Developing an Emotion- and Memory-Processing Group Intervention for PTSD with complex features: a group case series with survivors of repeated interpersonal trauma.
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SUBMITTER: Clifford G
PROVIDER: S-EPMC6070972 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAlterations in major stress response systems are present during the immediate aftermath of trauma and may play a role in determining risk for developing posttraumatic stress disorder (PTSD). However, the dynamics and determinants of stress responses during this acute recovery phase, and their relevance for longitudinal clinical course and prognosis, have yet to be fully examined. The objectives of the present study were to characterize stress response and habituation patterns to repeated social stressors in women who recently experienced interpersonal trauma and to determine the extent to which these stress responses were associated with PTSD during prospective follow-up.MethodThis longitudinal study examined salivary cortisol and alpha-amylase and heart rate (HR) responses to repeated stressors in 98 young women (ages 18-30). Participants included women who had experienced an incident of interpersonal trauma (i.e., physical and/or sexual assault) in the three months prior to their baseline assessment (n = 58) and a comparison group of healthy, non-traumatized women (n = 40). Women completed the Trier Social Stress Test (TSST), clinical interviews to evaluate posttraumatic stress symptom severity at the baseline assessment and again at 1-, 3-, and 6-month follow-ups.ResultsMultilevel models revealed a pattern of robust initial cortisol TSST responses and habituation across successive TSSTs; alpha-amylase and HR responses showed no evidence of habituation across TSSTs. Among interpersonal trauma survivors, current PTSD status was associated with more pronounced cortisol responses to the first TSST. Survivors exhibited similarly blunted cortisol responses across follow-up TSSTs regardless of PTSD status, suggesting habituation of cortisol responses among survivors who developed PTSD. PTSD re-experiencing symptoms were uniquely associated with blunting of cortisol TSST responses.ConclusionFindings suggest that PTSD as a diagnostic entity is meaningfully associated with cortisol responses to repeated social stressors. Social-evaluative threat is a salient form of danger for interpersonal trauma survivors. Identifying the determinants of cortisol (non)habituation to repeated social-evaluative threat among interpersonal trauma survivors could inform the development of early interventions for PTSD.
| S-EPMC7686015 | biostudies-literature
Project description:BackgroundThough lifetime exposure to traumatic events is significant, only a minority of individuals develops symptoms of posttraumatic stress disorder (PTSD). Post-trauma alterations in neurocognitive and affective functioning are likely to reflect changes in underlying brain networks that are predictive of PTSD. These constructs are assumed to interact in a highly complex way. The aim of this exploratory study was to apply machine learning models to investigate the contribution of these interactions on PTSD symptom development and identify measures indicative of circuit related dysfunction.MethodsN = 94 participants admitted to the emergency room of an inner-city hospital after trauma exposure completed a battery of neurocognitive and emotional tests 1 month after the incident. Different machine learning algorithms were applied to predict PTSD symptom severity and clusters after 3 months based.ResultsOverall, model accuracy did not differ between PTSD clusters, though the importance of cognitive and emotional domains demonstrated both key differences and overlap. Alterations in higher-order executive functioning, speed of information processing, and processing of emotionally incongruent cues were the most important predictors.ConclusionsData-driven approaches are a powerful tool to investigate complex interactions and can enhance the mechanistic understanding of PTSD. The study identifies important relationships between cognitive processing and emotion recognition that may be valuable to predict and understand mechanisms of risk and resilience responses to trauma prospectively.
| S-EPMC7310383 | biostudies-literature
Project description:BackgroundThe factor structure of posttraumatic stress disorder (PTSD) has been extensively studied in Western countries. Some studies have assessed its factor structure in Asia (China, Sri Lanka, and Malaysia), but few have directly assessed the factor structure of PTSD in an Indian adult sample. Furthermore, in a largely patriarchal society in India with strong gender roles, it becomes imperative to assess the association between the factors of PTSD and gender.ObjectiveThe purpose of the present study was to assess the factor structure of PTSD in an Indian sample of trauma survivors based on prevailing models of PTSD defined in the DSM-IV-TR (APA, 2000), and to assess the relation between PTSD factors and gender.MethodThe sample comprised of 313 participants (55.9% female) from Jammu and Kashmir, India, who had experienced a natural disaster (N=200) or displacement due to cross-border firing (N=113).ResultsThree existing PTSD models-two four-factor models (Emotional Numbing and Dysphoria), and a five-factor model (Dysphoric Arousal)-were tested using Confirmatory Factor Analysis with addition of gender as a covariate. The three competing models had similar fit indices although the Dysphoric Arousal model fit significantly better than Emotional Numbing and Dysphoria models. Gender differences were found across the factors of Re-experiencing and Anxious arousal.ConclusionsFindings indicate that the Dysphoric Arousal model of PTSD was the best model; albeit the fit indices of all models were fairly similar. Compared to males, females scored higher on factors of Re-experiencing and Anxious arousal. Gender differences found across two factors of PTSD are discussed in light of the social milieu in India.
| S-EPMC4247496 | biostudies-literature
Project description:Early intervention following exposure to a traumatic life event could change the clinical path from the development of post traumatic stress disorder (PTSD) to recovery, hence the interest in early detection and underlying biological mechanisms involved in the development of post traumatic sequelae. We introduce a novel end-to-end neural network that employs resting-state and task-based functional MRI (fMRI) datasets, obtained one month after trauma exposure, to predict PTSD symptoms at one-, six- and fourteen-months after the exposure. FMRI data, as well as PTSD status and symptoms, were collected from adults at risk for PTSD development, after admission to emergency room following a traumatic event. Our computational method utilized a per-region encoder to extract brain regions embedding, which were subsequently updated by applying the algorithmic technique of pairwise attention. The affinities obtained between each pair of regions were combined to create a pairwise co-activation map used to perform multi-label classification. The results demonstrate that the novel method's performance in predicting PTSD symptoms, in a prospective manner, outperforms previous analytical techniques reported in the fMRI literature, all trained on the same dataset. We further show a high predictive ability for predicting PTSD symptom clusters and PTSD persistence. To the best of our knowledge, this is the first deep learning method applied on fMRI data with respect to prospective clinical outcomes, to predict PTSD status, severity and symptom clusters. Future work could further delineate the mechanisms that underlie such a prediction, and potentially improve single patient characterization.
| S-EPMC8350148 | biostudies-literature
Project description:Contemporary symptom-based diagnosis of post-traumatic stress disorder (PTSD) largely overlooks related neurobehavioral mechanisms and relies entirely on subjective interpersonal reporting. Previous studies associating biomarkers with PTSD have mostly used symptom-based diagnosis as the main outcome measure, disregarding the wide variability and richness of PTSD phenotypical features. Here, we aimed to computationally derive potential biomarkers that could efficiently differentiate PTSD subtypes among recent trauma survivors. A three-staged semi-unsupervised method ("3C") was used to firstly categorize individuals by current PTSD symptom severity, then derive clusters based on clinical features related to PTSD (e.g. anxiety and depression), and finally to classify participants' cluster membership using objective multi-domain features. A total of 256 features were extracted from psychometrics, cognitive functioning, and both structural and functional MRI data, obtained from 101 adult civilians (age = 34.80 ± 11.95; 51 females) evaluated within 1 month of trauma exposure. The features that best differentiated cluster membership were assessed by importance analysis, classification tree, and ANOVA. Results revealed that entorhinal and rostral anterior cingulate cortices volumes (structural MRI domain), in-task amygdala's functional connectivity with the insula and thalamus (functional MRI domain), executive function and cognitive flexibility (cognitive testing domain) best differentiated between two clusters associated with PTSD severity. Cross-validation established the results' robustness and consistency within this sample. The neural and cognitive potential biomarkers revealed by the 3C analytics offer objective classifiers of post-traumatic morbidity shortly following trauma. They also map onto previously documented neurobehavioral mechanisms associated with PTSD and demonstrate the usefulness of standardized and objective measurements as differentiating clinical sub-classes shortly after trauma.
| S-EPMC7320966 | biostudies-literature
Project description:OBJECTIVE:Sexual abuse experienced in childhood and adolescence is associated with severity of posttraumatic stress disorder (PTSD), depressive symptoms, and emotion regulation difficulties. The current study examined the relationships among these factors in a sample of adolescents with sexual abuse-related PTSD. It was hypothesized that (a) self-perceived emotion regulation difficulties would predict severity of PTSD and depressive symptoms, and that (b) depressive symptoms would mediate the relationship between emotion regulation difficulties and PTSD. METHOD:Ninety treatment-seeking female adolescents with a history of sexual abuse were evaluated using the Child PTSD Symptom Scale-Interview and completed the Negative Mood Regulation Questionnaire and the Beck Depression Inventory as part of a baseline evaluation. RESULTS:Greater emotion regulation difficulties were associated with greater severity of PTSD and depressive symptoms. In addition, the relationship between emotion regulation difficulties and PTSD severity was mediated by depressive symptoms. However, the reverse was also true: the relationship between emotion regulation difficulties and depressive symptoms was mediated by PTSD symptoms. CONCLUSIONS:Mediation analyses showed that emotion regulation difficulties were associated with both PTSD and depressive symptoms rather than fitting a unidirectional model. These findings are consistent with and extend previous research and highlight the importance of emotion regulation in adolescent survivors of sexual abuse. (PsycINFO Database Record
| S-EPMC5756524 | biostudies-literature
Project description:BackgroundWithout effective treatment, PTSD and depression can cause persistent disability in disaster-affected populations.MethodsOur objective was to test the efficacy of Interpersonal Psychotherapy (IPT) delivered by trained local personnel compared with treatment as usual (TAU) for Posttraumatic Stress Disorder (PTSD) and Major Depressive Disorder (MDD) among adults affected by the Sichuan 2008 earthquake. A small randomized controlled trial of IPT + TAU versus TAU alone was delivered by local mental health personnel in Shifang, China. Between July 2011 and January 2012, 49 adults ≥ 18 years with PTSD, MDD or both were enrolled and randomized to 12 weekly sessions of IPT + TAU (27) or TAU (22) alone x 12 weeks. IPT was then offered to the TAU group. Unblinded follow up assessments were conducted at three and six months. IPT was a 12 session, weekly one hour treatment delivered by local personnel who were trained and supervised in IPT. TAU was continuation of prescribed psychotropic medication (if applicable) and crisis counseling, as needed. MAIN OUTCOME(S) AND MEASURES (S): Clinician Administered PTSD Scale (CAPS) PTSD diagnosis; Structured Clinical Interview for DSM-IV (SCID) for MDD diagnosis. Secondary measures included PTSD/depression symptoms, interpersonal conflict/anger, social support, self-efficacy and functioning.ResultsUsing an intent-to-treat analysis, 22 IPT + TAU and 19 TAU participants were compared at three months post-baseline. A significantly greater reduction of PTSD and MDD diagnoses was found in the IPT group (51.9%, 30.1%, respectively) versus the TAU group (3.4%, 3.4%, respectively). Despite the small sample, the estimates for time-by-condition analyses of target outcomes (2.37 for PTSD (p = .018) and 1.91 for MDD (p = .056)) indicate the improvement was better in the IPT + TAU condition versus the TAU group. Treatment gains were maintained at 6 months for the IPT group. A similar treatment response was observed in the TAU group upon receipt of IPT.ConclusionsThis initial study shows that IPT is a promising treatment for reducing PTSD and depression, the two major mental health disorders affecting populations surviving natural disaster, using a design that builds local mental health care capacity.Trial registrationClinicalTrials.Gov number, NCT01624935.
| S-EPMC4172897 | biostudies-literature
Project description:Previous studies have demonstrated relations between spontaneous neural activity evaluated by resting-state functional magnetic resonance imaging (fMRI) and symptom severity in post-traumatic stress disorder. However, few studies have used brain-based measures to identify imaging associations with illness severity at the level of individual patients. This study applied connectome-based predictive modeling (CPM), a recently developed data-driven and subject-level method, to identify brain function features that are related to symptom severity of trauma survivors. Resting-state fMRI scans and clinical ratings were obtained 10-15 months after the earthquake from 122 earthquake survivors. Symptom severity of post-traumatic stress disorder features for each survivor was evaluated using the Clinician Administered Post-traumatic Stress Disorder Scale (CAPS-IV). A functionally pre-defined atlas was applied to divide the human brain into 268 regions. Each individual's functional connectivity 268 × 268 matrix was created to reflect correlations of functional time series data across each pair of nodes. The relationship between CAPS-IV scores and brain functional connectivity was explored in a CPM linear model. Using a leave-one-out cross-validation (LOOCV) procedure, findings showed that the positive network model predicted the left-out individual's CAPS-IV scores from resting-state functional connectivity. CPM predicted CAPS-IV scores, as indicated by a significant correspondence between predicted and actual values (r = 0.30, P = 0.001) utilizing primarily functional connectivity between visual cortex, subcortical-cerebellum, limbic, and motor systems. The current study provides data-driven evidence regarding the functional brain features that predict symptom severity based on the organization of intrinsic brain networks and highlights its potential application in making clinical evaluation of symptom severity at the individual level.
| S-EPMC7779396 | biostudies-literature
Project description:In about 30-50% of patients with posttraumatic stress disorder (PTSD), symptoms persist after treatment. Although neurobiological research has advanced our understanding of PTSD, little is known about the neurobiology underlying persistence of PTSD. Two functional MRI scans were collected from 72 war veterans with and without PTSD over a 6- to 8-month interval, during which PTSD patients received trauma-focused therapy. All participants performed a trauma-unrelated emotional processing task in the scanner. Based on post-treatment symptom severity, a distinction was made between remitted and persistent patients. Behavioral and imaging measures of trauma-unrelated emotional processing were compared between the three groups (remitted patients, N=21; persistent patients, N=22; and combat controls, N=25) with repeated-measures (pre- and post-treatment) analyses. Second, logistic regression was used to predict treatment outcome. Before and after treatment, persistent patients showed a higher dorsal anterior cingulate cortex (dACC) and insula response to negative pictures compared with remitted patients and combat controls. Before treatment, persistent patients showed increased amygdala activation in response to negative pictures compared with remitted patients. The remitted patients and combat controls did not differ on the behavioral or imaging measures. Finally, higher dACC, insula, and amygdala activation before treatment were significant predictors of symptom persistence. Our results highlight a pattern of brain activation that may predict poor response to PTSD treatment. These findings can contribute to the development of alternative or additional therapies. Further research is needed to elucidate the heterogeneity within PTSD and describe how differences in neural function are related to treatment outcome. Such approaches are critical for defining parameters to customize PTSD treatment and improve treatment response rates.
| S-EPMC4748440 | biostudies-literature
Project description:Dropout rates in trauma-focused treatments for adult posttraumatic stress disorder (PTSD) are high. Most research has focused on demographic and pretreatment predictors of dropout, but findings have been inconsistent. We examined predictors of dropout in cognitive processing therapy (CPT) by coding the content of trauma narratives written in early sessions of CPT. Data are from a randomized controlled noninferiority trial of CPT and written exposure therapy (WET) in which CPT showed significantly higher dropout rates than WET (39.7% CPT vs. 6.4% WET). Participants were 51 adults with a primary diagnosis of PTSD who were receiving CPT and completed at least one of three narratives in the early sessions of CPT. Sixteen (31%) in this subsample were classified as dropouts and 35 as completers. An additional 9 participants dropped out but could not be included because they did not complete any narratives. Of the 11 participants who provided a reason for dropout, 82% reported that CPT was too distressing. The CHANGE coding system was used to code narratives for pathological trauma responses (cognitions, emotions, physiological responses) and maladaptive modes of processing (avoidance, ruminative processing, overgeneralization), each on a scale from 0 (absent) to 3 (high). Binary logistic regressions showed that, averaging across all available narratives, more negative emotions described during or around the time of the trauma predicted less dropout. More ruminative processing in the present time frame predicted lower rates of dropout, whereas more overgeneralized beliefs predicted higher rates. In the first impact statement alone, more negative emotions in the present time frame predicted lower dropout rates, but when emotional reactions had a physiological impact, dropout was higher. These findings suggest clinicians might attend to clients' written trauma narratives in CPT in order to identify indicators of dropout risk and to help increase engagement.
| S-EPMC7431675 | biostudies-literature