Positive valence systems engagement during social reward exposure psychotherapy for mid- and late-life depression

项目概要/摘要 拟议的K23以患者为导向的研究职业发展奖旨在提供 候选人的概念知识和技术技能所需的职业生涯的独立 研究人员专注于个性化过程中行为和神经网络目标的参与， 中晚期抑郁症的心理治疗候选人将在以下地点进行培训和研究活动： 威尔康奈尔老年精神病学研究所的ALACRITY中心。 该提案的前提是确定特定的行为和神经网络目标 可以指导制定具有广泛可扩展性和覆盖面潜力的精简干预措施。“接触”，a 一种针对晚年抑郁症的简化疗法，其主要干预措施是“奖励暴露”，可能会改变 正价系统（PVS）功能。候选人的初步研究表明， PVS结构的静息态功能连接（rsFC）在“参与”期间预测增加的行为 activation.此外，与单独愉快的活动相比， 在“参与”期间，导致行为激活的更大增加和抑郁严重程度的降低。最后， 由候选人进行的机器学习分析表明，低感知社会支持是 心理治疗早期反应不佳的最强预测因素。这些发现与动物和人类的结果一致。 研究证明了社会奖励的保护作用。根据这些观察，候选人 开发了“Engage-S”，这是一个基于社会奖励的“Engage”版本，旨在增加对有意义的 与他人的社会互动。 该培训研究旨在检验“Engage-S”中的社会奖励暴露是否会增强PVS 异常和改善中年和晚年抑郁症。参加者将是60名中年和老年人 成年（年龄≥ 50岁）重度抑郁症患者，随机接受9周“Engage-S”或症状 复习和心理教育（SRP）比较条件。在治疗过程中，我们将检查目标 PVS与rsFC的参与，行为激活量表（BADS），以及在一个新的社会 在基线、5周和9周时的奖励范式。我们将使用计算方法来识别 与治疗反应相关的神经成像和行为特征。 培训计划包括正式课程、结构化教程和动手方法培训 这将提供候选人的知识和技能：1）抑郁症和衰老的功能神经解剖学; 2） 使用功能磁共振成像评估中晚期抑郁症心理治疗期间的目标参与; 3） 用于识别治疗反应的临床和神经影像学预测因子的计算建模， 可用于个性化心理治疗;以及4）为R系列实验生成初步数据 治疗靶点参与应用。

PROJECT SUMMARY/ ABSTRACT The proposed K23 Patient-Oriented Research Career Development Award is designed to provide the candidate with the conceptual knowledge and technical skills needed for a career of an independent investigator focused on the engagement of behavioral and neural network targets during personalized psychotherapies for mid- and late depression. The candidate will conduct her training and research activities at the ALACRITY Center of the Weill Cornell Institute of Geriatric Psychiatry. The proposal is based on the premise that identifying specific behavioral and neural network targets can guide development of streamlined interventions with potential for broad scalability and reach. “Engage”, a streamlined therapy for late-life depression, whose principal intervention is “reward exposure”, may change Positive Valence Systems (PVS) functions. Preliminary studies by the candidate show that early increase in resting state functional connectivity (rsFC) of PVS structures during “Engage” predicts increased behavioral activation. Additionally, compared to solitary pleasant activities, exposure to rewarding social interactions during “Engage” leads to greater increase in behavioral activation and reduction of depression severity. Finally, a machine learning analysis conducted by the candidate showed that low perceived social support is the strongest predictor of poor response early in psychotherapy. These findings are in line with animal and human studies demonstrating the protective role of social rewards. Based on these observations, the candidate developed “Engage-S”, a social-reward based version of “Engage”, aimed to increase exposure to meaningful social interactions with others. The training study proposes to examine whether social reward exposure in “Engage-S” enhances PVS abnormalities and improves mid- and late-life depression. The participants will be 60 middle-aged and older adults (age ≥ 50) with major depression who will be randomized to 9-weeks of “Engage-S” or to a Symptom Review and Psychoeducation (SRP) comparison condition. During treatment, we will examine target engagement of the PVS with rsFC, a behavioral activation scale (BADS), and performance on a novel social reward paradigm at baseline, 5 weeks, and 9 weeks. We will use computational methods to identify neuroimaging and behavioral profiles associated with treatment response. The training plan consists of formal courses, structured tutorials, and hands-on methodological training that will offer the candidate knowledge and skills in: 1) Functional neuroanatomy of depression and aging; 2) Use of fMRI to assess target engagement during psychotherapies for mid- and late-life depression; 3) Computational modeling for the identification of clinical and neuroimaging predictors of treatment response that can be used to personalize psychotherapy; and 4) Generate preliminary data for an R-series experimental therapeutics target engagement application.