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

项目摘要 研究检查精神疾病和自杀之间的关系从退伍军人部 研究表明，患有双相情感障碍的人自杀率最高， 与创伤后应激障碍、精神分裂症和重度抑郁症相比。如果不治疗，双相情感障碍 这种疾病有5-15%的自杀死亡风险，使其成为退伍军人精神卫生保健的首要任务。 双相情感障碍患者的行为冲动-即使在情绪稳定期， 双相情感障碍中冲动性和自杀行为之间的关系可能有助于识别 最有可能在未来自杀的人。在理解这种关系方面的进展一直是 然而，由于不同的冲动性衡量标准的混淆，这一研究受到了很大的阻碍。冲动可能是 作为一个相对稳定的特质，使用自我报告的措施或作为一个国家的措施，通过评估， 使用对变化的环境突发事件敏感的行为范式。状态冲动模型 （1）“快速反应抑制”，涉及难以抑制在免疫系统中占优势的反应， 变化的环境条件和（2）“选择冲动”定义为无法延迟 为了更大的回报而满足这些模型与大脑区域的相关性很弱，但却有很强的重叠 与双相情感障碍的神经生物学有关我们建议调查的措施，状态和特质 40名有双相情感障碍（BD）和自杀企图史（BD+S）的情绪正常退伍军人的冲动性， 具有双相情感障碍且无自杀企图史的情绪稳定的退伍军人（BD-S）和40名健康对照（HC）。 本研究的主要自杀结局指标是使用哥伦比亚量表评估的自杀企图 自杀严重程度评定量表和医疗记录审查。这将通过使用一个 基于5种自杀行为中任何一种的发生率的更广泛的复合自杀结局指标 包括：（1）自杀死亡;（2）自杀未遂;（3）自杀未遂中断;（4）自杀未遂流产; 或（5）准备自杀行为。在全面的基线临床、神经心理和 所有退伍军人随后将完成6个月和12个月的亲自随访 情绪和自杀行为的临床评估本研究中特质冲动性的新测量方法包括 紧迫感和冲动/预谋的攻击。我们将评估冲动性攻击和 以及BD+S中的社会认知、功能障碍和神经认知功能。我们将进一步调查 两个神经回路敲击快速反应抑制（使用go/no-go任务）和选择的状态测量 冲动性（使用延迟折扣任务），分别使用功能性磁共振成像和 神经突取向分散和密度成像，这将提供纤维排列的新措施， 连接灰质区域的神经束中的神经突密度。动态因果模型将用于实证 分别测试包含这两个回路的大脑区域相互作用的因果模型。 机器学习将用于整合冲动性的基线特征和状态测量， 预测自杀行为超过一年。本研究的具体目的是：（1）探讨 在退伍军人双相情感障碍患者中，冲动性和自杀企图史的特质测量之间;（2） 研究两种状态冲动模型的神经回路及其与自杀的关系 在退伍军人与双相情感障碍的尝试历史;和（3）确定冲动措施的组合 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.