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

抑郁症状，包括消极的自我关注情绪（例如，悲伤，内疚），使创伤- 暴露退伍军人容易发展抑郁症和物质使用障碍（SUD）。两 这些条件反过来又与持续的执行和功能障碍有关。两者的共同标志 抑郁严重程度和成瘾行为是快感缺乏，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。此类奖励 因此，敏感性改变可能在促进抑郁和成瘾病理学方面发挥关键作用， 受过创伤的退伍军人对支持奖励的神经认知机制的精确理解- 基于决策，以及如何情绪融入这些过程，因此需要：a） 了解这些风险因素对当前和未来精神病理学的贡献，B）帮助检测和 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 一个强大的定量说明这些错综复杂的机制，通过解开：a）学习过程 （预测环境中的回报可能性），以及B）动作选择策略（选择动作 根据这些预测）。这个计算框架可以帮助更好地描述情绪低落如何影响 对回报的预测，以及将这些预测整合到决策策略中。此外，委员会认为， 鉴于现有的创伤暴露退伍军人的治疗集中在减少焦虑症状，有一个 开发行为治疗的重大需求，可以更彻底地针对抑郁症状 与低回报反应有关，如快感缺乏和物质使用。基于计算 奖励处理变更的评估对于提供新的处理可能特别有用和及时 目标和方向，以改善这种治疗结果的创伤暴露退伍军人。 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 最近，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 部署的年轻（20-40岁）创伤暴露退伍军人。该项目将使用贝叶斯建模应用于 分析基于奖励的决策，并对大脑回路活动进行基线依赖性测量， 功能性磁共振成像（fMRI）和情绪的实验操作，以精确描绘 a）悲伤情绪如何可能会使退伍军人的学习或战略调整产生偏差， B）这些影响驱动的计算偏差与抑郁和问题物质的关系程度 在这个人群中使用症状，以及c）这些计算标记可以预测的程度 退伍军人对针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，计算方法在识别低 奖励敏感性在抑郁的大脑，并在指导早期的退伍军人与这些标记 更合适更有效的治疗方法这项研究的结果将确定更精确的 奖励反应、情感临床特征和治疗反应的神经认知预测因子，以及 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理论基础。 该CDA-2奖项将促进申请人在利用计算和神经成像方面的培训 技术1）了解情感驱动的认知偏见在促进抑郁和物质方面的作用 使用症状，和2）预测治疗反应和长期临床过程。这样的训练反过来 促进申请人的长期职业目标，成为一个富有成效和成功的VA临床科学家， 其工作重点是提高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.