Targeting Network Dysfunction in Apathy of Late-life Depression Using Digital Therapeutics

使用数字疗法针对晚年抑郁症冷漠中的网络功能障碍

基本信息

批准号：
10591712
负责人：
Lauren Elizabeth Oberlin
金额：
$ 19.48万
依托单位：
WEILL MEDICAL COLL OF CORNELL UNIV
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-01 至 2028-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10591712
关键词：
Adult Aging Antidepressive Agents Attention Behavior Behavioral Brain Caregiver Burden Caring Clinical Clinical Trials Cognitive Cognitive deficits Consultations Control Groups Data Depressed mood Development Disease Disease remission Educational Intervention Elderly Emotional Evaluation Exercise Expectancy Functional Magnetic Resonance Imaging Functional disorder Future Generations Goals Health Home Impaired cognition Impairment Insula of Reil Intervention Investigational Therapies K-Series Research Career Programs Knowledge Major Depressive Disorder Medical Mental Depression Modeling Mood Disorders Moods Morbidity - disease rate Motivation National Institute of Mental Health Network-based Neurosciences Nucleus Accumbens Outcome Participant Patients Performance Persons Phenotype Population Prefrontal Cortex Process Public Health Quality of life Randomized Randomized, Controlled Trials Research Research Personnel Rest Rewards Sampling Series Severities Structure Subgroup Symptoms Syndrome Technical Expertise Training Training Programs Translating Video Games Withdrawal affective disturbance alternative treatment arm attentional control career chronic depression clinical efficacy clinically significant cognitive benefits cognitive control cognitive process cognitive training computerized depressed patient depressive symptoms design digital digital delivery digital treatment disability efficacious treatment executive function functional decline functional disability functional outcomes geriatric depression high risk hospitalization rates improved innovation motivated behavior neglect network dysfunction neuroimaging non-demented novel novel therapeutics patient engagement personalized medicine post intervention predicting response primary outcome reduce symptoms remote delivery response secondary outcome sedentary lifestyle skills social support network sustained attention theories therapeutic target training project treatment strategy virtual delivery web platform white matter

项目摘要

PROJECT SUMMARY/ABSTRACT This K23 Career Development Award is designed to provide the conceptual knowledge and technical skills necessary for the PI to pursue a career as an independent investigator evaluating brain network targets to optimize non-pharmacologic interventions for difficult-to-treat mood and cognitive disturbances in aging. Apathy is a persistent and disabling disorder of motivation that afflicts 30-50% of patients with late-life depression (LLD). Apathy worsens clinical outcomes in LLD, as apathetic depressed patients have poor response to existing treatments and higher rates of hospitalization, functional decline, and increased caregiver burden. Functional network abnormalities in a core set of brain circuits that interact to support motivated behavior (salience, executive control, and reward networks) may underlie apathy of LLD. Options for the efficacious treatment of apathy of LLD are limited, and novel interventions are urgently needed. This proposal is based on the premise that customizing interventions based on theorized brain mechanisms of apathy may increase the potency and scalability of promising therapies. This K23 proposes to conduct a randomized controlled trial evaluating the potential of a customized digital cognitive training (DCT) intervention to target brain network abnormalities in apathy of LLD and reduce symptoms of apathy and related cognitive and behavioral deficits. Eighty-four older adults with major depressive disorder and clinically significant apathy will be randomized to four weeks of a home-based DCT intervention or an active expectancy-matched control. The DCT will involve a suite of exercises designed to engage brain networks disrupted in apathy by targeting cognitive processes (i.e., salience processing, sustained attention, cognitive control) supported by these networks. Aligned with NIMH’s experimental therapeutics approach, we will examine the extent to which a targeted DCT program modulates functional network disturbances in apathy of LLD and improves apathy and associated cognitive deficits. The proposed study will generate preliminary data regarding network targets of a customized digital therapeutic that may translate to mood and cognitive benefits, and may guide future personalization of treatments for motivational disturbances in older adulthood. The training provided through this K23 will advance my knowledge and skillsets in: 1) the design and delivery of a neuroscience-informed, virtually-delivered intervention targeting a distinct subtype of depressed older adults; 2) use of functional MRI to probe mechanisms of action of the proposed DCT; 3) advanced longitudinal modeling of clinical trial data to assess trajectories of change and identify predictors of response and; 4) generating an R-series experimental therapeutics target engagement application. This training will prepare me for an independent research career developing and refining neuroscience-based interventions targeting traditionally intractable and functionally impairing mood and cognitive disturbances in older adulthood.

项目概要/摘要 K23 职业发展奖旨在提供概念知识和技术 PI 从事评估大脑网络目标的独立调查员职业所需的技能优化非药物干预措施，以治疗老年中难以治疗的情绪和认知障碍。冷漠是一种持续性的、致残的动机障碍，困扰着 30-50% 的晚年患者冷漠会使 LLD 的临床结果恶化，因为冷漠的抑郁症患者的预后较差。对现有治疗的反应以及更高的住院率、功能衰退和护理人员的增加一组核心大脑回路中的功能网络异常，这些回路相互作用以支持动机。行为（显着性、执行控制和奖励网络）可能是 LLD 选项冷漠的基础。 LLD 冷漠的有效治疗方法有限，迫切需要新的干预措施。该提议的前提是基于理论化的大脑定制干预措施 K23 提出，冷漠机制可能会增加有前景的疗法的效力和可扩展性。进行随机对照试验，评估定制数字认知训练 (DCT) 的潜力针对 LLD 冷漠的大脑网络异常进行干预，并减少冷漠和相关症状 84 名患有重度抑郁症的老年人和临床上的认知和行为缺陷。显着的冷漠将被随机分配到为期四个星期的家庭 DCT 干预或积极的 DCT 将涉及一系列旨在调动大脑网络的练习。通过针对认知过程（即显着性处理、持续注意力、认知在这些网络的支持下，我们将与 NIMH 的实验治疗方法保持一致。检查目标 DCT 程序在冷漠中调节功能网络干扰的程度 LLD 并改善冷漠和相关的认知缺陷。拟议的研究将产生初步数据。关于可能转化为情绪和认知益处的定制数字治疗的网络目标，并可能指导未来针对老年人动机障碍的个性化治疗。通过 K23 提供的培训将提高我在以下方面的知识和技能：1）设计和针对抑郁症的独特亚型提供基于神经科学的、虚拟的干预措施老年人；2) 使用功能性 MRI 来探讨所提出的 DCT 的作用机制；临床试验数据的纵向建模，以评估变化轨迹并确定反应预测因素 4) 生成 R 系列实验治疗目标参与应用程序。为我的独立研究生涯做好准备，开发和完善基于神经科学的干预措施针对老年人中传统上棘手且功能受损的情绪和认知障碍。