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.

受创伤者的解离症状很常见，使人衰弱，而且代价高昂。然而，很少有 了解其生物学机制如何与 PTSD 治疗相互作用。广泛的创伤性解离 涵盖一系列不同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。无论是单独还是在 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。创伤性分离还与重大的个人和社会影响有关。 负担。具有解离症状的创伤个体通常同时存在精神疾病， 自毁行为和自杀率很高，并且是不成比例的治疗利用者。此外， 他们在治疗干预后面临自然减员、无反应和复发的风险增加。尽管 创伤性解离症状的显着性和致残性，人们对它的神经生物学知之甚少。 这些过程和有针对性的干预措施并不存在。 PTSD 治疗研究既没有关注解离的神经中间表型，也没有关注解离的神经中间表型。 这些与心理生理和数字表型相关。与临床症状相比 通过采取措施，这些生物和即时数字解离标记可以更可靠地映射到 该疾病的潜在核心方面区分了儿童期和成人期的解离亚型 创伤。我们建议以之前的探索性 R21 为基础，现在捕获纵向多峰表型 与解离、创伤后应激障碍治疗前、治疗后和治疗期间相关的数据，包括经验得出的、 基于曝光的组件。 本研究的目标是 1) 了解映射到解离性的差异生物标志物 症状，2) 了解这些生物标志物如何最好地根据经验预测反应轨迹 基于护理标准的治疗。对于每个目标，我们将收集神经影像学、生理学和 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 基于基线和纵向解离中间表型的无偏预测模型。这 自然主义研究将使我们能够绘制解离生物学图谱，重要的是，解离性的变化 随着时间的推移，使用自然的循证治疗方法治疗 PTSD 130 患有创伤后应激障碍（PTSD）和一系列分离症状的寻求治疗的患者。 成功完成这些目标将为生物提供新颖而有力的理解。 创伤暴露后解离亚型的标记，并将确定其生物学机制 理解和治疗解离性创伤后应激障碍（PTSD）。