Bayesian modeling of mood-driven decision biases for predicting clinical outcome

抑郁症状，包括消极的自我关注的情绪(例如，悲伤、内疚)，会造成创伤-- 暴露的退伍军人易患抑郁症和物质使用障碍(SUD)。两者都有 这些情况反过来又与持续性的执行和功能障碍有关。两者的一个共同标志 抑郁的严重性和成瘾行为是快感缺失，即寻求和体验快乐的能力降低/ 健康的奖励，有证据表明大脑奖励中心的神经活动减少。这样的奖励 因此，敏感性改变可能在促进抑郁症和成瘾性病理中发挥关键作用。 受过创伤的退伍军人。准确理解支持奖赏的神经认知机制-- 因此，需要以决策为基础，以及情绪如何融入这些过程：a) 了解这些风险因素对当前和未来精神病理学的贡献，b)帮助发现和 预测具有如此复杂临床特征的退伍军人的治疗需求/结果。贝叶斯模型可以提供 通过解开对这些复杂机制的强有力的量化描述：a)学习过程 (环境中奖励可能性的预测)，以及b)动作选择策略(选择动作 基于这些预测)。这个计算框架可以帮助更好地描述情绪低落的影响 包括对回报的预测，以及将这些预测整合到决策策略中。此外， 鉴于现有的创伤暴露退伍军人治疗方法侧重于减少焦虑症状，有一种 迫切需要开发能够更彻底地针对抑郁症状的行为治疗方法 与奖赏反应性低有关，如快感缺乏和药物使用。基于计算的 对奖励处理变化的评估对于提供新的治疗可能特别有用和及时 改善创伤暴露退伍军人此类治疗结果的目标和方向。 为了解决这些问题，我们建议使用计算建模和神经成像来识别 近年来精神病理学和行为治疗反应的精确情感神经认知预测因子 部署年轻的(年龄在20-40岁)受创伤的退伍军人。该项目将使用贝叶斯建模应用于 分析基于奖励的决策，与基线相关的大脑回路活动测量源自 功能磁共振成像(FMRI)和对情绪的实验操作，以精确描绘 A)悲伤情绪可能会对指导退伍军人基于奖励的决策的学习或战略调整产生多大影响， B)这些情感驱动的计算偏差与抑郁和问题实质有关的程度 使用此人群中的症状，以及c)这些计算标记可以预测的程度 退伍军人对与创伤相关的抑郁症状的认知行为治疗的反应， 包括内疚和快感缺失。换句话说，计算方法能有效地识别Low 在抑郁的大脑中的奖励敏感性，以及在使用这样的标记对退伍军人进行早期指导时 走向更合适和更有效的治疗方法？这项研究的结果将确定更准确的 奖赏反应、情感临床特征和治疗反应的神经认知预测因子 为为退伍军人制定更有针对性和更有效的干预措施提供认知基础。 CDA-2奖将促进申请者在使用计算和神经成像方面的培训 技巧1)了解情感驱动的认知偏差在促进抑郁和物质方面的作用 使用症状，以及2)预测治疗反应和长期临床病程。这样的培训将反过来 促进申请者的长期职业目标，成为一名多产和成功的退伍军人事务部临床科学家， 其工作重点是改进退伍军人精神病理学的早期发现和有效治疗。

Depressive symptoms, including negative self-focused emotions (e.g., sadness, guilt), make trauma- exposed Veterans vulnerable to developing depressive pathology and substance use disorders (SUD). Both conditions are in turn linked to persistent executive and functional impairments. A common marker of both depression severity and addictive behavior is anhedonia, i.e., reduced ability to seek and experience pleasure/ healthy rewards, with evidence of reduced neural activity in the reward centers of the brain. Such reward sensitivity alterations are thus likely to play a critical role in promoting depressive and addictive pathology in trauma-exposed Veterans. A precise understanding of the neurocognitive mechanisms supporting reward- based decision-making, and how mood is integrated into these processes, is therefore needed to: a) understand the contribution of these risk factors to current and future psychopathology, b) help detect and predict treatment needs/outcomes for Veterans with such complex clinical profiles. Bayesian models can offer a powerful quantitative account of these intricate mechanisms by disentangling: a) learning processes (prediction of reward likelihood in the environment), and b) action selection strategies (choosing an action based on those predictions). This computational framework can help better delineate how low mood impacts both the prediction of rewards, and the integration of these predictions into decision strategies. Moreover, given that existing treatments for trauma-exposed Veterans focus on reducing anxiety symptoms, there is a significant need for developing behavioral treatments that can more thoroughly target depressive symptoms associated with low reward responsiveness, such as anhedonia and substance use. Computationally based assessment of reward processing alterations may be particularly useful and timely for providing new treatment targets and directions for improving such treatment outcomes for trauma-exposed Veterans. To address these questions, we propose to use computational modeling and neuroimaging to identify precise affective neurocognitive predictors of psychopathology and behavioral treatment response in recently deployed young (age 20-40) trauma-exposed Veterans. This project will use Bayesian modeling applied to the analysis of reward-based decisions, with baseline dependent measures of brain circuit activity derived from functional magnetic resonance imaging (fMRI), and experimental manipulation of mood, to delineate precisely a) how sad mood may bias the learning or strategic adjustments guiding reward-based decisions in Veterans, b) the degree to which these affect-driven computational biases relate to depression and problem substance use symptoms in this population, and c) the degree to which these computational markers can predict Veterans' response to a cognitive behavioral treatment targeting depressive symptoms associated with trauma, including guilt and anhedonia. In other words, can computational methods be useful in identifying low reward sensitivity in the depressed brain, and in directing early on Veterans with such markers towards more appropriate and effective treatments? The outcomes of this study will identify more precise neurocognitive predictors of reward responsiveness, affective clinical profile, and treatment response, and provide a cognitive rationale for developing more targeted and effective interventions for Veterans. This CDA-2 award will facilitate the applicant's training in the utilization of computational and neuroimaging techniques 1) to understand the role of affect-driven cognitive biases in promoting depressive and substance use symptoms, and 2) to predict treatment response and long-term clinical course. Such training will in turn facilitate the applicant's long-term career goals to become a productive and successful VA clinical scientist, whose work focuses on improving early detection and effective treatment of psychopathology in Veterans.