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

中晚年抑郁症社会奖励暴露心理治疗期间的正价系统参与

• 批准号: 10417136
• 负责人: Nili Solomonov
• 金额: $ 19.11万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10417136
• 关键词: Addictive Behavior; Age; Aging; Animals; Behavioral; Clinical; Computer Models; Computing Methodologies; Cues; Data; Development; Elderly; Exposure to; Functional Magnetic Resonance Imaging; Future; Generations; Geriatric Psychiatry; Human; Institutes; Intervention; Investigational Therapies; K-Series Research Career Programs; Knowledge; Lead; Machine Learning; Major Depressive Disorder; Mental Depression; Methodology; Modeling; National Institute of Mental Health; Neuroanatomy; Neurobiology; Participant; Performance; Pilot Projects; Positive Valence; Prediction of Response to Therapy; Psychotherapy; Randomized; Research Activity; Research Personnel; Rest; Rewards; Risk; Role; Series; Severities; Social Interaction; Social support; Structure; Symptoms; System; Technical Expertise; Testing; Training; Training Activity; base; biosignature; career; career development; depressive symptoms; design; evidence base; financial incentive; geriatric depression; geriatric major depression; improved; improved functioning; machine learning model; middle age; neural network; neuroimaging; novel; patient oriented research; patient subsets; psychoeducation; random forest; response; reward circuitry; skills; social; theories; therapeutic target; treatment response

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.

项目摘要/摘要 拟议的K23面向患者的研究职业发展奖旨在提供 具有独立职业所需的概念知识和技术技能的候选人 调查人员专注于个性化期间行为和神经网络目标的参与 中期和晚期抑郁症的心理治疗。候选人将在 威尔·康奈尔（Weill Cornell）老年精神病学研究所的酸化中心。 该提案基于以下前提：识别特定的行为和神经网络目标 可以指导开发流线型干预措施，并具有广泛的可伸缩性和触及范围。 “参与”，a 对晚期抑郁症的简化治疗，其主要干预是“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，早期增加 在“参与”预测期间，PVS结构的静止状态功能连通性（RSFC）增加了行为 激活。此外，与扎实的愉快活动相比，接触奖励社交互动 在“参与”期间，行为激活和抑郁严重程度的减少会增加。最后， 候选人进行的机器学习分析表明，感知到低的社会支持是 心理治疗早期反应不良的最强烈预测指标。这些发现与动物和人类一致 研究证明了社会奖励的保护作用。基于这些观察，候选人 开发了基于社会奖励的“参与”的“ Anging-S”，旨在增加对有意义的接触 与他人的社交互动。 培训研究的建议旨在检查“参与者”中的社会奖励暴露是否增强了PVS 异常并改善了中后期抑郁症。参与者将是60名中年和年龄较大的 大抑郁症的成人（≥50岁）将被随机分为9周的“参与者”或症状 审查和心理教育（SRP）比较条件。在治疗期间，我们将检查目标 PVS与RSFC的参与，行为激活量表（坏）以及在新颖的社交上的表现 基线，5周和9周的奖励范式。我们将使用计算方法来识别 与治疗反应相关的神经影像学和行为特征。 培训计划包括正式课程，结构化教程和动手培训 这将提供：1）抑郁和衰老的功能性神经解剖学； 2） 使用fMRI评估心理治疗中期和晚期抑郁症的目标参与； 3） 用于鉴定临床和神经影像学预测因素的计算模型，治疗反应的预测指标 可用于个性化心理治疗； 4）生成R系列实验的初步数据 治疗剂目标参与申请。