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

项目摘要 严重抑郁障碍(MDD)和相关的焦虑症是最常见的 美国昂贵的精神疾病，最近用于治疗的医疗支出超过71美元 每年10亿美元。现在公认的是，MDD代表了一系列疾病，但目前 以药物为基础的治疗方法在时间上是非选择性的，其疗效在不同的地区差异很大。 个人。在这个方案中，我们探索了一种新的​个性化干预策略，​，其中我们的目标是 通过行为和神经回路的闭环“调节”来预防和逆转MDD。 虽然一些人会因为生活中的压力事件而患上MDD，但另一些人 对压力引起的抑郁表现出更强的韧性。我们在这项提案中的目标是利用最近的 机器学习在识别和检测特定的亲弹性行为和模式方面的进展 在有弹性的个体中激活，然后使用这些数据来“引导”易感个体 支持弹性的国家。 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了 单是行为本身就能产生一种积极的弹性状态。我们将采用一种新的量化方法来 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有韧性的人。然后，为了测试促进这些行为是否可以 提供抑郁保护效果，然后我们将使用闭环策略来检测正在进行的 行为，并加强已确定的具有弹性的微观行为。第二，我们将在全电路范围内表演 大脑皮质下社会行为网络中的钙记录和无人监督的执行 在整个种群中检测到支持弹性的回路模体。然后我们将使用一种新颖的闭环系统 读写策略，以光发电“调整”电路动力学，以模拟这些具有弹性的状态。 我们将进一步探讨如何在不同的时间点完成这些干预措施 应激性生活事件(之前、期间和之后)，以测试电路干预是否可能提供 保护性或恢复性治疗。 这些数据可能被用来开发新的基于行为的MDD疗法，或者 显著改进目前脑深部刺激的使用，以产生有利于恢复的状态。

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.