Motivational anhedonia: neurocomputational mechanisms and temporal dynamics

精神障碍的神经生物学机制及其相关研究进展 症状是提高诊断能力和治疗效果的关键。人们普遍认为， 当前诊断类别中的异质性普遍阻碍了有效和可靠的识别 生物标志物，并越来越认识到症状本身是不同的。这是特别的 对于快感缺乏来说是真的，这是抑郁症，精神分裂症，物质障碍的常见症状 依赖和创伤后应激障碍。快感缺乏症是一个多方面的症状领域，已知涉及动机， 预见性和享乐性成分。重要的是，快感缺乏症的这些不同方面似乎有截然不同的 神经生物学机制，以及改善对此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非享乐性亚型。我们小组之前已经 假设“激励性快感缺乏症”可能是快感缺乏症的一种亚型，具有明显的神经生物学特征 与没有动机症状的快感缺乏相比。这项F32团契提案的重点是孤立 定义激励性快感缺乏症亚型的选择性行为和神经标志物。要做到这一点 目标，我们将(1)将申请人开发的新的计算建模框架应用于油井- 建立基于努力的决策措施，以更准确地表征个人努力敏感度 以及识别患有和不患有激励性快感缺乏的个体；(2)使用基于模型的分析和 功能成像验证激励性快感缺乏症亚域并识别激励性生物标记物 快感缺乏；(3)采用纵向抽样法(生态瞬时评估；EMA)检查 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艰苦的活动。 在完成这项建议中概述的项目后，我们将更好地了解神经生物学 激励性快感缺乏症亚型与两者区别的机制和症状关系 健康对照和非激励性快感缺乏症亚型。此外，这项奖学金将使 应聘者学习如何将她的计算建模背景与临床研究、功能 神经成像和EMA方法。如果成功，这项工作将导致更高的诊断精度(即 临床环境中的症状分类)，并促进制定个性化治疗计划 无享乐症患者。

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.