Decision Processes of Late-Life Suicide

描述（由申请人提供）：这是一项为期四年的老年自杀认知标志研究的竞争性更新。我们的首要目标是找到预测晚年高致命性自杀行为的因素。扩展应激素质模型，我们的研究项目旨在了解在实验范式和现实世界的决策背景下，类似特质的素质——认知控制受损、社会处理缺陷和冲动性——是如何在糟糕的决策中表达出来的。我们目前的横断面研究侧重于晚年自杀行为的认知相关性，将其与认知控制不良联系起来。在越来越多的证据中，我们有初步证据表明，老年自杀未遂者的决策能力受损。此外，我们有关于自杀行为异质性维度的重要线索。我们看到高死亡率的自杀企图者在复杂的环境中挣扎，做出糟糕的社会决定。相比之下，我们看到，在低死亡率和计划外自杀企图的人群中，有一种短视决定的倾向，属于冲动的广泛范畴。这项拟议的研究考察了具有高致死率尝试的个体做出不利决定的认知和神经基础。我们建议(i)将认知控制缺陷与老年自杀未遂者在复杂、模糊的环境中做出的不良决策联系起来，（ii）描述可能导致认知控制不良和不利决策的神经系统变化，（iii）将我们的研究扩展到可能导致晚年自杀行为的不良社会决策，以及（iv）开发一种结合这些特征的预测自杀风险的算法

DESCRIPTION (provided by applicant): This is a competitive renewal of a four-year study of cognitive markers of late-life suicide. Our overarching goal is to find factors predictive of high-lethality suicidal behavior in late life. Extending the stress-diathesis model, our research program seeks to understand how trait-like diatheses - impaired cognitive control, deficits in social processing, and impulsivity - are expressed in poor decisions, both in experimental paradigms and in the context of real-world decision making. Our current cross-sectional study focuses on cognitive correlates of late-life suicidal behavior, linking it to poor cognitive contro. Adding to a growing body of evidence, we have preliminary evidence that older suicide attempters are impaired in their decision-making. Further, we have important clues about dimensions of heterogeneity in suicidal behavior. We see that high-lethality suicide attempters struggle in complex environments and make poor social decisions. By contrast, we see a tendency toward short-sighted decisions falling under the broad category of impulsivity in people with low-lethality and unplanned suicide attempts. The proposed study examines the cognitive and neural underpinnings of disadvantageous decisions in individuals with high-lethality attempts. We propose to (i) relate cognitive control deficits to poor decision-making in complex, ambiguous environments in older suicide attempters, (ii) to describe neural system alterations that may underlie poor cognitive control and disadvantageous decisions, (iii) to extend our investigations to poor social decisions that may contribute to late-life suicidal behavior, and (iv to develop an algorithm for predicting suicide risk that incorporates these features in addition to established risk factors. These goals will be accomplished by two interlinked studies. Study I is a cross-sectional, case- control comparison of 70 high-lethality suicide attempters, 70 low-lethality suicide attempters, 70 non-suicidal depressed, and 50 non-psychiatric controls. We focus on older suicide attempters, as suicide rates are high in old age and because late-life suicide attempts, due to their serious and determined nature, offer a window into death by suicide. Study I will use a framework that combines behavioral economics and cognitive neuroscience, with multilevel clinical, behavioral, cognitive, and imaging assessments. Our sampling and assessment strategy was designed with particular attention to possible confounders. Study II will follow 250 older suicide attempters (110 enrolled to date + 140 newly recruited) for up to 4 years, capturing recurrent attempts. It will serve as a prospective validatin of our predictive model. The extensive clinical and behavioral/cognitive characterization combined with a machine learning approach will allow us to develop an algorithm for suicide risk prediction which can inform clinical practice. The investigative team includes experts in reward learning and model-based imaging (Dombrovski), geriatric neuroimaging (Aizeinstein), behavioral economics (De Bruine), analysis of neural and longitudinal data (Weissfield), decision neuroscience (Clark), neuropsychology of suicide (Keilp) and of geriatric depression (Butters).