Multimodal Approaches to Neurobiology of Traumatic Dissociation

创伤性解离神经生物学的多模式方法

• 批准号: 10356886
• 负责人: Milissa L Kaufman
• 金额: $ 72.23万
• 依托单位: MCLEAN HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10356886
• 关键词: Address; Adult; Aftercare; Amnesia; Anterior; Arousal; Attention; Behavioral; Biological; Biological Markers; Biology; Brain; Brain region; Childhood; Clinical; Clinical Trials; Complex; Computer Models; Data; Depersonalization; Derealizations; Diagnosis; Disease; Dissociation; Dorsal; Ecological momentary assessment; Emotional; Environment; Equipment and supply inventories; Evidence based treatment; Exhibits; Fright; Future; Goals; Image; Individual; Intervention; Learning; Machine Learning; Magnetic Resonance Imaging; Maps; Measures; Medial; Mental disorders; Modality; Modeling; Nature; Neurobiology; Numbness; Participant; Pathologic; Patient Self-Report; Patients; Phase; Phenotype; Physiological; Physiology; Post-Traumatic Stress Disorders; Prefrontal Cortex; Process; Psychometrics; Psychophysiology; Recording of previous events; Recovery; Reflex action; Refractory; Regulation; Relapse; Rest; Risk; Role; Self Destructive Behavior; Sinus Arrhythmia; Suicide; Symptoms; System; Time; Trauma; Work; base; cingulate cortex; clinical care; cost; diagnostic criteria; digital; disabling symptom; emotion regulation; heart rate variability; multimodal data; multimodal neuroimaging; multimodality; neuroimaging; novel; pediatric trauma; phenotypic data; predictive modeling; relating to nervous system; respiratory; response; skills; standard of care; targeted biomarker; targeted therapy trials; time use; trauma exposure

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.

受伤的个体中的分离症状是普遍，使人衰弱且昂贵的。但是，几乎没有 知道其生物学如何与PTSD治疗相互作用。创伤性解离广泛 包括一系列不同但临床上相互关联的症状：人格化，贬义， 健忘症，麻木，倒叙，被动影响现象和身份障碍。单独或在 各种组合，这些症状是诊断标准，并且通常相关的特征 多种精神疾病。创伤分离也与重要的个人和社会有关 负担。具有分离性症状的受创伤的个体通常具有共同出现的精神病疾病， 自我毁灭行为和自杀性的高度高，是不成比例的治疗用途。此外， 在治疗干预措施后，它们的流失，无响应和复发的风险增加。尽管有 创伤性解离性症状的显着性和残疾性质，对神经生物学知之甚少 这些过程和有针对性的干预措施不存在。 PTSD治疗研究既不考虑解离的神经中间表型，也不是如何 这些与心理生理和数字表型有关。与临床症状相比 措施，这些生物学和主义的分离标记可能会更牢固地映射到 儿童和成人后区分分离亚型的疾病的基本核心方面 创伤。我们建议以先前的探索性R21为基础，以捕获纵向多模式表型 与解离，前，后和PTSD治疗方式有关的数据，包括经验衍生的， 基于曝光的组件。 这项研究的目标将是1）了解映射到分离的差异生物标志物 症状，以及2）了解这些生物标志物如何最好地预测对经验的反应轨迹 基于护理标准治疗。对于这些目标，我们将收集神经影像学，生理学和 数字表型数据，使用多模式数据应用计算建模以提供机器学习 基于基线和纵向分离性中间表型的基于基于的，公正的预测模型。这 自然主义研究将使我们能够绘制解离的生物学，重要的是分离的变化 随着时间的流逝，使用自然主义的PTSD治疗130的症状和潜在的生物标志物 寻求治疗的患有PTSD的患者和一系列分离症状。 这些目标的成功完成将为生物学提供新颖而有力的理解 外伤暴露后解离亚型的标志物，并将确定生物学机制 通过解离理解和治疗PTSD。