Motivational anhedonia: neurocomputational mechanisms and temporal dynamics

精神障碍及其相关神经生物学机制的识别 症状对于提高诊断能力和治疗效果至关重要。人们普遍认为 当前诊断类别中普遍存在的异质性阻碍了有效和可靠的识别 生物标志物，并且人们越来越认识到症状本身是异质的。这一点特别 对于快感缺失来说也是如此，这是抑郁症、精神分裂症、物质障碍的常见症状 依赖性和创伤后应激障碍。快感缺失是一种多方面的症状领域，已知涉及动机、 预期成分和享乐成分。重要的是，快感缺失的这些不同方面似乎具有不同的特点 神经生物学机制，改善该症状群的诊断和治疗取决于 开发工具以更好地评估和分类快感缺失亚型。我们组此前曾 假设“动机性快感缺失”可能是快感缺失的一个亚型，具有独特的神经生物学特征： 与没有动机症状的快感缺乏相比。 F32 研究金提案的重点是隔离 定义动机性快感缺失亚型的选择性行为和神经标记。为了实现这一点 目标，我们将（1）将申请人开发的新计算建模框架应用于良好的 建立基于努力的决策衡量标准，以更准确地表征个人努力敏感性 并识别有或没有动机快感缺失的个体； (2) 使用基于模型的分析和 功能成像来验证动机快感缺乏子域并识别动机的生物标志物 快感缺失； (3) 采用纵向抽样方法（生态瞬时评估；EMA）来检验 随着时间的推移，快感缺乏的症状如何有助于受试者在日常生活中进行努力的活动。 完成本提案中概述的项目后，我们将更好地了解神经生物学 区分动机性快感缺乏亚型和这两种亚型的机制和症状关系 健康对照和非动机性快感缺乏亚型。此外，该奖学金将使 候选人学习如何将她的计算建模背景与临床研究、功能性研究相结合 神经影像学和 EMA 方法。如果成功，这项工作将带来更高的诊断精度（即 临床环境中的症状分类）并促进个性化治疗计划的制定 快感缺乏的患者。

The identification of neurobiological mechanisms underlying mental disorders and associated symptoms is critical for improving diagnostic ability and treatment efficacy. It is widely accepted that the prevalence of heterogeneity within current diagnostic categories hinders the identification of valid and reliable biomarkers, and is increasingly appreciated that symptoms themselves are heterogeneous. This is particularly true for anhedonia, which is a common symptom in the disorders of depression, schizophrenia, substance dependence and PTSD. Anhedonia is a multi-faceted symptom domain that is known to involve motivational, anticipatory and hedonic components. Importantly, these different aspects of anhedonia appear to have distinct neurobiological mechanisms, and improving diagnosis and treatment for this symptom cluster depends on developing tools for better assessment and classification of anhedonic subtypes. Our group has previously hypothesized that “motivational anhedonia” may be a subtype of anhedonia with distinct neurobiology as compared to anhedonia without motivational symptoms. The focus of this F32 fellowship proposal is to isolate selective behavioral and neural markers that define the motivational anhedonia subtype. To accomplish this goal, we will (1) apply a new computational modeling framework developed by the applicant to a well- established measure of effort-based decision-making to more precisely characterize individual effort sensitivity and to identify individuals with and without motivational anhedonia; (2) use model-based analysis and functional imaging to validate a motivational anhedonia sub-domain and to identify biomarkers of motivational anhedonia; and (3) use longitudinal sampling methods (Ecological Momentary Assessment; EMA) to examine how symptoms of anhedonia contribute to engagement in effortful activity in daily life within-subjects, over time. Upon completion of the project outlined in this proposal, we will better understand the neurobiological mechanisms and symptom relationships that differentiate the motivational anhedonia subtype from both healthy controls and non-motivational anhedonia subtypes. Additionally, this fellowship will enable the candidate to learn how to integrate her computational modeling background with clinical research, functional neuroimaging, and EMA methods. If successful, this work will lead to greater diagnostic precision (i.e. symptom classification in clinical settings) and facilitate the development of personalized treatment plans for anhedonic patients.