Progressive social withdrawal in trauma-exposed older adolescents and young adults: neurocircuitry predictors

遭受创伤的老年青少年和年轻人的渐进性社交退缩：神经回路预测因素

基本信息

批准号：
10672968
负责人：
ISABELLE M ROSSO
金额：
$ 46.75万
依托单位：
MCLEAN HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-20 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10672968
关键词：
20 year old Accelerometer Adolescent Adolescent and Young Adult Adult Age Alcohol consumption Amygdaloid structure Anhedonia Automobile Driving Behavioral Biological Markers Brain region Categories Cellular Phone Characteristics Clinical Code Data Data Collection Development Diagnostic Ecological momentary assessment Emotions Enrollment Evolution Exposure to Extinction Face Frequencies Fright Functional Magnetic Resonance Imaging Future Health Human Immune response Impairment Incentives Individual Intervention Interview Literature Location Loneliness Longevity Machine Learning Major Depressive Disorder Measures Mental Health Mental disorders Metadata Modeling National Institute of Mental Health Neurobiology Nucleus Accumbens Outcome Participant Pathologic Patient Self-Report Phenotype Population Post-Traumatic Stress Disorders Prefrontal Cortex Process Psychopathology Recording of previous events Research Rest Rewards Risk Risk Factors Role Sampling Social Behavior Social Interaction Social Network Specific qualifier value Structure Text Time Trauma Well in self Withdrawal Work cardiovascular risk factor density depressive symptoms digital disability follow-up functional MRI scan innovation interpersonal trauma learning strategy longitudinal design mortality neural circuit neurobiological mechanism neurofeedback neuroimaging physical conditioning post-traumatic symptoms prediction algorithm predictive modeling prospective rapid growth response reward circuitry reward processing social social anxiety social engagement substance use suicidal risk therapy development trauma exposure

项目摘要

Social withdrawal is a transdiagnostic phenotype that is strongly associated with detrimental physical and mental health outcomes across the lifespan. As quantified using structural features of individuals’ social networks, social withdrawal is associated with 60-70% increased mortality and a threefold increase in suicide risk. Recent findings document associations between trauma-related psychopathology and altered social network structural features, including size, density, diversity, and embeddedness. Critically, because the transition to adulthood (age 16-20) is a period of rapid expansion in social networks, forms of psychopathology that produce social withdrawal may confer particular risk for poor outcomes during this developmental stage. Thus, the transition to adulthood is a key developmental period for the evolution of social withdrawal in trauma- exposed populations. Recent literature indicates that social reward processing may drive social network size and complexity, but the role of disrupted social reward functioning in driving progressive social withdrawal following trauma exposure is poorly understood. Using an innovative longitudinal design including digital phenotyping of social behavior, the proposed study will investigate activity and connectivity within brain regions that bidirectionally code for social approach and avoidance, including the basolateral amygdala (BLA), ventromedial prefrontal cortex (VMPFC), and nucleus accumbens (NAcc). The hypothesis is that BLA-VMPFC- NAcc functional connectivity will prospectively predict progressive social withdrawal during the transition to adulthood following interpersonal trauma exposure. We will enroll 120 trauma-exposed participants (ages 16- 20) who endorse posttraumatic and/or depressive symptoms, and 60 healthy controls. Trauma-exposed participants will be stratified for baseline self-reported social anhedonia. We obtain baseline social withdrawal measures, including active self-reports of social interaction and passive smartphone based phenotyping of activity via accelerometer, GPS, and call/text metadata. Participants will complete an fMRI scan to obtain measures of BLA-VMPFC-NAcc connectivity, followed by one year of digital phenotyping using active and passive data to measure social withdrawal. Based on our extensive preliminary data, we hypothesize that: 1) baseline social withdrawal will be associated with baseline BLA-VMPFC-NAcc connectivity; (2) baseline BLA- VMPFC-NAcc connectivity will prospectively predict progressive social withdrawal over the course of a 12- month follow-up; (3) predictive models of social withdrawal that include BLA-VMPFC-NAcc connectivity will outperform alternate models. The proposed integrated approach (fMRI, digital phenotyping) will identify specific circuitry associated with progressive social withdrawal during the transition to adulthood, and will develop predictive algorithms that forecast social withdrawal trajectories based on baseline connectivity within BLA- VMPFC-NAcc circuitry. Ultimately this work could lead to the development of targeted interventions (e.g. neurofeedback), and may shift the field toward reward-circuitry accounts of social withdrawal.

社交退缩是一种跨诊断表型，与不健康的身体和健康密切相关。使用个人社会结构特征进行量化的整个生命周期的心理健康结果。网络中，社交退缩与死亡率增加 60-70% 以及自杀率增加三倍相关最近的研究结果记录了创伤相关的精神病理学与社会风险之间的关联。网络结构特征，包括规模、密度、多样性和嵌入性，至关重要。向成年过渡（16-20 岁）是社交网络、精神病理学形式迅速扩展的时期产生社交退缩的行为可能会在这个发育阶段带来不良后果的特殊风险。因此，向成年期的过渡是创伤中社交退缩演变的关键发展时期。最近的文献表明，社会奖励处理可能会推动社交网络的规模。和复杂性，但扰乱的社会奖励功能在推动渐进性社会退缩中的作用使用创新的纵向设计（包括数字化）对创伤暴露后的情况知之甚少。社会行为的表型，拟议的研究将调查大脑区域内的活动和连接双向编码社交接近和回避，包括基底外侧杏仁核（BLA），腹内侧前额叶皮质 (VMPFC) 和伏隔核 (NAcc) 的假设是 BLA-VMPFC-。 NAcc 功能连接将前瞻性地预测在向社会过渡期间渐进的社交退缩我们将招募 120 名经历过创伤的参与者（16 岁至 16 岁）。 20) 认可创伤后和/或抑郁症状，以及 60 名遭受创伤的健康对照。参与者将根据基线自我报告的社交快感缺失进行分层，我们获得基线社交退缩。措施，包括社交互动的主动自我报告和基于智能手机的被动表型分析通过加速度计、GPS 和通话/文本元数据进行的活动参与者将完成功能磁共振成像扫描以获得信息。测量 BLA-VMPFC-NAcc 连接性，然后使用主动和根据我们广泛的初步数据，我们利用了被动数据来衡量社交退缩：1）基线社交退缩将与基线 BLA-VMPFC-NAcc 连接性相关； (2) 基线 BLA- VMPFC-NAcc 连接性将前瞻性地预测 12 年内渐进的社交退缩月随访；(3) 包括 BLA-VMPFC-NAcc 连接的社交退缩预测模型优于替代模型。所提出的综合方法（功能磁共振成像、数字表型分析）将确定具体的与成年过渡期间渐进性社交退缩相关的电路，并将发展预测算法，根据 BLA 内的基线连接性预测社交退缩轨迹最终，这项工作可能会导致有针对性的干预措施的发展（例如神经反馈），并可能将领域转向社交退缩的奖励回路帐户。