Predicting Suicidal Behavior in Veterans with Bipolar Disorder using Behavioral and Neuroimaging Based Impulsivity Phenotypes

项目摘要 退伍军人事务部关于精神疾病与自杀关系的研究 事件表明，双相情感障碍患者的自杀率最高，甚至更高 与创伤后应激障碍相比，精神分裂症和重度抑郁症。如果不治疗，就会患躁郁症 精神障碍有5%-15%的自杀死亡风险，使其成为退伍军人心理健康护理的首要任务。 患有双相情感障碍的个体表现冲动--即使在欣快期和理解 双相情感障碍患者的冲动性和自杀行为之间的关系可能有助于识别 未来自杀未遂的风险最大的个人。在理解这种关系方面取得了进展 然而，不同冲动衡量标准的混为一谈，大大阻碍了这一进程。冲动可以是 使用自我报告测量作为相对稳定的特征进行测量，或作为通过 使用对环境意外情况变化敏感的行为范式。状态冲动性模型 关注于(1)“快速反应抑制”，包括难以抑制的反应。 环境状况变化的背景和(2)“选择冲动”，定义为不能拖延 满足感换来更大的回报。这些模型的相关性很弱，但与大脑区域有很强的重叠。 与双相情感障碍的神经生物学有关。我们提出了状态和特征的调查措施 40例有自杀未遂史的双相情感障碍退伍军人的冲动性(BD+S)，40 有双相情感障碍且无自杀未遂病史的退伍军人(BD-S)和40名健康对照(HC)。 在这项研究中，自杀结果的主要衡量标准是使用哥伦比亚大学的 自杀严重程度评定量表和病历回顾。这将通过使用 基于5种自杀行为中的任何一种的更广泛的综合自杀结果测量 包括：(1)自杀死亡；(2)自杀未遂；(3)自杀未遂；(4)自杀未遂； (五)自杀未遂行为。遵循全面的基线临床、神经心理学和 神经影像评估所有退伍军人随后将完成为期6个月和12个月的面对面随访 情绪和自杀行为的临床评估。这项研究中对特质冲动的新测量将包括 紧迫感和冲动的/有预谋的攻击。我们将评估冲动性攻击之间的关系 以及BD+S的社会认知、功能障碍和神经认知功能。我们将进一步调查 快速反应抑制(使用进行/不进行任务)和选择的两个神经回路敲击状态测量 冲动性(使用延迟折扣任务)，分别使用功能磁共振成像和 轴突定向弥散和密度成像，这将提供纤维排列和 连接灰质区域的神经束中的轴突密度。动态因果模型将用于经验性 分别测试由这两个回路组成的大脑区域相互作用的因果模型。 机器学习将被用来将基线特征和冲动状态的测量纵向结合起来 预测一年内的自杀行为。本研究的具体目的是：(1)考察二者之间的关系 双相情感障碍退伍军人冲动特质与自杀未遂史之间的关系 研究两种状态冲动模型背后的神经回路及其与自杀的关系 在患有双相情感障碍的退伍军人中尝试病史；以及(3)确定冲动测量的哪种组合 在基线时间点区分BD+S与BD-S和HC，可用于预测自杀行为 纵向超过1年。

Project Summary Studies examining the relationship between psychiatric illness and suicide from the Department of Veterans Affairs indicate that individuals with bipolar disorder have the highest rate of suicide, which is even greater compared to post-traumatic stress disorder, schizophrenia and major depression. When left untreated, bipolar disorder has a 5-15% risk of death by suicide, making it a top priority for Veterans' mental health care. Individuals with bipolar disorder behave impulsively – even during euthymic periods and understanding the relationship between impulsivity and suicidal behavior in bipolar disorder may assist in the identification of individuals at greatest risk for a future suicide attempt. Progress in understanding this relationship has been significantly hampered, however, by the conflation of different measures of impulsivity. Impulsivity can be measured as a relatively stable trait using self-report measures or as a state measure assessed through the use of behavioral paradigms sensitive to changing environmental contingencies. Models of state impulsivity have focused on (1) “rapid-response inhibition” involving difficulty inhibiting responses that are prepotent in the context of changing environmental situations and (2) “choice impulsivity” defined as the inability to delay gratification for a larger reward. These models correlate weakly, but strongly overlap with brain regions implicated in the neurobiology of bipolar disorder. We propose investigating measures of state and trait impulsivity in 40 euthymic Veterans with bipolar disorder (BD) and a suicide attempt history (BD+S), 40 euthymic Veterans with bipolar disorder and no suicide attempt history (BD-S) and 40 healthy controls (HC). The primary suicide outcome measure in this study will be suicide attempts assessed using the Columbia Suicide Severity Rating Scale and medical record review. This will be complemented through the use of a broader composite suicide outcome measure based on the occurrence of any of 5 types of suicidal behavior including: (1) death by suicide; (2) suicide attempt; (3) interrupted suicide attempt; (4) aborted suicide attempt; or (5) preparatory suicidal acts. Following comprehensive baseline clinical, neuropsychological and neuroimaging assessments all Veterans will subsequently complete 6- and 12-month in-person follow-up clinical assessments of mood and suicidal behavior. Novel measures of trait impulsivity in this study will include urgency and impulsive/premeditated aggression. We will assess the relationship between impulsive aggression and social cognition, functional disability and neurocognitive functioning in BD+S. We will further investigate two neural circuits tapping state measures of rapid response inhibition (using a go/no-go task) and choice impulsivity (using a delay discounting task), respectively, using functional magnetic resonance imaging and neurite orientation dispersion and density imaging, which will provide novel measures of fiber arrangement and neurite density in tracts connecting gray matter regions. Dynamic causal modeling will be used to empirically test causal models regarding the interaction of brain regions comprising these two circuits respectively. Machine learning will be used to integrate baseline trait and state measures of impulsivity to longitudinally predict suicidal behavior over one year. The specific aims of this study are: (1) to investigate the relationship between trait measures of impulsivity and suicide attempt history in Veterans with bipolar disorder; (2) to investigate the neural circuitry underlying two models of state impulsivity and their relationship to suicide attempt history in Veterans with bipolar disorder; and (3) to identify which combination of impulsivity measures differentiates BD+S from BD-S and HC at a baseline timepoint and can be used to predict suicidal behavior longitudinally over 1 year.