptoms at Preseason Baseline in Adolescent Student Athletes With a History of Mental Health Problems.

Objective: Pre-injury mental health problems are associated with greater symptom reporting following sport-related concussion. We applied a statistical and psychometric approach known as network analysis to examine the interrelationships among symptoms at baseline in adolescent student athletes with a history of mental health problems. Design: Cross-sectional study. Setting: High schools in Maine, USA. Participants: A cohort of 44,527 adolescent student athletes completed baseline preseason testing with ImPACT® between 2009 and 2015, and those with a history of mental health problems reporting at least one symptom were included (N = 2,412; 14-18 years-old, 60.1% girls). Independent Variables: Self-reported history of treatment for a psychiatric condition. Main Outcome Measures: Physical, cognitive, and emotional symptoms from the Post-Concussion Symptom Scale. Results: Student athletes reported high frequencies of emotional symptoms (nervousness: boys = 46.6%, girls = 58.3%; irritability: boys = 37.9%, girls = 46.9%; sadness: boys = 38.7%, girls = 53.2%), sleep/arousal-related symptoms (trouble falling asleep: boys = 50.4%, girls = 55.1%; sleeping less than usual: boys = 43.8%, girls = 45.2%; and fatigue: boys = 40.3%, girls = 45.2%), headaches (boys = 27.5%, girls = 41.8%), and inattention (boys = 47.8%, girls = 46.9%) before the start of the season. Although uncommonly endorsed, dizziness was the most central symptom (i.e., the symptom with the highest aggregate connectedness with different symptoms in the network), followed by feeling more emotional and feeling slowed down. Dizziness was related to physical and somatic symptoms (e.g., balance, headache, nausea, numbness/tingling) whereas increased emotionality was related to sadness, nervousness, and irritability. Feeling slowed down was connected to cognitive (e.g., fogginess, forgetfulness), and sensory symptoms (e.g., numbness/tingling, light sensitivity). There were no gender differences in the symptom network structure. Conclusions: We examined the interconnections between symptoms reported by student athletes with mental health problems at preseason baseline, identifying how physical, cognitive, and emotional symptoms interact and potentially reinforce each other in the absence of injury. These findings are a step toward informing more precise interventions for this subgroup of athletes if they are slow to recover following concussion.

Towards a precision psychiatry approach to anxiety disorders with ecological momentary assessment: the example of panic disorder.

BACKGROUND: Treatments for anxiety disorders are among the most effective in psychiatry. Yet, there is considerable room for improvement. AIM: In this paper, we discuss the value of ecological momentary assessment as a research method and clinical tool. METHODS: We begin by describing ecological momentary assessment and its advantages, including the ability to collect ecologically valid information about mental disorders, in real time, in individual patients. We then illustrate the value of this approach for anxiety disorder treatment using two patients with panic disorder who completed ecological momentary assessments for 2 weeks before and after a cognitive-behavioural therapy intervention. We focus especially on two key pieces of information provided by ecological momentary assessment data: information about symptom dynamics and information about the relationships among symptoms as they unfold over time within individual patients. PERSPECTIVE: Although considerable work is needed to further develop this methodology in the context of anxiety disorder treatment, we believe that these pieces of information may ultimately inform our understanding of how anxiety disorder treatments have their effect and how those treatments can be tailored to individual patients.

Applying network analysis to understand depression and substance use in Indian adolescents.

INTRODUCTION: Network analysis has been used to better understand relationships between depressive symptoms. Existing work has rarely examined networks of adolescents or individuals in non-western countries. METHODS: We used data from 13,035 adolescents (52.5% male; Mage=13.8) from Bihar, a low-resource state in India. Depression was measured using the Patient Health Questionnaire-9, and substance use was measured using a questionnaire adapted from the World Health Organization. We modeled a network of depressive symptoms and a network examining connections between depressive symptoms and substance use. RESULTS: The most commonly reported depressive symptoms were sleep problems, poor appetite, and low energy. In the depression network, feeling like a failure and sad mood were the most central symptoms, and somatic symptoms clustered together. To our surprise, depressive symptoms were only weakly associated with substance use. LIMITATIONS: Our study uses cross-sectional data, which are not sufficient to draw causal inferences about the relationships between symptoms. Additionally, we used an exploratory data-driven approach, and we did not pose a priori hypotheses about the relationships between symptoms. DISCUSSION: Our findings suggest that feelings like a failure and sad mood are highly central symptoms in Indian adolescents; future research may examine if these symptoms are strong targets for intervention. Sad mood has commonly been identified as a central symptom of depression in western samples, while feeling like a failure has not. We offer avenues for future research, illustrating how network analysis may enhance our ability to understand, prevent, and treat psychopathology in LMICs.