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.

最近的研究表明，与一般美国人相比，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，但其发病机制仍不清楚理解，包括与情感不稳定（AI）相关的神经回路的改变自杀行为。人工智能是一种以维度方式跨越多种精神疾病的特征。二芯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之外低风险和高风险退伍军人中人工智能这两个组成部分的关键神经回路的连接性自杀，该项目检查情感惊恐调节，一种转化的心理生理测量由杏仁核介导，提供可靠、低成本、非语言的人工智能组件度量。进展情况确定定量的自杀行为将有助于预防和预测自杀行为测量与人工智能相关的神经活动/连接性和/或情感惊恐调节，以响应经过验证的令人不快的图片，可能作为自杀风险的维度心理生理内表型。此外，我们将研究人工智能的三种可靠的自我报告衡量标准（衡量不稳定性、强度和情绪调节）可能作为自杀行为的维度表型。检查这个退伍军人的神经回路、生理学和行为的组合处于低水平（非自杀性精神控制）自杀的高风险（自杀意念者、自杀未遂者）以及健康的控制，前景广阔了解自杀行为的发病机制，并确定最终可能提供的目标逆转此类致病过程的新颖治疗干预措施。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自杀并确定导致情绪处理异常的神经回路紊乱自杀风险低和高的退伍军人。为此，我们将描述 144 名退伍军人样本的特征，每个退伍军人各 36 名。四个组。所有参与者都将接受严格的诊断和临床评估（包括多项经充分验证的 AI 和自杀风险测量），并接受 3T 功能 MRI 和情感惊吓调制当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 6个月的随访中，所有参与者将重复惊吓评估，以检验重测的可靠性。三个患者组将在 6 个月和 12 个月时进行临床评估随访。该项目旨在识别退伍军人自杀行为背后的行为、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 个月随访时的自杀行为向上。影响：我们的前瞻性多模式设计有望帮助揭示生物以及心理因素导致自杀行为。拟议的研究可能有助于前瞻性地确定最有自杀行为风险的退伍军人。