Neurocognitive markers of short-term risk for suicidal behavior in high-risk Veterans

项目总结/摘要 鉴于每年退伍军人自杀率很高，预防自杀已成为VHA的战略 要务虽然在筛选退伍军人自杀风险方面取得了进展，但假阳性率很高， 这仍然是一个挑战，可能会将临床资源从那些最需要它们的退伍军人那里转移出去。 需要创新的新方法来评估自杀风险，准确识别短期（例如即将到来的 周）自杀行为的风险，并且基于客观标记，而不是主要依赖于自我- 次报告.最近的工作已经确定了几个认知领域，包括冲动控制和抑制， 注意力分散，隐含的语义内容和奖励/惩罚的处理，这可能都是 在自杀者身上发生了改变 目前的项目将使用一组基于计算机的测试来评估这些认知领域， 然后进行计算建模，以提取代表潜在认知的附加元变量， 前瞻性预测高危退伍军人自杀行为的短期风险。退伍军人 将在自杀事件后从急性住院精神病病房招募，并在3个月时进行测试。 间隔一年;我们还将记录自杀行为（SB），定义为实际，中断或流产 企图自杀或准备自杀的行为。在目标1中，我们将使用短期内收集的神经认知任务分数 在SB之前，确定哪些任务（以及哪些认知领域的改变）可以 前瞻性预测SB的短期风险（未来3个月内）。在目标2中，我们将进行计算 对行为数据进行建模，以提取额外的元变量，描述潜在的认知过程 如反应谨慎（冲动）;奖励、惩罚和中性反馈的主观价值; 持续;和倾向于探索新的反应，以确定这些元变量中的哪些可以 在目标3中，我们将联合收割机结合这些神经认知任务得分和元变量， 沿着自杀风险的“标准”指标，如人口统计学、自我报告和临床评估， 统计预测模型，以确定是否包括这些认知变量和元变量， 显著改善了对该高风险退伍军人样本中SB短期风险预测。 这项研究的结果将提供急需的信息，以改善退伍军人的身份识别 可能在未来几周内试图自杀，以便临床资源可以有针对性地提供帮助。 他们基于计算机的神经认知测试代表了相对客观的行为标记， 在临床环境中部署相当简单，甚至通过远程医疗。此外，我们对潜在认知的关注 作为可修改的因素，过程将通过建议具体的 基于退伍军人个人认知特征的治疗方法。

Project Summary/Abstract Given the high rates of Veteran suicide each year, suicide prevention has become a VHA strategic priority. While progress has been made in screening Veterans for suicide risk, high rates of false positives remain a challenge, potentially diverting clinical resources away from those Veterans who need them most. Innovative new approaches to assess suicide risk are needed, that accurately identify short-term (e.g. coming weeks) risk of suicidal behavior, and that are based on objective markers rather than relying primarily on self- report. Recent work has identified several cognitive domains, including impulse control and inhibition, increased distractibility, implicit semantic content, and processing of rewards/punishments, that may all be altered in suicidal individuals. The current project will use a battery of computer-based tests to assess these cognitive domains, followed by computational modeling to extract additional meta-variables representing latent cognitive processes, to prospectively predict short-term risk of suicidal behavior in high-risk Veterans. Veterans will be recruited from acute in-patient psychiatric units following a suicidal event, and tested at 3-month intervals for a year; we will also record suicidal behavior (SB), defined as an actual, interrupted, or aborted attempt or a behavior preparatory to suicide. In Aim 1, we will use neurocognitive task scores collected shortly before the SB, to determine which tasks (and therefore, alterations in which cognitive domains) can prospectively predict short-term risk of SB (within the next 3 months). In Aim 2, we will conduct computational modeling on the behavioral data, to extract additional meta-variables, describing latent cognitive processes such as response caution (impulsivity); subjective value of rewarding, punishing, and neutral feedback; perseveration; and tendency to explore new responses, to determine which of these meta-variables can prospectively predict SB. In Aim 3, we will combine these neurocognitive task scores and meta-variables, along with "standard" indicators of suicide risk such as demographic, self-report, and clinical assessment, in a statistical prediction model, to determine whether inclusion of these cognitive variables and meta-variables can significantly improve prediction of short-term risk for SB in this high-risk Veteran sample. The results of this study will provide much-needed information to improve identification of Veterans likely to make a suicide attempt in the upcoming weeks, so that clinical resources can be targeted to help them. The computer-based neurocognitive tests represent relatively objective behavioral markers that could be deployed fairly simply in a clinical setting or even via telehealth. Additionally, our focus on underlying cognitive processes, as modifiable factors, will improve the ability to optimize intervention by suggesting specific therapeutic approaches based on an individual Veteran's cognitive profile.