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.

We investigated within-day symptom dynamics in PTSD patients using experience sampling technology.Temporal and contemporaneous symptom networks differed; thus, it is important to estimate both.Changes in hypervigilance were an important predictor of symptoms at the next time point.

Pharmacological memory modulation to augment trauma-focused psychotherapy for PTSD: a systematic review of randomised controlled trials.

Trauma-focused psychotherapy (tf-PT) is the first-line treatment for posttraumatic stress disorder (PTSD). Tf-PT focuses on processing and modulating trauma memories. Not all patients benefit, however, and there is room for improvement of efficacy. Pharmacologically augmenting trauma memory modulation in the context of tf-PT may help optimise treatment outcome. To systematically review effects of pharmacologically augmented memory modulation in the context of tf-PT for PTSD (PROSPERO preregistration ID: CRD42021230623). We conducted a systematic review of randomised controlled trials of psychotherapy treatment for PTSD. We included placebo-controlled studies that augmented at least one treatment session pharmacologically targeting memory extinction or reconsolidation. We calculated post-treatment between group (pharmacological augmentation vs placebo control) effect sizes of PTSD symptom severity. We included 13 RCTs. There was large heterogeneity in augmentation procedure and methodological quality. Four studies showed significantly greater PTSD symptom reduction in the pharmacological augmentation group (propranolol, hydrocortisone, dexamethasone, D-cycloserine) compared to placebo. Seven studies showed no significant effect of pharmacological augmentation compared to placebo (D-cycloserine, rapamycin, mifepristone, propranolol, mifepristone combined with D-cycloserine, methylene blue). Two studies showed significantly smaller PTSD symptom reduction in the pharmacological augmentation group (D-cycloserine, dexamethasone) compared to placebo. Results of pharmacological augmentation were mixed overall and heterogenous for the pharmacological agents tested in more than one study. Additional studies and replications are needed to identify which pharmacological agents work, in which combination and to identify patient groups that benefit most to tailor PTSD treatment.

Temporal associations between trauma-related sleep disturbances and PTSD symptoms: An experience sampling study.

OBJECTIVE: There is robust evidence for the influence of sleep disturbances on the maintenance of PTSD symptoms. However, little is known about day-to-day variation in trauma-related sleep disturbances (namely insomnia symptoms and nightmares) and their associations with PTSD symptoms. Therefore, we explored the dynamic interplay of these symptoms in daily life using an experience sampling method (ESM). METHOD: For 15 consecutive days, participants with PTSD symptomatology as primary complaint (N = 48) reported momentary levels of insomnia symptoms and nightmares as well as PTSD symptoms via a mobile app. RESULTS: Multilevel model analyses revealed that insomnia and nightmares were significant predictors of PTSD symptomatology on the following day; furthermore, nightmares were predictive of each of the four PTSD symptom clusters, namely reexperiencing, avoidance, cognition and hyperarousal as well as symptoms of dissociation. However, PTSD symptoms did not predict insomnia or nightmares during the following night. Multilevel mediation analyses suggested that nightmares mediate the relationship between insomnia and next-day PTSD symptoms. CONCLUSIONS: These findings support accumulating evidence that trauma-related sleep disturbances play an important role in the maintenance of PTSD symptoms, by elevating symptoms daily. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Effects of current treatments for trauma survivors with posttraumatic stress disorder on reducing a negative self-concept: a systematic review and meta-analysis.

Background: A negative self-concept is characterised by dysfunctional cognitions about the self and has been suggested to be a key factor involved in the development and maintenance of posttraumatic stress disorder (PTSD). In addition, the current definitions of PTSD according to DSM-5 and the new ICD-11 diagnosis of Complex PTSD (CPTSD) include aspects of negative self-concept in their diagnostic criteria. Objective: The aim of this meta-analysis was to synthesise the currently available evidence on the effects of psychological interventions for PTSD on negative self-concept. Methods: PubMed, PsychINFO, PSYNDEX, PTSDpubs and Cochrane Library were searched for randomised controlled trials (RCTs) of psychological treatments for PTSD symptoms in adults, published up to February 2021. A systematic review and meta-analysis were conducted, with risk of bias assessed by the Cochrane Risk of Bias Assessment Tool. Results: A total of 25 RCTs (N = 2585) were included in the meta-analysis. Results showed that psychological interventions significantly improve a negative self-concept with a moderate to large controlled effect size (k = 30, g = 0.67, 95% CI [0.31, 1.02], p < .001) at post-treatment. Heterogeneity between studies was large but could not be accounted for by moderators included in the current analysis, i.e. different types of interventions (e.g. with vs. without a cognitive restructuring component, trauma-focused vs. not). Conclusions: Current treatments for PTSD are effective in reducing a negative self-concept. However, more research is needed to identify moderators of this effect and identify interventions that are most effective for reducing negative self-concept.

Antecedentes: El autoconcepto negativo se caracteriza por cogniciones disfuncionales sobre uno mismo y se ha sugerido que es un factor clave involucrado en el desarrollo y mantención del trastorno de estrés postraumático (TEPT). Además, la definición actual de TEPT según el DSM-5 y el nuevo diagnóstico de TEPT complejo (TEPTC) de la CIE-11 incluyen aspectos del autoconcepto negativo en sus criterios diagnósticos.Objetivo: El objetivo de este meta-análisis fue sintetizar la evidencia actualmente disponible sobre los efectos de las intervenciones psicológicas para el TEPT sobre el autoconcepto negativo.Métodos: Se realizaron búsquedas en PubMed, PsychINFO, PSYNDEX, PTSDpubs y Cochrane Library de ensayos controlados aleatorizados (ECA) de tratamientos psicológicos para los síntomas de TEPT en adultos, publicados hasta febrero de 2021. Se realizó una revisión sistemática y un meta-análisis, con riesgo de sesgo evaluado por la Herramienta Cochrane de Evaluación del Riesgo de Sesgo.Resultados: Se incluyeron un total de 25 ECA (N = 2585) en el meta-análisis. Los resultados mostraron que las intervenciones psicológicas mejoran significativamente el autoconcepto negativo con un tamaño del efecto controlado de moderado a grande (k = 30, g = 0,67, IC del 95% [0,31, 1,02], p < 0,001) después del tratamiento. La heterogeneidad entre los estudios fue grande, pero los moderadores incluidos en el análisis actual no pudieron explicarla, es decir, diferentes tipos de intervenciones (p. ej., presencia vs. ausencia de un componente de reestructuración cognitiva, centrada en el trauma vs. no centrada en el trauma).Conclusiones: Los tratamientos actuales para el TEPT son efectivos para reducir el autoconcepto negativo. Sin embargo, se necesita de más investigaciones para identificar moderadores de este efecto e identificar intervenciones que sean más efectivas para reducir el autoconcepto negativo.

Comparing PTSD symptom networks in type I vs. type II trauma survivors.

Background: Network analysis has gained increasing attention as a new framework to study complex associations between symptoms of post-traumatic stress disorder (PTSD). A number of studies have been published to investigate symptom networks on different sets of symptoms in different populations, and the findings have been inconsistent. Objective: We aimed to extend previous research by testing whether differences in PTSD symptom networks can be found in survivors of type I (single event; sudden and unexpected, high levels of acute threat) vs. type II (repeated and/or protracted; anticipated) trauma (with regard to their index trauma). Method: Participants were trauma-exposed individuals with elevated levels of PTSD symptomatology, most of whom (94%) were undergoing assessment in preparation for PTSD treatment in several treatment centres in Germany and Switzerland (n = 286 with type I and n = 187 with type II trauma). We estimated Bayesian Gaussian graphical models for each trauma group and explored group differences in the symptom network. Results: First, for both trauma types, our analyses identified the edges that were repeatedly reported in previous network studies. Second, there was decisive evidence that the two networks were generated from different multivariate normal distributions, i.e. the networks differed on a global level. Third, explorative edge-wise comparisons showed moderate or strong evidence for specific 12 edges. Edges which emerged as especially important in distinguishing the networks were between intrusions and flashbacks, highlighting the stronger positive association in the group of type II trauma survivors compared to type I survivors. Flashbacks showed a similar pattern of results in the associations with detachment and sleep problems (type II > type I). Conclusion: Our findings suggest that trauma type contributes to the heterogeneity in the symptom network. Future research on PTSD symptom networks should include this variable in the analyses to reduce heterogeneity.

Antecedentes: El análisis de redes ha ganado cada vez más atención como un nuevo marco para estudiar asociaciones complejas entre síntomas del Trastorno de Estrés Postraumático (TEPT). Se han publicado una cantidad de estudios para investigar las redes de síntomas en diferentes conjuntos de síntomas en distintas poblaciones, y los hallazgos han sido inconsistentes.Objetivos: Nuestro objetivo fue ampliar la investigación previa probando si se pueden encontrar diferencias entre las redes de síntomas del TEPT en sobrevivientes de trauma de tipo 1 (evento único; súbito e inesperado, niveles elevados de amenaza aguda) versus los de tipo 2 (eventos repetidos y/o prolongados; anticipados) (con respecto a su trauma índice).Métodos: Los participantes eran individuos expuestos al trauma con niveles elevados de sintomatología de TEPT, la mayoría de los cuales (94%) se sometían a una evaluación en preparación para el tratamiento del TEPT en varios centros de Alemania y Suiza (n = 286 con tipo 1 y n = 187 con tipo 2 de trauma). Estimamos modelos gráficos Bayesianos Gaussianos para cada tipo de grupo de trauma y exploramos las diferencias entre los grupos en la red de síntomas.Resultados: En primer lugar, para ambos tipos de trauma, nuestros análisis identificaron los bordes que se reportaron repetidamente en estudios de redes anteriores. En segundo lugar, hubo evidencia decisiva que las dos redes fueron generadas de diferentes distribuciones normales multivariadas, es decir, las redes diferían a nivel global. En tercer lugar, las comparaciones exploratorias de los bordes mostraron una evidencia de moderada a fuerte para 12 bordes específicos. Los bordes que surgieron como especialmente importantes para distinguir las redes fueron las intrusiones y flashbacks, destacando la asociación fuertemente positiva entre los grupos de tipo 2 en comparación con los sobrevivientes de trauma del grupo de tipo 1. Los flashbacks mostraron un patrón similar de resultados en las asociaciones con desapego y problemas de sueño (tipo 2 > tipo 1).Conclusiones: Nuestros resultados sugieren que el tipo de trauma contribuye a la heterogeneidad en los síntomas de red. La investigación futura sobre las redes de los síntomas de TEPT debería incluir esta variable en los análisis para reducir la heterogeneidad.

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).