Reward Learning in Late-Life Suicidal Behavior

晚年自杀行为的奖励学习

• 批准号: 10576396
• 负责人: Alexandre Y. Dombrovski
• 金额: $ 68.36万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10576396
• 关键词: Address; Affective; Attention; Behavior; Behavioral; Brain Injuries; Brain imaging; Calibration; Clinical; Cognition; Cognitive; Computer Models; Computing Methodologies; Data; Decision Making; Depressed mood; Deterioration; Disease susceptibility; Dorsal; Elderly; Emotional; Entropy; Failure; Feeling suicidal; Functional Magnetic Resonance Imaging; Funding; Heterogeneity; Impaired cognition; Impairment; Individual; Information Management; Intervention; Learning; Life; Maintenance; Mental Depression; National Institute of Mental Health; Neurosciences; Personality; Persons; Populations at Risk; Process; Protocols documentation; Psychological reinforcement; Psychopathology; Recording of previous events; Reproducibility; Research Domain Criteria; Research Personnel; Research Priority; Resources; Rest; Rewards; Risk Management; Sampling; Signal Transduction; Statistical Methods; Study models; Suicide; Suicide attempt; Testing; Theoretical model; Thinking; Time; Uncertainty; Vision; Vulnerable Populations; Work; cognitive control; computer framework; design; experience; experimental study; forgetting; imaging modality; imaging study; information model; information processing; mathematical model; neural; neural circuit; neuroimaging; neuromechanism; novel; pressure; response; suicidal; suicidal behavior; suicidal individual; suicidal risk; suicide attempter; suicide model; suicide rate; suicide victim; theories

ABSTRACT The US is facing rising suicide rates. Yet, we have only a limited understanding of why some people, but not others, progress from contemplating to attempting suicide. In the past funding period, we have shown that depressed older adults whose decision-making is impaired are more likely to progress from suicidal ideation to action. Specifically, using decision experiments, computational modeling, and fMRI, we have found replicable deficits in learning and choice processes paralleled by altered ventromedial and dorsolateral prefrontal abstract learning signals. In this renewal application, we propose to extend these findings by examining how people at risk for suicide make decisions under cognitive and emotional demands that are more representative of the suicidal crisis. In our computational framework these demands include (i) a high information load and (ii) constraints on information processing imposed by time pressure and impending threats. We have developed and validated new experimental and computational methods for studying information-processing bottlenecks during decision-making. Specifically, our reinforcement learning computational model applied to behavioral and neuroimaging data, enables us to examine how people use their limited neurocomputational resources to make good decisions under high information load. Our preliminary studies show that decision-making in this context (i) relies on resource-rational strategies for managing information load, (ii) is subserved by dorsal attention and cingulo-opercular networks, (iii) is likely disrupted in attempted suicide, (iv) a deficit paralleled by abnormal dorsal attention network responses to information load. We thus propose to test the general hypothesis that people at risk for suicide are prone to information-processing bottlenecks arising from alterations in these cortical networks. We will perform decision experiments and cognitive computational models (Aim 1) in a discovery sample and a non-overlapping replication sample (n = 200 each) to ensure that findings are robust to the clinical and cognitive heterogeneity of suicidal behavior. Both samples will include individuals maximally representative of suicide victims, namely older depressed suicide attempters, about half of whom survived near-lethal attempts. Functional neuroimaging experiments manipulating information load will interrogate the neurocomputational dynamics of the dorsal attention network and cingulo-opercular network during decision- making in one sample (n = 200, Aim 2). A careful characterization of psychopathology, personality, cognition, psychotropic exposure and brain damage from suicide attempts will allow us to control for key confounds. The interdisciplinary team has expertise in mechanisms of suicidal behavior (Dombrovski), decision neuroscience (Dombrovski, McGuire, Hallquist), imaging methods (Hallquist), and suicide risk management (Szanto, Dombrovski). This work aligns with a key objective of the NIMH’s prioritized research agenda on suicide: “to identify cognitive dysfunction/neural circuitry profiles … associated with suicide risk” and taps into the reinforcement learning and limited capacity constructs of the RDoC framework.

摘要 美国正面临自杀率上升的问题。然而，我们只能有限地理解为什么有些人，而不是 其他人，从考虑自杀到企图自杀的过程。在过去的资助期，我们已经表明 决策能力受损的抑郁老年人更有可能从自杀念头进展到 行动。具体地说，利用决策实验、计算建模和功能磁共振成像，我们发现了可复制的 前额叶腹内侧和背外侧抽象改变与学习和选择过程的障碍平行 学习信号。在这份续签申请中，我们建议通过研究人们如何在 自杀风险在认知和情感需求下做出决定，这些需求更能代表 自杀危机。在我们的计算框架中，这些需求包括(I)高信息量和(Ii) 时间压力和迫在眉睫的威胁对信息处理造成的限制。我们已经开发出 并验证了研究信息处理瓶颈的新的实验和计算方法 在决策过程中。具体地说，我们的强化学习计算模型应用于行为和 神经成像数据，使我们能够检查人们如何使用他们有限的神经计算资源来 高信息负荷下的正确决策。我们的初步研究表明，在这种情况下的决策 (I)依赖资源理性策略来管理信息负荷，(Ii)以背部注意为辅，以及 扣带-顶叶网络，(Iii)自杀未遂可能中断，(Iv)异常 背侧注意网络对信息负荷的反应。因此，我们建议检验一般假设，即 有自杀风险的人容易因为这些基因的改变而出现信息处理瓶颈 大脑皮层网络。我们将进行决策实验和认知计算模型(目标1)。 发现样本和非重叠复制样本(n=200)，以确保结果对 自杀行为的临床和认知异质性。这两个样本将最大限度地包括个人 自杀受害者的代表，即老年抑郁症自杀未遂者，其中约一半幸存下来 近乎致命的尝试。操纵信息负荷的功能神经成像实验将询问 决策过程中背侧注意网络和扣带盖网络的神经计算动力学 在一个样本中制作(n=200，目标2)。对精神病理，个性，认知， 精神药物的接触和自杀企图造成的脑损伤将使我们能够控制关键的混淆。这个 跨学科团队在自杀行为机制(东布罗夫斯基)、决策神经科学方面拥有专业知识 (Dombrovski，McGuire，Hallquist)、成像方法(Hallquist)和自杀风险管理(Szanto， 东布罗夫斯基)。这项工作符合美国国立卫生研究院关于自杀的优先研究议程的一个关键目标： 识别认知功能障碍/神经回路特征…与自杀风险有关“，并利用 强化学习和RDoC框架的有限能力构建。