Robust Predictors of Mania and Psychosis

项目摘要/摘要 新的融资机会公告的目的，RFA-OD-17-004，用于密集 健康行为的纵向分析：利用新技术了解健康行为 (U01)，是建立一个合作协议网络，以协作研究影响关键健康的因素 个人在动态环境中的行为，使用密集的纵向数据收集和分析 方法：研究方法。重要的是，在将大脑的知识转化为新的和 更有效地治疗人类大脑疾病，如严重的精神障碍。事实上，严重的精神疾病 障碍，包括精神障碍，是一种大脑疾病，不仅是毁灭性的，因为他们 会导致在生命早期发生的严重干扰，但对许多人来说，病程是进行性的，导致 慢性衰弱和早期死亡。因此，需要加快对触发因素的了解 (或增加或降低)躁狂和精神病发作的可能性，并将这种知识转化为 更有效的治疗干预措施至关重要。拟议的“稳健预测指标”的主要目标是 躁狂和精神错乱“是指找出引发危险的生物、环境和社会因素 精神状态，尤其是躁狂症和精神病，在已知处于这些疾病的风险中的个人。这个 这项工作的最终目标是提供可量化和可预测的信息，这些信息可以用来搭建 生物学观察和量身定做的干预策略，以最大限度地提高个人层面的疗效。我们首先 开发模型来预测精神病和躁狂症的传统临床措施，使用(1)数字，低成本 通过智能手机和可穿戴设备实现最低负担交互(目标1)，以及(2)从 面对面临床互动中的人脸和语音(目标2)，这项工作利用了我们现有的数据 已经收好了。接下来，我们将收集100人年的伪连续多变量行为 来自100名精神障碍患者的数据，以进一步检验和验证我们早期的观察 在更广泛的情感性和非情感性精神障碍患者中，他们可能会经历 一年时间内的疾病波动，采用多种策略来优化参与者 参与(目标3)。作为一个有代表性的例子，我们还将进行一项研究，将睡眠、能量 支出和躁狂症状随着时间的推移，使用在前三个目标中获得的数据，来量化如何 能量消耗和能量感知之间的关系在我们的研究人群中以不同的方式存在 可能对健康行为产生重要后果(目标4)。因此，该项目的主要目标是 获得高质量、时间密集的行为、认知和临床数据 成年患者，不仅是为了方便未来的调查，这些行为变化指向 神经生物学过程，也是更有效、更有针对性的治疗方法的先驱，例如实时 可根据个人环境中的动态因素提供的干预措施。

PROJECT SUMMARY/ABSTRACT The purpose of the new funding opportunity announcement, RFA-OD-17-004 for Intensive Longitudinal Analysis of Health Behaviors: Leveraging New Technologies To Understand Health Behaviors (U01), is to establish a cooperative agreement network to collaboratively study factors that influence key health behaviors in the dynamic environment of individuals, using intensive longitudinal data collection and analytic methods. Importantly, progress has been slow and frustrating in translating knowledge of the brain to new and more effective treatments for human brain diseases such as severe mental disorders. In fact, severe mental disorders, which include psychotic disorders, are brain diseases that are not only devastating because they result in severe disruptions that occur early in life, but, for many, the course of illness is progressive, leading to chronic debilitation and early mortality. Thus the need to accelerate knowledge about the factors that trigger (or increase or decrease the likelihood) of manic and psychotic episodes, and to translate this knowledge to more effective treatment interventions, is critical. The primary goal of the proposed “Robust Predictors of Mania and Psychosis” is to identify biological, environmental, and social factors that trigger dangerous mental states, particularly mania and psychosis, in individuals known to be at risk for these conditions. The eventual goal of this work is to provide quantifiable and predictable information that can be used to scaffold biological observations and tailor intervention strategies to maximize efficacy at the individual level. We first develop models to predict conventional clinical measures specific to psychosis and mania using (1) digital, low- to-minimal burden interactions through smartphones and wearables (Aim 1), and (2) measures extracted from face and voice during in-person clinical interactions (Aim 2), work which leverages existing data we have already collected. We will next collect one hundred person-years of pseudo-continuous multivariate behavioral data from one hundred individuals with a psychotic disorder, to further test and validate our early observations in a wider array of individuals with affective and non-affective psychotic disorders, who are likely to experience illness fluctuations within a one-year timeframe, employing several strategies to optimize participant engagement (Aim 3). We will also perform, as a representative example, a study comparing sleep, energy expenditure, and mania symptoms over time, using data obtained in the first three aims, to quantify how the relationship between energy expenditure and energy perception varies across our study population in ways that could have important consequences for health behaviors (Aim 4). The main goals of this project are thus to acquire high quality, temporally dense behavioral, cognitive, and clinical data on an important cohort of young adult patients, not only to facilitate future investigations linking these behavioral change points to neurobiological processes but also as a precursor to more effective, targeted therapeutics, such as real-time interventions that could be delivered based on dynamic factors in an individual's environment.