Multimodal Approaches to Neurobiology of Traumatic Dissociation

创伤后个体的分离症状很常见，使人衰弱，而且代价高昂;然而， 了解其生物学机制如何与创伤后应激障碍治疗相互作用。广泛的创伤性分离 包括一系列不同的，但临床上相互关联的症状：人格解体，现实解体， 健忘症、麻木、闪回、被动影响现象和身份障碍。单独或 各种组合，这些症状作为诊断标准和常见的相关特征， 多种精神疾病创伤性分离也与显著的个人和社会 负担具有分离症状的创伤个体通常同时患有精神疾病， 高比例的自我毁灭行为和自杀倾向，是不成比例的治疗利用者。此外，本发明还提供了一种方法， 他们在接受治疗干预后出现流失、无反应和复发的风险更高。尽管 创伤性分离症状的显著和致残性质，对创伤性分离症状的神经生物学知之甚少。 这些进程和有针对性的干预措施并不存在。 创伤后应激障碍治疗研究既没有着眼于神经中间表型的分离，也没有如何 这些与心理生理和数字表型有关。与临床症状相比 通过测量，这些生物学的和当下的解离数字标记可以更有力地映射到 儿童期和成人期后的分离亚型的障碍的基本核心方面 外伤我们建议在我们先前的探索性R21基础上，现在捕获纵向多模态表型 与分离相关的数据，包括创伤后应激障碍治疗方式的前、后和期间， 基于安全的组件。 本研究的目标是：1）了解映射到解离性细胞的差异生物标志物， 症状，以及2）了解这些生物标志物如何最好地预测经验性反应的轨迹 基于标准的治疗。对于每一个目标，我们将收集神经成像，生理学， 数字表型数据，应用多模态数据的计算建模来提供机器学习 基线和纵向分离中间表型的无偏预测模型。这 自然主义的研究将使我们能够绘制解离的生物学，重要的是，解离的变化 症状和潜在的生物标志物随着时间的推移，使用自然主义的证据为基础的治疗PTSD在130 寻求治疗的创伤后应激障碍患者，以及一系列分离症状。 这些目标的成功完成将提供一个新的和强大的理解生物学 创伤暴露后解离亚型的标志物，并将确定 理解和治疗创伤后应激障碍

Dissociative symptoms in traumatized individuals are common, debilitating, and costly; however, little is known about how its biological mechanisms interact with PTSD treatment. Traumatic dissociation broadly encompasses a range of distinct, yet clinically interrelated symptoms: depersonalization, derealization, amnesia, numbing, flashbacks, passive influence phenomena, and identity disturbances. Either alone or in various combinations, these symptoms serve as diagnostic criteria and commonly associated features across multiple psychiatric disorders. Traumatic dissociation is also associated with significant personal and societal burden. Traumatized individuals with dissociative symptoms typically have co-occurring psychiatric conditions, high rates of self-destructive behaviors and suicidality, and are disproportionate treatment utilizers. In addition, they are at increased risk for attrition, non-response and relapse following treatment interventions. Despite the significant and disabling nature of traumatic dissociative symptoms, little is known about the neurobiology of these processes and targeted interventions do not exist. PTSD treatment studies have neither looked at neural intermediate phenotypes of dissociation, nor how these are associated with psychophysiological and digital phenotypes. Compared to clinical symptom measures, these biological and in-the-moment digital markers of dissociation may more robustly map onto the underlying core aspects of the disorder differentiating dissociation subtypes following childhood and adult trauma. We propose to build upon our prior Exploratory R21 to now capture longitudinal multimodal phenotype data related to dissociation, pre-, post- and during PTSD treatment modalities that include empirically-derived, exposure-based components. The goals of this study will be 1) to understand the differential biomarkers that map onto dissociative symptoms, and 2) to understand how these biomarkers may best predict trajectory of response to empirically based standard-of-care treatments. For each of these Aims, we will collect Neuroimaging, Physiology, and Digital Phenotyping data, applying computational modeling with multimodal data to provide machine-learning based, unbiased predictive models of dissociative intermediate phenotypes at baseline and longitudinally. This naturalistic study will allow us to map the biology of dissociation, and importantly, the change in dissociative symptoms and underlying biomarkers over time, using naturalistic evidenced-based treatment for PTSD in 130 treatment-seeking patients with PTSD, and a range of dissociative symptoms. Successful completion of these Aims will provide a novel and powerful understanding of the biological markers of dissociation subtypes following trauma exposure, and will identify biological mechanisms for understanding and treating PTSD with dissociation.