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

最近的研究表明，与美国普通人相比，退伍军人的自杀风险更高。 人口尽管在了解自杀行为的危险因素方面取得了进展， 理解，包括与情感不稳定（AI）相关的神经回路的改变， 自杀行为AI是一种以维度方式跨越多种精神疾病的特征。两个核心 人工智能的组成部分是情绪反应和调节。除了检查大脑活动和功能 在低风险和高风险的退伍军人中，这两个AI组件的关键神经回路的连接性 自杀，这个项目研究情感惊吓调制，一个翻译，心理生理学措施 由杏仁核介导，提供了一个可靠的，低成本的，非语言的AI组件的度量。进展 自杀行为的预防和预测将有助于确定定量的 AI相关神经活动/连接性和/或情感惊吓调制的测量， 不愉快的图片，并可能作为自杀风险的维度心理生理学内表型。 此外，我们还将研究三种可靠的AI自我报告指标（测量不稳定性，强度和 情绪调节），这可能是自杀行为的维度表型。研究这个 神经回路，生理学和行为的组合在退伍军人在低（非自杀性精神病控制） 和高自杀风险（自杀意念者，自杀未遂者），以及健康对照， 了解自杀行为的发病机制，并确定可能最终提供 新的治疗干预，以扭转这种致病过程。 拟议的纵向研究将重点关注人工智能作为与风险直接相关的关键维度 并确定神经回路紊乱，情绪处理异常的基础， 低风险和高风险自杀的退伍军人。为此，我们将对144名退伍军人的样本进行表征， 四个组。所有参与者都将接受严格的诊断和临床评估（包括几个 AI和自杀风险的充分验证的措施），并接受3 T功能MRI和情感惊吓调制 测量，而他们执行被动情绪处理/反应和主动情绪调节任务。在 6-随访一个月，所有受试者将重复惊吓评估，以检查重测信度。 将在6个月和12个月时对三个患者组进行临床评估随访。该项目旨在 确定退伍军人自杀行为的行为、神经生物学和心理生理学特征 并确定基线心理生理学指标是否可预测12个月随访时的自杀行为- 起来影响：我们的前瞻性，多模式设计承诺帮助揭示生物学的机制。 心理因素导致自杀行为。拟议的研究可能有助于前瞻性地 找出最有可能自杀的退伍军人

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.