Dynamic networks of PTSD symptoms during conflict

The cross-sectional and longitudinal interconnectedness of physical, psychological and role functioning following physical trauma: A network analysis

OBJECTIVE

Surviving physical trauma can have a large impact on one's daily life. Patients are at increased risk for poor physical health, psychological complaints, and problems in role functioning - which is often experienced simultaneously. The present study explores the interconnectedness of physical, psychological, and role functioning during the first two years post-injury, both cross-sectionally and longitudinally from a network perspective.

METHODS

3785 trauma patients (Mage = 64.2 years, SDage = 18.9 years, 50.5% female) completed questionnaires on physical, psychological, and role functioning across six measurement occasions during the first two years post-injury. The Injury Severity Score (ISS) was retrieved from the local trauma registry. Mixed graphical network models and cross-lagged network models were estimated to examine which items of recovery played a central role and were mostly related to other items in cross-sectional and longitudinal networks respectively.

RESULTS

The cross-sectional networks showed especially strong interconnections between impairments of physical and role functioning and also within post-traumatic stress symptoms. The longitudinal networks extended these results by showing that pain, impaired mobility, limitations in self-care, anxiety/depressive symptoms, and several post-traumatic stress symptoms were strong predictors for impairments in functioning at later stages of recovery.

CONCLUSION

Our findings showed that impairments in physical, psychological, and role functioning experienced by trauma patients are largely intertwined across the two years following injury. Monitoring physical impairments and psychological complaints early in recovery might help to more promptly provide the best fitting aftercare for trauma patients, which can improve recovery on the long-term.

Predictors of substance use during treatment for addiction: A network analysis of ecological momentary assessment data

BACKGROUND AND AIMS

Ecological momentary assessment (EMA) studies have previously demonstrated a prospective influence of craving on substance use in the following hours. Conceptualizing substance use as a dynamic system of causal elements could provide valuable insights into the interaction of craving with other symptoms in the process of relapse. The aim of this study was to improve the understanding of these daily life dynamic inter-relationships by applying dynamic networks analyses to EMA data sets.

DESIGN, SETTING AND PARTICIPANTS

Secondary analyses were conducted on time-series data from two 2-week EMA studies. Data were collected in French outpatient addiction treatment centres. A total of 211 outpatients beginning treatment for alcohol, tobacco, cannabis, stimulants and opiate addiction took part.

MEASUREMENTS

Using mobile technologies, participants were questioned four times per day relative to substance use, craving, exposure to cues, mood, self-efficacy and pharmacological addiction treatment use. Multi-level vector auto-regression models were used to explore contemporaneous, temporal and between-subjects networks.

FINDINGS

Among the 8260 daily evaluations, the temporal network model, which depicts the lagged associations of symptoms within participants, demonstrated a unidirectional association between craving intensity at one time (T0) and primary substance use at the next assessment (T1, r = 0.1), after controlling for the effect of all other variables. A greater self-efficacy at T0 was associated with fewer cues (r = -0.04), less craving (r = -0.1) and less substance use at T1 (r = -0.07), and craving presented a negative feedback loop with self-efficacy (r = -0.09).

CONCLUSIONS

Dynamic network analyses showed that, among outpatients beginning treatment for addiction, high craving, together with low self-efficacy, appear to predict substance use more strongly than low mood or high exposure to cues.

A month in review: longitudinal dynamics between daily PTSD symptom networks, affect, and drinking behaviors in female college students

Introduction

Sexual victimization (SV) is common among college women, with approximately half of those who have experienced SV meeting criteria for posttraumatic stress disorder (PTSD) within a year. Both SV and PTSD are associated with alcohol misuse among college women, often explained by the self-medication hypothesis. Existing literature focuses on overall PTSD severity rather than potential day-to-day fluctuations in specific symptoms, which might play a crucial role in understanding alcohol misuse risk. Studies also examine only same-day or next-day associations between PTSD and drinking, neglecting the potential for longer-term changes.

Methods

This study explores the short-term longitudinal stability and time-lagged predictive dynamics of PTSD symptoms, affect, and drinking behavior among 174 female college heavy episodic drinkers over four weeks. Participants were categorized into three groups: those with a history of SV and PTSD (n = 77), women with SV but without PTSD (n = 59), and women without prior trauma history (n = 38) to be able to examine differences by trauma exposure, and PTSD. We compared the longitudinal stability of PTSD symptom networks, affect (arousal, positive affect, and negative affect), and drinking behavior across groups. Support vector regression determined which PTSD symptom networks and affect best predict drinking behavior at specific time lags within a 0-7 day range.

Results

The PTSD group showed higher longitudinal stability for PTSD symptom networks (adjusted ps <.049) and arousal (adjusted ps <.048), but lower stability for negative affect (adjusted p =.013) and drinking behavior, including alcohol cravings (adjusted p =.019) and consumption (adjusted ps =.012), compared to the comparison groups. This suggests individuals with PTSD have more stable symptoms and arousal levels but greater fluctuations in negative affect and alcohol-related behaviors. Secondary analysis revealed PTSD symptom networks optimally predicted alcohol cravings with a three-day time lag (r=.88, p <.001) and consumption with a four-day time lag (r=.82, p <.001).

Discussion

These findings challenge assumptions regarding immediate effects of PTSD and affect on drinking behavior and underscore the need for therapeutic approaches that consider longer-range effects. Future research should expand on these findings by incorporating longer-range assessments and exploring a broader range of symptom interactions.

Alone on the frontline: The first report of PTSD prevalence and risk in de-occupied Ukrainian villages

IMPORTANCE

The ongoing Russian invasion of Ukraine marks a critical juncture in a series of events posing severe threat to the health of Ukrainian citizens. While recent reports reveal higher rates of PTSD in Ukrainian refugees following Russia's invasion - data for Ukrainians remaining at the warfront is inherently difficult to access. A primarily elderly demographic, Ukrainians in previously Russian-occupied areas near the front (UPROANF) are at particular risk.

DESIGN

Data was sourced from screening questionnaires administered between March 2022 and July 2023 by mobile health clinics providing services to UPROANF.

SETTING

Previously occupied villages in Eastern and Southern Ukraine.

PARTICIPANTS

UPROANF attending clinics completed voluntary self-report surveys reporting demographics, prior health diagnoses, and PTSD symptom severity (n = 450; Meanage = 53.66; 72.0% female).

EXPOSURE

Participants were exposed to Russian occupation of Ukrainian villages.

MAIN OUTCOME AND MEASURES

The PTSD Checklist for the DSM-V (PCL-5) with recommended diagnostic threshold (i.e. 31) was utilized to assess PTSD prevalence and symptom severity. ANCOVA was used to examine hypothesized positive associations between (1) HTN and (2) loneliness and PTSD symptoms (cumulative and by symptom cluster).

RESULTS

Between 47.8% and 51.33% screened positive for PTSD. Though cumulative PTSD symptoms did not differ based on HTN diagnostic status, those with HTN reported significantly higher PTSD re-experiencing symptoms (b = 1.25, SE = 0.60, p = .046). Loneliness was significantly associated with more severe cumulative PTSD symptoms (b = 1.29, SE = 0.31, p < .001), re-experiencing (b = 0.47, SE = 0.12, p < .001), avoidance (b = .18, SE = 0.08, p = .038), and hypervigilance (b = 0.29, SE = 0.13, p = .036).

CONCLUSIONS AND RELEVANCE

PTSD prevalence was higher than other war-exposed populations. Findings highlight the urgent mental health burden among UPROANF, emphasizing the need for integrated care models addressing both trauma and physical health. Given the significance of loneliness as a risk factor, findings suggest the potential for group-based, mind-body interventions to holistically address the physical, mental, and social needs of this highly traumatized, underserved population.

Temporal network of experience sampling methodology identifies sleep disturbance as a central symptom in generalized anxiety disorder

BACKGROUND

A temporal network of generalized anxiety disorder (GAD) symptoms could provide valuable understanding of the occurrence and maintenance of GAD. We aim to obtain an exploratory conceptualization of temporal GAD network and identify the central symptom.

METHODS

A sample of participants (n = 115) with elevated GAD-7 scores (Generalized Anxiety Disorder 7-Item Questionnaire [GAD-7] ≥ 10) participated in an online daily diary study in which they reported their GAD symptoms based on DSM-5 diagnostic criteria (eight symptoms in total) for 50 consecutive days. We used a multilevel VAR model to obtain the temporal network.

RESULTS

In temporal network, a lot of lagged relationships exist among GAD symptoms and these lagged relationships are all positive. All symptoms have autocorrelations and there are also some interesting feedback loops in temporal network. Sleep disturbance has the highest Out-strength centrality.

CONCLUSIONS

This study indicates how GAD symptoms interact with each other and strengthen themselves over time, and particularly highlights the relationships between sleep disturbance and other GAD symptoms. Sleep disturbance may play an important role in the dynamic development and maintenance process of GAD. The present study may develop the knowledge of the theoretical model, diagnosis, prevention and intervention of GAD from a temporal symptoms network perspective.

Dynamic symptom associations in posttraumatic stress disorder: a network approach

Background and objective: The current study aimed to investigate the within-day symptom dynamics in PTSD patients, specifically focusing on symptoms that most predict changes in other symptoms. The study included a baseline diagnostic assessment, followed by an assessment using the experience sampling method (ESM) via a smartphone.Method: Participants answered questions related to their PTSD symptoms four times per day for 15 consecutive days (compliance rate 75%). The clinical sample consisted of 48 treatment-seeking individuals: 44 with PTSD as a primary diagnosis, and four patients with subsyndromal PTSD, all of whom had not yet begun trauma-focused treatment. The ESM assessment included the 20 items from the PTSD Checklist for DSM-5, five items from the International Trauma Questionnaire (ITQ) assessing disturbances in relationships and functional impairment, and two items from the Clinician-Administered PTSD Scale for DSM-5 assessing symptoms of depersonalization and derealization.Results: Temporal networks (prospective associations between symptoms) showed that changes in hypervigilance predicted changes in the greatest number of symptoms at the next time point. Furthermore, hypervigilance showed temporal connections with at least one additional symptom from each of the DSM-5 PTSD symptom clusters.Conclusions: Results show that the contemporaneous network (representing the relationship between given symptoms within the same assessment occasion) and the temporal network (representing prospective associations between symptoms) differ and that it is important to estimate both. Some findings from earlier research are replicated, but heterogeneity across studies remains. Future studies should include potential moderators.

Using experience sampling methodology (ESM) to improve our understanding of day-to-day intrusion frequency and related distress in survivors of trauma

BACKGROUND AND OBJECTIVES

Cognitive models of posttraumatic stress disorder (PTSD) suggest that appraisals of traumatic sequelae and subsequent distress drive the development and maintenance of PTSD. Posttraumatic research has relied heavily on macro-longitudinal designs, with weeks or months between assessments of trauma-related cognitions and symptoms. The present study uses experience sampling methodology (ESM) better understand the day-to-day experiences of trauma exposed individuals.

METHODS

One-hundred trauma exposed adults reported their posttraumatic symptoms, interpretations, and behaviours four times a day over a 10-day ESM period.

RESULTS

As anticipated, within-person fluctuations in negative appraisals of intrusions and maladaptive coping strategies (e.g., thought suppression) were significantly positively associated with intrusion frequency and related distress. In all cases, the associations for negative appraisals and maladaptive coping were stronger with intrusion related distress than intrusion frequency.

LIMITATIONS

The observed contemporaneous associations only demonstrate that variables reliably fluctuated together and cannot indicate causality.

CONCLUSIONS

The findings demonstrate that day-to-day fluctuations in trauma related perceptions and sequelae are significant and should be explored alongside broader individual differences to advance our understanding of the development, maintenance, and treatment of PTSD.

The Effects of the Processing of Positive Memories Technique on Posttrauma Affect and Cognitions Among Survivors of Trauma: Protocol for a Daily Diary Study

BACKGROUND

The Processing of Positive Memories Technique (PPMT) is a promising new treatment approach for posttraumatic stress disorder (PTSD), which involves detailed narration and processing of specific positive autobiographical memories. Indeed, preliminary case-series studies have found reductions in PTSD symptoms, negative affect, and negative cognitions among survivors of trauma who have received PPMT. However, PPMT's effects have not been investigated at the daily level. In this study, we describe the protocol for a study that will examine the daily-level impacts of PPMT in a trauma-exposed, nonclinical community sample.

OBJECTIVE

This study uses an innovative research protocol that combines case-series design and daily diary approaches to examine changes in daily affect, daily cognitions, and daily PTSD symptoms pre- and post-PPMT. We hypothesize that at the daily level, in comparison to their own pre-PPMT levels, following the PPMT intervention, participants will report (1) a lower count of endorsed daily PTSD symptoms, (2) increases in daily positive affect and decreases in daily negative affect, (3) increases in positive affect reactivity to daily positive events, and (4) decreases in daily posttrauma cognitions.

METHODS

We are currently recruiting participants (target n=70) from a metroplex in the southwest United States. Following a screening survey, eligible participants complete a preintervention baseline survey, followed by 21 daily surveys in their natural environments. Then, they receive 4 PPMT sessions on a weekly basis. After the conclusion of the PPMT intervention, participants complete a postintervention outcome survey and 21 daily surveys. To compare daily affect, daily cognitions, and daily PTSD symptoms before and after PPMT, we will use the daily diary report data and conduct multilevel random intercepts and slopes linear regression models.

RESULTS

Data collection was initiated in March 2022 and is expected to end by June 2024. As of November 28, 2023, a total of 515 participants had consented to the study in the screening phase. No analyses will be conducted until data collection has been completed.

CONCLUSIONS

Study findings could clarify whether deficits in positive autobiographical memory processes may also characterize PTSD alongside deficits in traumatic memory processes. Furthermore, PPMT could be an additional therapeutic tool for clinicians to help clients reduce posttraumatic distress in their everyday lives.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/51838.

Longitudinal PTSD network structure: measuring PTSD symptom networks over 5 years

BACKGROUND

Network modeling has been applied in a range of trauma-exposed samples, yet results are limited by an over reliance on cross-sectional data. The current analyses used posttraumatic stress disorder (PTSD) symptom data collected over a 5-year period to estimate a more robust between-subject network and an associated symptom change network.

METHODS

A PTSD symptom network is measured in a sample of military veterans across four time points (Ns = 1254, 1231, 1106, 925). The repeated measures permit isolating between-subject associations by limiting the effects of within-subject variability. The result is a highly reliable PTSD symptom network. A symptom slope network depicting covariation of symptom change over time is also estimated.

RESULTS

Negative trauma-related emotions had particularly strong associations with the network. Trauma-related amnesia, sleep disturbance, and self-destructive behavior had weaker overall associations with other PTSD symptoms.

CONCLUSIONS

PTSD's network structure appears stable over time. There is no single 'most important' node or node cluster. The relevance of self-destructive behavior, sleep disturbance, and trauma-related amnesia to the PTSD construct may deserve additional consideration.

Development and validation of a brief screener for posttraumatic stress disorder risk in emergency medical settings

OBJECTIVE

Predicting risk of posttraumatic stress disorder (PTSD) in the acute care setting is challenging given the pace and acute care demands in the emergency department (ED) and the infeasibility of using time-consuming assessments. Currently, no accurate brief screening for long-term PTSD risk is routinely used in the ED. One instrument widely used in the ED is the 27-item Immediate Stress Reaction Checklist (ISRC). The aim of this study was to develop a short screener using a machine learning approach and to investigate whether accurate PTSD prediction in the ED can be achieved with substantially fewer items than the IRSC.

METHOD

This prospective longitudinal cohort study examined the development and validation of a brief screening instrument in two independent samples, a model development sample (N = 253) and an external validation sample (N = 93). We used a feature selection algorithm to identify a minimal subset of features of the ISRC and tested this subset in a predictive model to investigate if we can accurately predict long-term PTSD outcomes.

RESULTS

We were able to identify a reduced subset of 5 highly predictive features of the ISRC in the model development sample (AUC = 0.80), and we were able to validate those findings in the external validation sample (AUC = 0.84) to discriminate non-remitting vs. resilient trajectories.

CONCLUSION

This study developed and validated a brief 5-item screener in the ED setting, which may help to improve the diagnostic process of PTSD in the acute care setting and help ED clinicians plan follow-up care when patients are still in contact with the healthcare system. This could reduce the burden on patients and decrease the risk of chronic PTSD.

Investigating sleep, stress, and mood dynamics via temporal network analysis

OBJECTIVE/BACKGROUND

Prior research has emphasized the bidirectional relationships between sleep, stress, and affective states, such as depression. Given the inherent variability and fluctuations associated with sleep, assessing how sleep and affective variables function within a dynamic system may help further uncover possible causes and consequences of sleep disturbances, as well as find candidate targets for intervention. To this end, we examined dynamic relationships between self-reported stress, depressed mood, and clinically-relevant sleep parameters via temporal network analysis.

METHODS

Participants were 401 nurses (92% female, 78% White, Mage = 39.47 years) who completed 14 days of sleep diaries incorporating self-reported stress and depression, as well as total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset.

RESULTS AND CONCLUSIONS

Overall, total sleep time emerged as a highly influential variable in the context of "outstrength centrality," meaning total sleep time had numerous outward connections with other variables (e.g., stress and sleep efficiency). The high outstrength centrality of total sleep time suggests this variable is a source of activation within this dynamic system. Conversely, stress showed high "instrength centrality," suggesting this variable was highly impacted by other variables in the system, such as depressed mood and sleep efficiency. These findings emphasize the importance of assessing unfolding sleep processes within a naturalistic setting, and implicate the role of total sleep time in fueling depressed mood and stress. Discussion emphasizes implications of these results for understanding the connections between sleep, stress, and depression as well as clinical relevance of these findings.

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

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

PTSD Symptom dynamics after the great east japan earthquake: mapping the temporal structure using Dynamic Time Warping

Background: After the Great East Japan Earthquake [GEJE], approximately 70,000 Japan Ground Self Defense Force [JGSDF] personnel were deployed, risking Post-Traumatic Stress Disorder [PTSD]. The network approach to psychopathology suggests that symptoms may cause and exacerbate each other, resulting in the emergence and maintenance of disorders, including PTSD. It is therefore important to further explore the temporal interplay between symptoms. Most studies assessing the factor structure of the Impact of Event Scale-Revised [IES-R] have used cross-sectional designs. In this study, the structure of the IES-R was re-evaluated while incorporating the temporal interplay between symptoms.Methods: Using Dynamic Time Warping [DTW] the distances between PTSD symptoms on the IES-R were modelled in 1120 JGSDF personnel. Highly correlated symptoms were clustered at the group level using Distatis three-way principal component analyses of the distance matrices. The resulting clusters were compared to the original three subscales of the IES-R using a Confirmatory Factor Analysis (CFA).Results: The DTW analysis yielded four symptom clusters: Intrusion (five items), Hyperarousal (six items), Avoidance (six items), and Dissociation (five items). CFA yielded better fit estimates for this four-factor solution (RMSEA = 0.084, CFI = 0.918, TLI = 0.906), compared to the original three subscales of the IES-R (RMSEA = 0.103, CFI = 0.873, TLI = 0.858).Conclusions: DTW offers a new method of modelling the temporal relationships between symptoms. It yielded four IES-R symptom clusters, which may facilitate understanding of PTSD as a complex dynamic system.

Dynamic networks of psychotic symptoms in adults living in precarious housing or homelessness

BACKGROUND

People living in precarious housing or homelessness have higher than expected rates of psychotic disorders, persistent psychotic symptoms, and premature mortality. Psychotic symptoms can be modeled as a complex dynamic system, allowing assessment of roles for risk factors in symptom development, persistence, and contribution to premature mortality.

METHOD

The severity of delusions, conceptual disorganization, hallucinations, suspiciousness, and unusual thought content was rated monthly over 5 years in a community sample of precariously housed/homeless adults (n = 375) in Vancouver, Canada. Multilevel vector auto-regression analysis was used to construct temporal, contemporaneous, and between-person symptom networks. Network measures were compared between participants with (n = 219) or without (n = 156) history of psychotic disorder using bootstrap and permutation analyses. Relationships between network connectivity and risk factors including homelessness, trauma, and substance dependence were estimated by multiple linear regression. The contribution of network measures to premature mortality was estimated by Cox proportional hazard models.

RESULTS

Delusions and unusual thought content were central symptoms in the multilevel network. Each psychotic symptom was positively reinforcing over time, an effect most pronounced in participants with a history of psychotic disorder. Global connectivity was similar between those with and without such a history. Greater connectivity between symptoms was associated with methamphetamine dependence and past trauma exposure. Auto-regressive connectivity was associated with premature mortality in participants under age 55.

CONCLUSIONS

Past and current experiences contribute to the severity and dynamic relationships between psychotic symptoms. Interrupting the self-perpetuating severity of psychotic symptoms in a vulnerable group of people could contribute to reducing premature mortality.

Recovering Within-Person Dynamics from Psychological Time Series

Idiographic modeling is rapidly gaining popularity, promising to tap into the within-person dynamics underlying psychological phenomena. To gain theoretical understanding of these dynamics, we need to make inferences from time series models about the underlying system. Such inferences are subject to two challenges: first, time series models will arguably always be misspecified, meaning it is unclear how to make inferences to the underlying system; and second, the sampling frequency must be sufficient to capture the dynamics of interest. We discuss both problems with the following approach: we specify a toy model for emotion dynamics as the true system, generate time series data from it, and then try to recover that system with the most popular time series analysis tools. We show that making straightforward inferences from time series models about an underlying system is difficult. We also show that if the sampling frequency is insufficient, the dynamics of interest cannot be recovered. However, we also show that global characteristics of the system can be recovered reliably. We conclude by discussing the consequences of our findings for idiographic modeling and suggest a modeling methodology that goes beyond fitting time series models alone and puts formal theories at the center of theory development.

Transdiagnostic Psychopathology in a Help-Seeking Population of an Early Recognition Center for Mental Disorders: Protocol for an Experience Sampling Study

BACKGROUND

Prevention in psychiatry provides a promising way to address the burden of mental illness. However, established approaches focus on specific diagnoses and do not address the heterogeneity and manifold potential outcomes of help-seeking populations that present at early recognition services. Conceptualizing the psychopathology manifested in help-seeking populations from a network perspective of interacting symptoms allows transdiagnostic investigations beyond binary disease categories. Furthermore, modern technologies such as smartphones facilitate the application of the Experience Sampling Method (ESM).

OBJECTIVE

This study is a combination of ESM with network analyses to provide valid insights beyond the established assessment instruments in a help-seeking population.

METHODS

We will examine 75 individuals (aged 18-40 years) of the help-seeking population of the Cologne early recognition center. For a maximally naturalistic sample, only minimal exclusion criteria will be applied. We will collect data for 14 days using a mobile app to assess 10 transdiagnostic symptoms (ie, depressive, anxious, and psychotic symptoms) as well as distress level 5 times a day. With these data, we will generate average group-level symptom networks and personalized symptom networks using a 2-step multilevel vector autoregressive model. Additionally, we will explore associations between symptom networks and sociodemographic, risk, and resilience factors, as well as psychosocial functioning.

RESULTS

The protocol was designed in February 2020 and approved by the Ethics Committee of the University Hospital Cologne in October 2020. The protocol was reviewed and funded by the Köln Fortune program in September 2020. Data collection began in November 2020 and was completed in November 2021. Of the 258 participants who were screened, 93 (36%) fulfilled the inclusion criteria and were willing to participate in the study. Of these 93 participants, 86 (92%) completed the study. The first results are expected to be published in 2022.

CONCLUSIONS

This study will provide insights about the feasibility and utility of the ESM in a help-seeking population of an early recognition center. Providing the first explorative phenotyping of transdiagnostic psychopathology in this population, our study will contribute to the innovation of early recognition in psychiatry. The results will help pave the way for prevention and targeted early intervention in a broader patient group, and thus, enable greater intended effects in alleviating the burden of psychiatric disorders.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/35206.

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

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

The memory-experience gap for PTSD symptoms: The correspondence between experience sampling and past month retrospective reports of traumatic stress symptoms

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.

Gaming to cope: Applying network analysis to understand the relationship between posttraumatic stress symptoms and internet gaming disorder symptoms among disaster-exposed Chinese young adults

Research has demonstrated that posttraumatic stress disorder (PTSD) is associated with internet-related problematic behaviors. However, studies have not explored the linkage between PTSD symptoms and internet gaming disorder (IGD) symptoms. The current study aimed to investigate the relationship between posttraumatic stress symptoms (PTSS) and IGD symptoms via network analysis. We conducted a cross-sectional study with 341 Chinese young adults directly exposed to a typhoon and examined the network structure of PTSS and IGD symptoms, along with bridge symptoms, to elucidate how they co-occur. Results indicated that 'avoiding external reminders' and 'anhedonia' were identified as the most central symptoms in the PTSD network, whereas 'preoccupation,' 'gaming despite harms', and 'loss of control' ranked highest on centrality in the IGD network. Two bridge symptoms emerged within the combined PTSD and IGD network model: 'concentration difficulties' and 'conflict due to gaming' from among the PTSS and IGD symptoms, respectively. These findings reveal novel associations between PTSS and IGD symptoms and provide an empirically-based hypothesis for how these two disorders may co-occur among individuals exposed to natural disasters.

Do positive and negative emotional reactions during war predict subsequent symptomatology? A prospective experience sampling study

While peritraumatic negative emotions have been associated with subsequent posttraumatic stress and depression, the predictive role of real-time emotional reactions to specific stressors during prolonged stress exposure is still unclear, particularly that of positive emotions. The current study uses experience sampling methodology to examine individual general levels of negative and positive emotions, and emotional reactivity to specific stressors during war, as prospective predictors of posttraumatic stress and depression. Ninety-six civilians exposed to rocket fire during the 2014 Israel-Gaza war reported exposure to rocket warning sirens and levels of ten negative and six positive emotions twice a day for 30 days. Symptoms of posttraumatic stress and depression were then assessed two months post-war. Participants reported higher negative emotions and lower positive emotions during assessment windows with sirens. Over time, negative emotions decreased and positive emotions increased. Higher levels of overall negative emotions predicted posttraumatic stress symptoms and depression symptoms two months later. Levels of positive emotions, and negative and positive emotional reactivity to sirens, were not associated with subsequent symptomatology. Our results indicate the stronger role of overall negative emotions as predictors of symptomatology compared with momentary emotional reactivity, and the stronger predictive role of negative compared with positive emotions.

Cross-cultural investigation of COVID-19 related acute stress: A network analysis

The outbreak of the COVID-19 pandemic has confronted humanity with an ongoing biopsychosocial stressor, imposing multifaceted challenges to individuals and societies. Particularly, the pandemic reflects an ongoing, potentially life-threatening danger to self and others, which may instigate acute stress symptoms (ASS). This study utilized a network framework to assess cross-national ASS a short time following the initial COVID-19 outbreak. Three samples of adult participants from China, Israel, and Switzerland completed a self-report assessment of acute stress symptoms. Network analyses were utilized to uncover the phenotype and dynamics of different ASS in these three countries. The ASS network analyses revealed extensive connections in all networks and reflected the structure of ASS. The centrality indexes in all networks were from the hyperarousal cluster. "Feeling jumpy" was the node with the highest strength centrality in the Israeli sample and "physiological reactivity" was the item with the highest centrality in the Swiss sample. In the Chinese sample, the item with the highest centrality was "feeling alert to danger." The findings reveal that despite some variations, the overall clinical picture of ASS in response to the COVID-19 pandemic is universal. These findings highlight the centrality of hyperarousal symptoms, presumably reflecting its significance for clinical interventions.

Connecting the dots: A network approach to post‐traumatic stress symptoms in Chinese healthcare workers during the peak of the Coronavirus Disease 2019 outbreak

Healthcare workers are at elevated risk to develop symptoms of post-traumatic stress disorder (PTSD) in response to an outbreak of a highly infectious disease. The current study set-out to model the complex interrelations between PTSD symptoms during the peak of the Coronavirus Disease 2019 outbreak in 291 Chinese healthcare workers and 291 matched control cases that were selected from the general population. For this purpose, we estimated regularized partial correlation networks. Within the network of healthcare workers, we observed a central role for avoidance of reminders of the traumatic event, physiological cue reactivity, anger/irritability, re-experiencing, and startle. We identified three clusters of closely interconnected PTSD symptoms in healthcare workers, consisting of (a) symptoms of re-experiencing and anxious arousal, (b) symptoms of avoidance and amnesia and (c) symptoms of emotional numbing and dysphoric arousal. Respectively, startle, avoidance of reminders and feeling detached emerged as bridging nodes in these communities. Although yielding highly similar network models, the PTSD symptom structure of healthcare workers showed several unique features compared to the matched control sample. This is informative for interventions aimed at targeting PTSD symptoms in healthcare workers in the context of a public health emergency.

Consolidation/reconsolidation therapies for the prevention and treatment of PTSD and re-experiencing: a systematic review and meta-analysis

Translational research highlights the potential of novel 'memory consolidation/reconsolidation therapies' to treat re-experiencing symptoms and post-traumatic stress disorder (PTSD). This systematic review and meta-analysis assessed the efficacy of so-called memory consolidation/reconsolidation therapies in randomised controlled trials (RCTs) for prevention and treatment of PTSD and symptoms of re-experiencing in children and adults (PROSPERO: CRD42020171167). RCTs were identified and rated for risk of bias. Available data was pooled to calculate risk ratios (RR) for PTSD prevalence and standardised mean differences (SMD) for PTSD/re-experiencing severity. Twenty-five RCTs met inclusion criteria (16 prevention and nine treatment trials). The methodology of most studies had a significant risk of bias. We found a large effect of reconsolidation interventions in the treatment of PTSD (11 studies, n = 372, SMD: -1.42 (-2.25 to -0.58), and a smaller positive effect of consolidation interventions in the prevention of PTSD (12 studies, n = 2821, RR: 0.67 (0.50 to 0.90). Only three protocols (hydrocortisone for PTSD prevention, Reconsolidation of Traumatic Memories (RTM) for treatment of PTSD symptoms and cognitive task memory interference procedure with memory reactivation (MR) for intrusive memories) were superior to control. There is some emerging evidence of consolidation and reconsolidation therapies in the prevention and treatment of PTSD and intrusive memories specifically. Translational research should strictly adhere to protocols/procedures describing precise reconsolidation conditions (e.g. MR) to both increase the likelihood of positive findings and more confidently interpret negative findings of putative reconsolidation agents.

Clustering Individuals on Limited Features of a Vector Autoregressive Model

Dynamical interplays in emotions have been investigated using vector autoregressive (VAR) models, whose estimates can be used to cluster participants into unknown groups. The present study evaluated a clustering algorithm, the alternating least square (ALS) algorithm, for accuracy in predicting individual group membership. We systematically manipulated (a) the number of variables in a model, (b) the size of group differences in regression coefficients, and (c) the number of regression coefficients that vary across the groups (i.e., effective features). The ALS algorithm works reliably when there are at least 5 effective features with very large group differences in a 5-variable model; and 9 effective features with very large group differences in a 10-variable model. These findings suggest that the ALS algorithm is sensitive to group differences that are present only in several coefficients of a VAR model, but that the group differences have to be large. We also found that the ALS algorithm outperforms another clustering method, Gaussian mixture modeling. The ALS algorithm was further evaluated with unbalanced sample sizes between groups and with a greater number of groups in data (Study 2). A real data application was provided to illustrate how to interpret the detected group differences (Study 3).

A Network Analysis of the Associations Between Posttraumatic Stress Symptoms and Posttraumatic Growth Among Disaster-Exposed Chinese Young Adults

Posttraumatic stress symptoms (PTSS) and posttraumatic growth (PTG) have been shown to coexist following exposure to a traumatic event, but consensus about what accounts for this association is lacking. Network analysis is a novel analytic method that can explain this linkage. In a sample of 1,809 Chinese college students (66.1% female, age range: 16-35 years) who were directly exposed to a typhoon, we investigated the network structure of PTSS and PTG, along with bridge symptoms and elements, to elucidate how distress and growth coexist. The seven strongest edges found in the model included two between elements in the PTSS cluster, one between elements of PTG, and four between elements of PTSS and PTG. Eight bridge symptoms and elements emerged: intrusive thoughts, emotional cue reactivity, hypervigilance, self-destructive or reckless behavior, nightmares, and physiological cue reactivity among PTSS, and changed priorities and stronger religious faith among PTG elements. These findings reveal connections between PTSS and PTG that explain how these constructs may coexist in individuals exposed to natural disasters. The network perspective provides a novel way to conceptualize the association between PTSS and PTG and contributes to the field's understanding of recovery after traumatic events.

Posttraumatic stress disorder in daily life among World Trade Center responders: Temporal symptom cascades

BACKGROUND

Posttraumatic stress disorder (PTSD) symptoms are common in the immediate aftermath of a trauma, but it is their persistence over time that leads to a diagnosis. This pattern highlights the critical role of symptom maintenance to understanding and treating the disorder. Relatively few studies have explored whether PTSD symptoms may be interacting or triggering one another to worsen and maintain the disorder, a dynamic we refer to as "symptom cascades." Additionally, little work has tested in real-time how other maintenance factors, such as stress, contribute to such events in daily life.

METHODS

The present study in a group (N = 202) of World Trade Center (WTC) responders oversampled for PTSD tested day-to-day temporal associations among PTSD symptom dimensions (i.e., intrusions, avoidance, numbing, and hyperarousal) and stress across one week.

RESULTS

Longitudinal models found hyperarousal on a given day predicted increased PTSD symptoms the next day, with the effect sizes almost double compared to other symptom dimensions or daily stress. Intrusions, in contrast, showed little prospective predictive effects, but instead were most susceptible to the effects from other symptoms the day before. Avoidance and numbing showed weaker bidirectional effects.

LIMITATIONS

Findings are from a unique population and based on naturalistic observation.

CONCLUSIONS

Results are consistent with the idea of symptom cascades, they underscore hyperarousal's strong role in forecasting short-term increases in PTSD (even more than stress per se) and they raise the prospect of highly specific ecological momentary interventions to potentially disrupt PTSD maintenance in daily life.

Network analysis of PTSD in college students across different areas after the COVID-19 epidemic

Background: Various studies have examined the psychological 'typhoon eye' and 'ripple' effects in mental disorders following COVID-19. However, these studies only considered the disorders as entities and assessed severity, and overlooked the differences in specific symptoms of disorders. Objectives: This aim of the study is to assess the psychological typhoon eye and ripple effects at the symptom-level in posttraumatic stress disorder (PTSD), which is considered as a common psychopathology following the COVID-19 epidemic. Method: In total, 1150 undergraduates, including 271 students from the Hubei province (e.g. epidemic centre) and 879 students from other provinces, completed the self-report questionnaire. The networks were estimated and compared using the R packages. Results: Although the PTSD networks of Hubei and non-Hubei undergraduates were similarly connected and shared some symptoms with high centrality (e.g. flashbacks, irritability and anger), there were differences across the networks. Distorted cognition and no positive emotion only exhibited high centrality in the Hubei network. Physiological responses and exaggerated startle only exhibited high centrality in the non-Hubei network. Conclusion: These findings suggested that the psychological typhoon eye and ripple effects may co-exist at the symptom level. Targeted and distinct psychological services for college students in Hubei and non-Hubei provinces should be emphasized following COVID-19.

Disturbed Sleep Connects Symptoms of Posttraumatic Stress Disorder and Somatization: A Network Analysis Approach

Posttraumatic stress disorder (PTSD) and physical health problems, particularly somatic symptom disorder, are highly comorbid. Studies have only examined this co-occurrence at the disorder level rather than assessing the associations between specific symptoms. Using network analysis to identify symptoms that act as bridges between these disorders may allow for the development of interventions to specifically target this comorbidity. We examined the association between somatization and PTSD symptoms via network analysis. This included 349 trauma-exposed individuals recruited through the National Centre for Mental Health PTSD cohort who completed the Clinician-Administered PTSD Scale for DSM-5 and the Patient Health Questionnaire-15. A total of 215 (61.6%) individuals met the DSM-5 diagnostic criteria for PTSD. An exploratory graph analysis identified four clusters of densely connected symptoms within the overall network: PTSD, chronic pain, gastrointestinal issues, and more general somatic complaints. Sleep difficulties played a key role in bridging PTSD and somatic symptoms. Our network analysis demonstrates the distinct nature of PTSD and somatization symptoms, with this association connected by disturbed sleep.

Person-specific networks in psychopathology: Past, present, and future

In the psychological network approach, mental disorders such as major depressive disorder are conceptualized as networks. The network approach focuses on the symptom structure or the connections between symptoms instead of the severity (i.e., mean level) of a symptom. To infer a person-specific network for a patient, time-series data are needed. By far the most common model to statistically model the person-specific interactions between symptoms or momentary states has been the vector autoregressive (VAR) model. Although the VAR model helps to bring psychological network theory into clinical research and closer to clinical practice, several discrepancies arise when we map the psychological network theory onto the VAR-based network models. These challenges and possible solutions are discussed in this review.

Assessing Change in Intervention Research: The Benefits of Composite Outcomes

Intervention research is often time- and resource-intensive, with numerous participants involved over extended periods of time. To maximize the value of intervention studies, multiple outcome measures are often included, either to ensure a diverse set of outcomes is being assessed or to refine assessments of specific outcomes. Here, we advocate for combining assessments, rather than relying on individual measures assessed separately, to better evaluate the effectiveness of interventions. Specifically, we argue that by pooling information from individual measures into a single outcome, composite scores can provide finer estimates of the underlying theoretical construct of interest while retaining important properties more sophisticated methods often forgo, such as transparency and interpretability. We describe different methods to compute, evaluate, and use composites depending on the goals, design, and data. To promote usability, we also provide a preregistration template that includes examples in the context of psychological interventions with supporting R code. Finally, we make a number of recommendations to help ensure that intervention studies are designed in a way that maximizes discoveries. A Shiny app and detailed R code accompany this article and are available at https://osf.io/u96em/ .

Latent cause inference during extinction learning in trauma-exposed individuals with and without PTSD

BACKGROUND

Problems in learning that sights, sounds, or situations that were once associated with danger have become safe (extinction learning) may explain why some individuals suffer prolonged psychological distress following traumatic experiences. Although simple learning models have been unable to provide a convincing account of why this learning fails, it has recently been proposed that this may be explained by individual differences in beliefs about the causal structure of the environment.

METHODS

Here, we tested two competing hypotheses as to how differences in causal inference might be related to trauma-related psychopathology, using extinction learning data collected from clinically well-characterised individuals with varying degrees of post-traumatic stress (N = 56). Model parameters describing individual differences in causal inference were related to multiple post-traumatic stress disorder (PTSD) and depression symptom dimensions via network analysis.

RESULTS

Individuals with more severe PTSD were more likely to assign observations from conditioning and extinction stages to a single underlying cause. Specifically, greater re-experiencing symptom severity was associated with a lower likelihood of inferring that multiple causes were active in the environment.

CONCLUSIONS

We interpret these results as providing evidence of a primary deficit in discriminative learning in participants with more severe PTSD. Specifically, a tendency to attribute a greater diversity of stimulus configurations to the same underlying cause resulted in greater uncertainty about stimulus-outcome associations, impeding learning both that certain stimuli were safe, and that certain stimuli were no longer dangerous. In the future, better understanding of the role of causal inference in trauma-related psychopathology may help refine cognitive therapies for these disorders.

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.

Emotional reactivity to war stressors: An experience sampling study in people with and without different psychiatric diagnoses

There is a lack of knowledge regarding real-time emotional reactivity to high-intensity stressors, particularly in people with mental illness, a potentially vulnerable population. The current study aimed to examine negative emotional reactions to recurring high-intensity stressors within a continuous war situation, in people with different psychiatric diagnosis types. Experience sampling method was used to examine emotional reactions among 143 civilians exposed to rockets during the 2014 Israel-Gaza war, of them 18.2% with psychosis, 14.7% with anxiety or depression and 67.1% without mental illness. Participants reported exposure to rocket warning sirens and the levels of 10 negative emotions twice a day for 30 days. Negative emotional levels were higher on most emotions following high-intensity stressors (sirens), that is, emotional reactivity was demonstrated in real-time during war. Overall, no difference in reactivity was found among the three study groups. Moreover, people with anxiety/depression were less reactive than people without mental illness on sadness and being overwhelmed. The findings indicate similar and sometimes lower emotional reactivity to high-intensity stressors in people with mental illness compared to the general population. Nevertheless, people with mental illness seem to have significant emotional needs during war, to be addressed in prevention and intervention efforts.

Forecasting individual risk for long-term Posttraumatic Stress Disorder in emergency medical settings using biomedical data: A machine learning multicenter cohort study

The necessary requirement of a traumatic event preceding the development of Posttraumatic Stress Disorder, theoretically allows for administering preventive and early interventions in the early aftermath of such events. Machine learning models including biomedical data to forecast PTSD outcome after trauma are highly promising for detection of individuals most in need of such interventions. In the current study, machine learning was applied on biomedical data collected within 48 h post-trauma to forecast individual risk for long-term PTSD, using a multinominal approach including the full spectrum of common PTSD symptom courses within one prognostic model for the first time. N = 417 patients (37.2% females; mean age 46.09 ± 15.88) admitted with (suspected) serious injury to two urban Academic Level-1 Trauma Centers were included. Routinely collected biomedical information (endocrine measures, vital signs, pharmacotherapy, demographics, injury and trauma characteristics) upon ED admission and subsequent 48 h was used. Cross-validated multi-nominal classification of longitudinal self-reported symptom severity (IES-R) over 12 months and bimodal classification of clinician-rated PTSD diagnosis (CAPS-IV) at 12 months post-trauma was performed using extreme Gradient Boosting and evaluated on hold-out sets. SHapley Additive exPlanations (SHAP) values were used to explain the derived models in human-interpretable form. Good prediction of longitudinal PTSD symptom trajectories (multiclass AUC = 0.89) and clinician-rated PTSD at 12 months (AUC = 0.89) was achieved. Most relevant prognostic variables to forecast both multinominal and dichotomous PTSD outcomes included acute endocrine and psychophysiological measures and hospital-prescribed pharmacotherapy. Thus, individual risk for long-term PTSD was accurately forecasted from biomedical information routinely collected within 48 h post-trauma. These results facilitate future targeted preventive interventions by enabling future early risk detection and provide further insights into the complex etiology of PTSD.

Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients

There is growing concern that the social and physical distancing measures implemented in response to the Covid-19 pandemic may negatively impact health in other areas, via both decreased physical activity and increased social isolation. Here, we investigated whether increased engagement with digital social tools may help mitigate effects of enforced isolation on physical activity and mood, in a naturalistic study of at-risk individuals. Passively sensed smartphone app use and actigraphy data were collected from a group of psychiatric outpatients before and during imposition of strict Covid-19 lockdown measures. Data were analysed using Gaussian graphical models: a form of network analysis which gives insight into the predictive relationships between measures across timepoints. Within-individuals, we found evidence of a positive predictive path between digital social engagement, general smartphone use, and physical activity-selectively under lockdown conditions (N = 127 individual users, M = 6201 daily observations). Further, we observed a positive relationship between social media use and total daily steps across individuals during (but not prior to) lockdown. Although there are important limitations on the validity of drawing causal conclusions from observational data, a plausible explanation for our findings is that, during lockdown, individuals use their smartphones to access social support, which may help guard against negative effects of in-person social deprivation and other pandemic-related stress. Importantly, passive monitoring of smartphone app usage is low burden and non-intrusive. Given appropriate consent, this could help identify people who are failing to engage in usual patterns of digital social interaction, providing a route to early intervention.

Why we should move from reductionism and embrace a network approach to parental burnout

Network science has allowed varied scientific fields to investigate and visualize complex relations between many variables, and psychology research has begun to adopt a network perspective. In this paper, we consider how leaving behind reductionist approaches and instead embracing a network perspective can advance the field of parental burnout. Although research into parental burnout is in its early stages, we argue that a network approach to parental burnout could set the scene for radically new vistas in parental burnout research. We claim that such an approach can allow simultaneous investigations (and clear visualizations) of many variables related to parental burnout and their interactions, integrates smoothly with prior family systems theories, and prioritizes dynamic research questions. We likewise discuss potential future clinical applications, such as interventions targeting central nodes and treatment personalized to a specific family's network system. We also review practical considerations, limitations, and future directions for researchers interested in applying a network approach to parental burnout research.

On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology

BACKGROUND

In the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms' causal influence within a network. Accordingly, centrality measures have been used in numerous network-based cross-sectional studies to identify specific treatment targets, based on the assumption that deactivating highly central nodes would proliferate to other nodes in the network, thereby collapsing the network structure and alleviating the overall psychopathology (i.e., the centrality hypothesis).

METHODS

Here, we summarize three types of evidence pertaining to the centrality hypothesis in psychopathology. First, we discuss the validity of the theoretical assumptions underlying the centrality hypothesis in psychopathology. We then summarize the methodological aspects of extant studies using centrality measures as predictors of symptom change following treatment, while delineating their main findings and several of their limitations. Finally, using a specific dataset of 710 treatment-seeking patients with posttraumatic stress disorder (PTSD) as an example, we empirically examine node centrality as a predictor of therapeutic change, replicating the approach taken by previous studies, while addressing some of their limitations. Specifically, we investigated whether three pre-treatment centrality indices (strength, predictability, and expected influence) were significantly correlated with the strength of the association between a symptom's change and the change in the severity of all other symptoms in the network from pre- to post-treatment (Δnode-Δnetwork association). Using similar analyses, we also examine the predictive validity of two simple non-causal node properties (mean symptom severity and infrequency of symptom endorsement).

RESULTS

Of the three centrality measures, only expected influence successfully predicted how strongly changes in nodes/symptoms were associated with change in the remainder of the nodes/symptoms. Importantly, when excluding the amnesia node, a well-documented outlier in the phenomenology of PTSD, none of the tested centrality measures predicted symptom change. Conversely, both mean symptom severity and infrequency of symptom endorsement, two standard non-network-derived indices, were found to be more predictive than expected influence and remained significantly predictive also after excluding amnesia from the network analyses.

CONCLUSIONS

The centrality hypothesis in its current form is ill-defined, showing no consistent supporting evidence in the context of cross-sectional, between-subject networks.

Symptom structure of PTSD and co-morbid depressive symptoms - a network analysis of combat veteran patients

BACKGROUND

Despite extensive research, symptom structure of posttraumatic stress disorder (PTSD) is highly debated. The network approach to psychopathology offers a novel method for understanding and conceptualizing PTSD. However, extant studies have mainly used small samples and self-report measures among sub-clinical populations, while also overlooking co-morbid depressive symptoms.

METHODS

PTSD symptom network topology was estimated in a sample of 1489 treatment-seeking veteran patients based on a clinician-rated PTSD measure. Next, clinician-rated depressive symptoms were incorporated into the network to assess their influence on PTSD network structure. The PTSD-symptom network was then contrasted with the network of 306 trauma-exposed (TE) treatment-seeking patients not meeting full criteria for PTSD to assess corresponding network differences. Finally, a directed acyclic graph (DAG) was computed to estimate potential directionality among symptoms, including depressive symptoms and daily functioning.

RESULTS

The PTSD symptom network evidenced robust reliability. Flashbacks and getting emotionally upset by trauma reminders emerged as the most central nodes in the PTSD network, regardless of the inclusion of depressive symptoms. Distinct clustering emerged for PTSD and depressive symptoms within the comorbidity network. DAG analysis suggested a key triggering role for re-experiencing symptoms. Network topology in the PTSD sample was significantly distinct from that of the TE sample.

CONCLUSIONS

Flashbacks and psychological reactions to trauma reminders, along with their strong connections to other re-experiencing symptoms, have a pivotal role in the clinical presentation of combat-related PTSD among veterans. Depressive and posttraumatic symptoms constitute two separate diagnostic entities, but with meaningful between-disorder connections, suggesting two mutually-influential systems.

A Temporal Network Approach to Paranoia: A Pilot Study

Paranoid beliefs have been conceptualized as a central psychological process linked to schizophrenia and many mental disorders. Research on paranoia has indicated that it is pivotal to consider not only levels but also dynamic aspects of incriminated related mechanisms over time. In the present study, we conceptualized paranoia as a system of interacting elements. To do so, we used temporal network analysis to unfold the temporal dynamics between core psychological paranoia-related mechanisms, such as self-esteem, sadness, feeling close to others, and experiential avoidance. Time-series data of 23 participants with high scores in paranoia and/or interpersonal sensitivity were collected via experience sampling methodology (ESM). We applied a multilevel vector autoregressive (mlVAR) model approach and computed three distinct and complementary network models (i.e., contemporaneous, temporal, and between-subject) to disentangle associations between paranoia-related mechanisms in three different time frames. The contemporaneous model indicated that paranoia and sadness co-occurred within the same time frame, while sadness was associated with both low self-esteem and lack of closeness to others. The temporal model highlighted the importance of feeling close to others in predicting low paranoia levels in the next time frame. Finally, the between-subject model largely replicated an association found in both contemporaneous and temporal models. The current study reveals that the network approach offers a viable data-driven methodology for elucidating how paranoia-related mechanisms fluctuate over time and may determine its severity. Moreover, this novel perspective may open up new directions toward identifying potential targets for prevention and treatment of paranoia-related problems.

Exploring Perceived Interactions Between Consequences of Traumatic Brain Injury

OBJECTIVE

To explore the perceived interactions between consequences of traumatic brain injury (TBI).

PARTICIPANTS

Fifteen clinicians experienced in working with patients with TBI.

METHODS

Participating clinicians completed an online questionnaire in which they estimated the degree to which consequences of TBI (taken from the Brief ICF Core Set for Traumatic Brain Injury) causally relate to each other. Based on these perceived interactions, a visual network was constructed and centrality measures for this network were computed.

RESULTS

The resulting network demonstrates various strong perceived causal relations between the consequences of TBI. Impairments in consciousness were perceived to most strongly cause other TBI consequences in the network. Difficulties with acquiring, keeping, and terminating a job were perceived to be most strongly caused by other TBI consequences. Difficulties in partaking in complex interpersonal interactions were also perceived to play a central role in the network.

CONCLUSION

In the perception of clinicians, consequences of TBI interact with each other and are thus not solely a direct result of the injury. While more research is needed to map the interactions between consequences of TBI, our results could have important implications for the way we understand and treat the problems patients are faced with after TBI.

Associations of History of Displacement, Food Insecurity, and Stress With Maternal-Fetal Health in a Conflict Zone: A Case Study

Background: In populations with a history of conflict, early identification of pregnant women who are at risk of adverse pregnancy outcomes is challenging, especially if sonography is not available. We evaluated the performance of symphysis-fundal height (SFH) for identification of high-risk pregnancies and investigated if food security and diet quality, clinical biomarkers, and stress were associated with SFH and two known indicators of maternal-fetal well-being, sonography-estimated fetal weight and amniotic fluid index (AFI).

Methods: For this cross-sectional study, 61 women with high-risk pregnancies were recruited after referral to the obstetrics and gynecology unit at San José Hospital in Popayán, Colombia. Multiple stepwise linear and ordered logistic regressions were used to identify associations of SFH, sonography-estimated fetal weight and AFI classification with history of displacement, food insecurity, post-traumatic stress symptoms as well as biopsychosocial risk evaluated through the Colombian risk scale.

Results: History of displacement was associated with lower SFH Z-scores, but higher hemoglobin, taking iron supplements and a higher diastolic blood pressure were associated with higher SFH Z-scores. SFH was also associated with AFI but not with sonography-estimated fetal weight. Stress indicators were associated with a higher AFI. In contrast family support, an element of the Colombian biopsychosocial risk assessment, was associated with a higher sonography-estimated fetal weight, whereas more hours of sleep/day were associated with lower sonography-estimated fetal weight.

Conclusion: SFH was not only associated with biological factors known to affect maternal/fetal health but also with history of displacement, thus validating its use in conflict areas for pregnancy assessment. Associations of biopsychosocial stressors with maternal-fetal outcomes highlight the need for a systematic assessment of stress in pregnant women from conflict zones.

Applicability of an Automated Model and Parameter Selection in the Prediction of Screening-Level PTSD in Danish Soldiers Following Deployment: Development Study of Transferable Predictive Models Using Automated Machine Learning

BACKGROUND

Posttraumatic stress disorder (PTSD) is a relatively common consequence of deployment to war zones. Early postdeployment screening with the aim of identifying those at risk for PTSD in the years following deployment will help deliver interventions to those in need but have so far proved unsuccessful.

OBJECTIVE

This study aimed to test the applicability of automated model selection and the ability of automated machine learning prediction models to transfer across cohorts and predict screening-level PTSD 2.5 years and 6.5 years after deployment.

METHODS

Automated machine learning was applied to data routinely collected 6-8 months after return from deployment from 3 different cohorts of Danish soldiers deployed to Afghanistan in 2009 (cohort 1, N=287 or N=261 depending on the timing of the outcome assessment), 2010 (cohort 2, N=352), and 2013 (cohort 3, N=232).

RESULTS

Models transferred well between cohorts. For screening-level PTSD 2.5 and 6.5 years after deployment, random forest models provided the highest accuracy as measured by area under the receiver operating characteristic curve (AUC): 2.5 years, AUC=0.77, 95% CI 0.71-0.83; 6.5 years, AUC=0.78, 95% CI 0.73-0.83. Linear models performed equally well. Military rank, hyperarousal symptoms, and total level of PTSD symptoms were highly predictive.

CONCLUSIONS

Automated machine learning provided validated models that can be readily implemented in future deployment cohorts in the Danish Defense with the aim of targeting postdeployment support interventions to those at highest risk for developing PTSD, provided the cohorts are deployed on similar missions.

The current status of temporal network analysis for clinical science: Considerations as the paradigm shifts?

OBJECTIVE

Network analysis in psychology has ushered in a potentially revolutionary way of analyzing clinical data. One novel methodology is in the construction of temporal networks, models that examine directionality between symptoms over time. This paper provides context for how these models are applied to clinically-relevant longitudinal data.

METHODS

We provide a survey of statistical and methodological issues involved in temporal network analysis, providing a description of available estimation tools and applications for conducting such analyses. Further, we provide supplemental R code and discuss simulations examining temporal networks that vary in sample size, number of variables, and number of time points.

RESULTS

The following packages and software are reviewed: graphicalVAR, mlVAR, gimme, SparseTSCGM, mgm, psychonetrics, and the Mplus dynamic structural equation modeling module. We discuss the utility each procedure has for specific design considerations.

CONCLUSION

We conclude with notes on resources for estimating these models, emphasizing how temporal networks best approximate network theory.

A network analysis of posttraumatic stress disorder and dissociation in trauma-exposed adolescents

Posttraumatic stress disorder (PTSD) and dissociation have long been recognized to co-occur, leading the DSM-5 to introduce a dissociative subtype of PTSD into its nomenclature. Most research to date on the dissociative subtype has focused on adults. The current study aimed to extend this research to an adolescent sample and to examine symptom-level associations between PTSD and dissociation using network analysis. The analysis was conducted with 448 trauma-exposed detained US adolescents (24.55% female; mean age 15.98 ± 1.25 years). A network consisting of 20 DSM-5 PTSD symptoms was constructed, followed by a network consisting of 20 PTSD symptoms and five dissociative items. Expected influence bridge centrality was estimated to examine items with the most/strongest cross-construct connections (i.e. between PTSD and dissociation). The PTSD symptoms concentration problems, amnesia and recurrent memories and the dissociative items depersonalization, derealisation and can't remember things that happened had the highest bridge centrality values. These symptom-level associations extend our understanding of the PTSD-dissociation relationship by pointing to specific symptoms of PTSD and dissociation that may drive the co-morbidity between the two constructs. These findings may inform future intervention efforts.

Extending our understanding of the association between posttraumatic stress disorder and positive emotion dysregulation: A network analysis approach

Posttraumatic stress disorder (PTSD) has empirically-established associations with positive emotion dysregulation. Extending existing research, we utilized a network approach to examine relations between PTSD symptom clusters (intrusions, avoidance, negative alterations in cognitions and mood [NACM], alterations in arousal and reactivity [AAR]) and positive emotion dysregulation dimensions (nonacceptance, impulse control, goal-directed behavior). We identified (1) differential relations of PTSD symptom clusters with positive emotion dysregulation, and (2) central symptoms accounting for the PTSD and positive emotion dysregulation inter-group interconnections. Participants were 371 trauma-exposed community individuals (M_age = 43.68; 70.9 % females; 34.5 % white). We estimated a regularized Gaussian Graphic Model comprising four nodes representing the PTSD symptom clusters and three nodes representing positive emotion dysregulation dimensions. Study results indicated the key role of AAR and intrusions clusters in the PTSD group and impulse control difficulties in the positive emotion dysregulation group. Regarding cross-group connectivity patterns, findings indicate the pivotal role of (1) AAR in its link with positive emotion dysregulation dimensions, and (2) nonacceptance of positive emotions and impairment in goal-directed behavior in the context of positive emotions in their link to PTSD symptom clusters. Thus, the current study indicates the potentially central role of particular PTSD symptom clusters and positive emotion dysregulation dimensions, informing assessment and treatment targets.

The short-term dynamics of posttraumatic stress disorder symptoms during the acute posttrauma period

BACKGROUND

The manner in which posttraumatic stress disorder (PTSD) develops remains largely unknown. PTSD is comprised of 20 symptoms across 4 clusters. These clusters were hypothesized to reflect a failure of recovery model in which intrusive symptoms appear first. Intrusive symptoms led to avoidance of trauma-related stimuli, which resulted in sustained arousal. The sustained arousal ultimately led to dysphoria.

METHODS

This hypothesized symptom progression was evaluated during the acute posttrauma period (the first 30 days postevent). Participants (N = 80) reported their PTSD symptoms for 30 days via mobile devices. Using a short-term dynamic modeling framework, a temporal and contemporaneous model of PTSD symptoms was obtained.

RESULTS

In the temporal network, a fear-conditioning component was identified that supported the hypothesized set of relations among symptom clusters. The contemporaneous network was classified by two subnetworks. The first corresponded to a fear-conditioning model that included symptoms of intrusions and avoidance. The second included symptoms of dysphoria and arousal.

CONCLUSIONS

These findings suggest that, after a trauma, there may be a fear-conditioning process that involves intrusions, avoidance, and arousal symptoms. Dysphoric symptoms were also present but developed as a partially distinct component.

Affective dynamics among veterans: Associations with distress tolerance and posttraumatic stress symptoms

We tested a dynamic structural equation model (DSEM; Asparouhov, Hamaker, & Muthén, 2018) of positive and negative affect in 254 veterans with approximately 1.5 years of experience sampling data. The analysis provided estimates of several aspects of veteran's emotional experience including "trait" positive and negative affect (i.e., mean levels), inertia (i.e., tendency for emotions to self-perpetuate), innovation variance (conceptualized as lability, reactivity, or exposure to stressors), and cross-lagged associations between positive and negative affect. Veterans with higher trait negative affect had more negative affect inertia and innovation variance. This suggests a pattern whereby the veteran has more negative reactions, and negative emotions, in turn, tend to maintain themselves, contributing to higher trait negative affect. In contrast, veterans with higher trait positive affect exhibited more positive affect innovation variance (e.g., positive reactivity). Although veterans showed some consistency in dynamics across emotions (e.g., positive and negative reactivity were positively correlated), trait positive and negative affect were not significantly associated. Veterans with higher posttraumatic stress symptoms (PTSS) at baseline exhibited higher reactivity to negative events, less positive affect, and more negative affect during the follow-up. Veterans with higher distress tolerance reported not only lower PTSS but also a more adaptive pattern of affective experience characterized by lower inertia and reactivity in negative affect and more positive lagged associations between negative affect and subsequent positive affect. The results demonstrated that distress tolerance and PTSS in veterans were associated with dynamics of positive and negative emotion over time, suggesting specific differences in affect regulation processes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Does sense of threat in civilians during an armed conflict predict subsequent depression symptoms?

OBJECTIVE

We aimed to assess whether peritraumatic threat experienced during a period of armed conflict predicted subsequent depression symptoms.

METHOD

Ninety-six Israeli civilians provided real-time reports of exposure to rocket warning sirens and subjective sense of threat, twice daily for 30 days, during the 2014 Israel-Gaza conflict. Depression symptoms were reported 2 months after the conflict. Mixed-effects models were used to estimate peritraumatic threat levels and peritraumatic threat reactivity (within-person elevations in threat following siren exposure). These were then assessed as predictors of depression symptoms at 2 months in an adjusted regression model.

RESULTS

Individual peritraumatic threat level, but not peritraumatic threat reactivity, was a significant predictor of 2 months depression symptoms, even after controlling for baseline depression symptoms.

CONCLUSIONS

The findings imply that in situations of ongoing exposure, screening for perceived levels of peritraumatic threat might be useful in identifying those at risk for developing subsequent depression symptoms.