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

最近的研究表明，与普通美国人相比，退伍军人表现出更高的自杀风险。 人口。尽管在了解自杀行为的危险因素方面取得了进展，但其发病机制尚不完善。 了解，包括与情绪不稳定(AI)相关的神经回路的改变 自杀行为。人工智能是一种以一种维度的方式跨越多种精神障碍的特征。双核 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 低风险和高风险退伍军人AI这两个组成部分潜在的关键神经回路的连通性 自杀，这个项目考察了情感惊吓调制，这是一种翻译的心理生理学测量 由杏仁核调节，提供了一种可靠的、低成本的、非语言的人工智能组件度量。取得的进展 对自杀行为的定量识别有助于预防和预测自杀行为。 对人工智能相关神经活动/连通性和/或情感惊吓调制的测量 令人不快的图片，并可能作为自杀风险的维度心理生理学内表型。 此外，我们还将检查AI的三个可靠的自我报告测量(测量稳定性、强度和 情绪调节)，这可能是自杀行为的一个维度表型。检查这一点 低龄退伍军人的神经回路、生理和行为的组合(非自杀精神对照) 自杀的高风险(自杀意念者、自杀未遂者)，以及健康对照，都很有希望 以了解自杀行为的发病机制，并确定最终可能提供 逆转这种致病过程的新的治疗干预措施。 拟议的纵向研究将侧重于人工智能作为与风险直接相关的关键维度 并找出导致情绪处理异常的神经回路障碍 处于低和高自杀风险的退伍军人。为了做到这一点，我们将对144名退伍军人样本进行表征，每个样本36名 这四个组。所有参与者都将接受严格的诊断和临床评估(包括 经过良好验证的AI和自杀风险测量)，并接受3T功能MRI和情感惊吓调制 在他们执行被动情绪处理/反应和主动情绪调节任务时进行测量。在… 在6个月的随访中，所有参与者都将重复惊吓评估，以检验重测的信度。 三组患者将在6个月和12个月时进行临床评估和随访。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测量是否可以预测12个月后的自杀行为 向上。影响：我们的前瞻性、多模式设计承诺帮助揭示生物 而心理因素会导致自杀行为。拟议中的研究可能会对未来的研究有所帮助 找出自杀行为风险最大的退伍军人。

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.