Neurobiology of Affective Instability in Veterans at Low and High Risk for Suicide

低自杀风险和高自杀风险退伍军人情感不稳定的神经生物学

基本信息

批准号：
10311973
负责人：
ERIN A. HAZLETT
金额：
--
依托单位：
JAMES J PETERS VA MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-01 至 2022-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10311973
关键词：
Affect Affective Amygdaloid structure Attention Behavior Behavioral Biological Biological Factors Biological Markers Biology Brain Clinic Clinical assessments Data Diagnostic Dimensions Down-Regulation Emotional Emotions Exhibits Functional Magnetic Resonance Imaging Future Generations Impairment Individual Intervention Lead Life Link Literature Longitudinal Studies Major Depressive Disorder Measurement Measures Mediating Mental disorders Military Personnel National Institute of Mental Health Neurobiology Participant Pathogenesis Pathogenicity Pathologic Patient Self-Report Patients Pattern Phenotype Physiology Population Prevention Process Proxy Psychological Factors Psychometrics Psychophysiology Public Health Regulation Research Research Personnel Risk Risk Factors Sampling Services Suicide Suicide attempt Suicide prevention Symptoms Testing Time Translating Veterans Work affective modulation of startle base clinical risk cohort completed suicide cost design emotion regulation emotional reaction endophenotype experience follow up assessment follow-up habituation high risk interest mind control multimodality neural circuit neuroimaging neuromechanism novel prevention service preventive intervention prospective psychosocial reducing suicide relating to nervous system response suicidal suicidal behavior suicidal risk suicide attempter trait two-dimensional

项目摘要

Recent work demonstrates that veterans exhibit higher suicide risk compared with the general U.S. population. Despite progress in understanding risk factors for suicidal behavior, the pathogenesis is poorly understood, including alterations in the neural circuitry underlying affective instability (AI) associated with suicidal behavior. AI is a trait that cuts across multiple psychiatric disorders in a dimensional manner. Two core components of AI are emotional reactivity and regulation. In addition to examining brain activity and functional connectivity in key neural circuitry underlying these two components of AI in veterans at low and high risk for suicide, this project examines affective startle modulation, a translational, psychophysiological measure mediated by the amygdala that provides a reliable, low-cost, nonverbal metric of AI components. Progress in the prevention and prediction of suicidal behavior would be facilitated by the identification of quantitative measures of AI-related neural activity/connectivity and/or affective startle modulation in response to validated unpleasant pictures and may serve as dimensional psychophysiological endophenotypes of risk for suicide. Additionally, we will examine three reliable self-report measures of AI (measuring lability, intensity, and emotion regulation) which may serve as a dimensional phenotype of suicidal behavior. Examining this combination of neural circuits, physiology, and behavior in veterans at low (non-suicidal psychiatric controls) and high risk (suicidal ideators, suicide attempters) for suicide, as well as healthy controls, holds great promise for understanding the pathogenesis of suicidal behavior, and identifying targets that may ultimately provide novel treatment intervention to reverse such pathogenic processes. The proposed longitudinal study will focus on AI as a critical dimension that is directly associated with risk for suicide and identify the neural-circuitry disturbances that underlie emotion processing abnormalities in veterans at low and high risk for suicide. To do so, we will characterize a sample of 144 veterans, 36 in each of the four groups. All participants will receive rigorous diagnostic and clinical assessments (including several well-validated measures of AI and suicide risk), and undergo 3T functional MRI and affective startle modulation measurement while they perform passive emotion-processing/reactivity and active emotion-regulation tasks. At 6-month follow-up, all participants will repeat the startle assessment in order to examine test-retest reliability. Clinical assessment follow-up will be done at 6- and 12-months in the three patient groups. The project aims to identify behavioral, neurobiological, and psychophysiological features underlying suicidal behavior in veterans and determine whether baseline psychophysiological measures predict suicidal behavior at 12-month follow- up. Impact: Our prospective, multi-modal design promises to help uncover the mechanisms by which biological and psychological factors give rise to suicidal behavior. The proposed research may aid in prospectively identifying veterans at greatest risk for suicidal behavior.

最近的工作表明，与美国将军相比，退伍军人表现出更高的自杀风险人口。尽管在理解自杀行为的危险因素方面进展，但发病机理却很差理解，包括与与自杀行为。 AI是一种以维度方式削减多种精神病障碍的特征。两个核心 AI的组成部分是情感反应性和调节。除了检查大脑活动和功能在低和高风险的退伍军人中，这两个组成部分的关键神经电路中的连通性自杀，该项目检查了情感惊吓调制，这是一种转化，心理生理措施由Amygdala介导的，提供可靠的，低成本的非语言AI组件度量。进步通过识别定量，可以促进对自杀行为的预防和预测响应经过验证不愉快的图片，可以用作自杀风险的维度心理生理生理表型。此外，我们将研究三种可靠的自我报告量度（测量不稳定，强度和情绪调节）可以用作自杀行为的维表型。检查一下在低（非杀害精神病患者）的退伍军人中，神经回路，生理和行为的结合和高风险（自杀构想者，自杀式意见）以及健康的控制，具有巨大的希望了解自杀行为的发病机理，并确定可能最终提供的目标新的治疗干预措施以扭转这种致病过程。拟议的纵向研究将集中于AI作为与风险直接相关的关键维度自杀并确定情绪处理异常的神经通路障碍自杀的低风险和高风险的退伍军人。为此，我们将表征144名退伍军人的样本，每一个四组。所有参与者将获得严格的诊断和临床评估（包括几个 AI和自杀风险的验证量度很好），并经历3T功能性MRI和情感惊吓调制测量在执行被动情绪处理/反应性和主动情绪调节任务时。在为期6个月的随访，所有参与者都将重复惊吓评估，以检查测试可靠性。在三个患者组中，将在6个月和12个月的时间进行临床评估随访。该项目旨在识别退伍军人的行为，神经生物学和心理生理特征并确定基线心理生理学措施是否在12个月以后预测自杀行为 - 向上。影响：我们的前瞻性，多模式设计有望帮助揭示生物学的机制心理因素会导致自杀行为。拟议的研究可能有助于前瞻性确定自杀行为风险最大的退伍军人。