Cortical oscillatory dynamics and decision deficits in suicidal behavior - Resubmission

尽管进行了大量的研究工作，但在理解和预防自杀方面进展缓慢。 这至少在一定程度上是由于当前自杀理论的模糊性，这些理论依赖于所有人的言语描述 结构和随后的预测，从而提供了相关的而不是机械的解释 自杀行为(某人)。相比之下，以学习理论和决策为基础的正式理论框架 神经科学，如强化学习(RL)，提供了一种有效的方法来揭示认知和 通过使用明确的数学概念来定义结构，涉及自杀的决策过程， 假设、预测和感兴趣的假设。此外，当面临危机时，从 考虑试图自杀涉及到在认知需求增加的情况下做出决定的需要 高信息负荷(多个决策选项)、不确定性和时间压力(当前危机)。 然而，尽管现实生活中的决策是动态的，并受到情景需求的影响，但现有的 自杀素质的神经模型是相对静态的，尚未与决策的进步相结合。 神经科学。相比之下，电生理学与计算建模相结合可以揭示这种动态 试验内神经事件的展开，非常适合回答关于皮质的机械性问题 作为实时决策基础的震荡。这个项目将在实验中操纵认知 使用具有验证的计算模型的RL任务来检查决策的决策需求 SB中的进程(目标1)。它还将建立在基本电生理学文献的基础上，这些文献涉及α，β， 通过检测强化的神经编码，工作记忆和认知控制中的theta振荡 自杀未遂的历史(目标2)。探索性目标3将允许初步审查 自我报告情绪、压力和与任务相关的神经活动对行为的影响。参与者为120名成年人(40人 自杀未遂者、40名自杀意念者和40名健康对照)招募自住院、门诊和 社区设置。专家指导团队将提供行为和计算建模方面的培训， 基础神经科学和电生理学。这项工作将在纽约大学精神病学系进行。 匹兹堡大学在支持其初级教师的职业发展方面有着长期的记录。 这个K23将通过以下方式促进候选人的长期目标：为自杀研究和预防做出贡献 确定某人决策缺陷的神经计算机制。项目始终如一 NIMH的目标和资金优先事项，并建立在候选人之前由NIMH资助的RDoC-知情的基础上 工作。它也属于NIMH计算精神病学计划的几个重点领域 用来自临床的实验数据通知模型用于临床研究的基本计算模型 了解神经元活动的个体差异如何有助于传递和 中枢神经系统的病理生理信息的处理“)。

Despite significant research efforts, the progress in understanding and prevention of suicide has been slow. This is due at least in part to the vagueness of current suicide theories that rely on verbal specifications of all constructs and consequent predictions and thus provide correlative rather than mechanistic accounts of suicidal behavior (SB). In contrast, formal theoretical frameworks grounded in learning theory and decision neuroscience, such as reinforcement learning (RL), afford a productive approach for uncovering cognitive and decision processes involved in suicide through the use of explicit mathematical ideas to define constructs, hypotheses, predictions, and assumptions of interest. Further, when faced with a crisis, a transition from thinking about to attempting suicide involves the need to make a decision under increased cognitive demands imposed by high information load (multiple decision options), uncertainty, and time pressure (current crisis). However, although real-life decision-making is dynamic and is influenced by situational demands, existing neural models of suicidal diathesis are relatively static and not yet integrated with the advances in decision neuroscience. In contrast, electrophysiology augmented with computational modeling can reveal the dynamic unfolding of within-trial neural events and is well suited for answering mechanistic questions about cortical oscillations that underlie real-time decision-making. This project will experimentally manipulate cognitive demands on decision-making using an RL task with a validated computational model to examine decision processes in SB (Aim 1). It will also build on the basic electrophysiology literature that implicates alpha, beta, and theta oscillations in working memory and cognitive control by examining neural encoding of reinforcement history in attempted suicide (Aim 2). Exploratory Aim 3 will allow for a preliminary examination of the effects of self-reported mood, stress, and task-related neural activity on behavior. Participants will be 120 adults (40 suicide attempters, 40 suicide ideators, and 40 healthy controls) recruited from inpatient, outpatient, and community settings. Expert mentorship team will provide training in behavioral and computational modeling, basic neuroscience, and electrophysiology. The work will take place at the Department of Psychiatry of the University of Pittsburgh, which has a long track record of supporting career development of its junior faculty. This K23 will promote the Candidate’s long-term goals of contributing to suicide research and prevention by identifying the neurocomputational mechanisms of deficient decision-making in SB. The project is consistent with the NIMH’s goals and funding priorities and builds on the Candidate’s prior NIMH-funded RDoC-informed work. It also falls into several areas of emphasis for NIMH’s Computational Psychiatry Program (“translating basic computational models into clinical research by informing the models with experimental data from clinical population,” “understanding how individual differences in neuronal activity contribute to the transmission and processing of pathophysiological information in the central nervous system”).