Generating pro-resilient states through individualized circuit read-write therapeutics

通过个性化电路读写疗法产生有弹性的状态

• 批准号: 10002574
• 负责人: Annegret Lea Falkner
• 金额: $ 243万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-07 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10002574
• 关键词: Anxiety Disorders; Behavior; Behavior Therapy; Behavioral; Brain; Calcium; Chronic; Data; Deep Brain Stimulation; Depressed mood; Detection; Disease; Event; Goals; Health; Individual; Intervention; Life; Machine Learning; Major Depressive Disorder; Mental Depression; Mental disorders; Outcome; Pattern; Pharmaceutical Preparations; Phase; Population; Resistance; Social Behavior; Stress; Testing; Therapeutic; Time; United States; Writing; base; cost; neural circuit; novel; prevent; protective effect; reinforced behavior; relating to nervous system; resilience; resilient behavior; restorative treatment; social stress

Project Summary Major depressive disorder (MDD) and associated anxiety disorders are the most prevalent and costly mental illnesses in the United States, with health spending on treatment recently exceeding $71 billion per year. It is now well established that MDD represents a spectrum of disorders, but current drug based approaches to treatment are temporally nonselective, and their efficacy varies highly across individuals. In this proposal, we explore a ​novel individualized intervention strategy,​ wherein we aim to prevent and reverse MDD through closed-loop behavioral and neural circuit “tuning”. While some individuals develop MDD as a result of a stressful life event, other individuals appear more resilient to stress-induced depression. Our goal in this proposal is to leverage recent advances in machine learning to identify and detect specific pro-resilient behaviors and patterns of activation in resilient individuals, and then use these data to “steer” susceptible individuals into pro-resilient states. We will accomplish this in two phases. In the first phase, we will test whether modification of behavior alone can generate a pro-resilient state. We will take a novel quantitative approach to behavior analysis, using machine learning to identify specific micro behaviors that are unique to resilient individuals during a chronic social stress. Then, to test whether promoting these behaviors can provide depression-protective effects, we will then use a closed-loop strategy to detect ongoing behavior, and reinforce identified pro-resilient micro behaviors. Second, we will perform circuit-wide calcium recordings in the brain’s subcortical social behavior network and perform unsupervised detection of pro-resilience circuit motifs across the population. We will then use a novel closed-loop read-write strategy to optogentically “tune” the circuit dynamics to mimic these pro-resilient states. We will further explore how these interventions can be accomplished at various time points relative to a stressful life event (before, during, and after) to test whether circuit intervention can potentially provide protective or restorative treatment. These data can potentially be used to develop novel behavior-based therapies for MDD, or to significantly refine the current use of deep-brain stimulation in order to generate pro-resilient states.

项目摘要 重度抑郁症（MDD）和相关的焦虑症是最普遍的， 美国治疗费用高昂的精神疾病，最近用于治疗的医疗支出超过71美元 每年10亿。现在已经确定MDD代表一系列疾病，但目前 基于药物的治疗方法在时间上是非选择性的，它们的疗效在不同的地区差异很大。 个体在这个建议中，我们探索了一种新的个性化干预策略，我们的目标是 通过闭环行为和神经回路“调整”来预防和逆转MDD。 虽然有些人发展MDD作为一个有压力的生活事件的结果，其他人 对压力引起的抑郁症更有弹性。我们在此提案中的目标是利用最近 机器学习的进步，以识别和检测特定的亲弹性行为和模式， 激活弹性个体，然后使用这些数据来“引导”易感个体进入 支持恢复力的国家 我们将分两个阶段完成这项工作。在第一阶段，我们将测试是否修改 行为本身就能产生一种有利于恢复的状态。我们将采取一种新的定量方法， 行为分析，使用机器学习来识别特定的微观行为， 在长期的社会压力下恢复的个体。然后，为了测试促进这些行为是否可以 提供抑郁保护作用，然后我们将使用闭环策略来检测正在进行的 行为，并加强识别的亲弹性微观行为。第二，我们将在整个电路范围内执行 大脑皮层下社会行为网络中的钙记录， 在整个人群中检测亲弹性电路图案。然后我们将使用一个新的闭环 读-写策略来光遗传地“调谐”电路动态以模仿这些亲弹性状态。 我们将进一步探讨这些干预措施如何在相对于一个时间点的不同时间点完成。 压力生活事件（之前，期间和之后），以测试电路干预是否可能提供 保护或恢复治疗。 这些数据可能用于开发新的基于行为的MDD疗法，或 显著改进目前使用的脑深部电刺激，以产生有利于恢复的状态。