Remote computational phenotyping of behavioral and affective dynamics in major depression

项目摘要/摘要 重度抑郁症是一种高度衰弱的疾病，影响着全球超过3亿人。治疗 作业可能涉及一个漫长的反复试验过程，并因症状不同而变得复杂。这个 研究领域标准(RDoC)矩阵为调查精神障碍提供了一个框架， 跨多个级别的分析进行集成。抑郁症状与RDoC阳性密切相关 配价系统(PVS)结构域，但PVS结构如何与常见抑郁相关尚不清楚 症状包括情绪低落、快感缺乏和冷漠。行为和情感的计算探索 动力学显示出巨大的前景，它是一种通过计算对个体进行表型识别的手段，并提供了一种 验证与症状异质性相关的PVS结构。智能手机的无处不在使它们成为 远程测试的理想平台。我们建议使用智能手机收集三个“游戏化”的纵向数据 衡量风险决策、概率强化学习和奖励-努力权衡的任务 情感状态的同时波动。我们将建立远程收集计算的可靠性 行为和情感动力学分析用于了解抑郁症状的异质性。我们会 首先在实验室和通过智能手机远程测试社区样本(n=200)，以验证行为和 情感计算参数与抑郁症状具有相同的关系(情绪低落， 两种环境(目标1)中的快感缺乏和冷漠。然后，我们将招募一大批患有 中度抑郁症状(n=400)，并使用智能手机远程测试长达12个月(目标 2)。我们将测试行为和情感计算参数是否与 随着时间的推移会出现抑郁症状。我们还将使用数据驱动的递归神经网络方法来识别 我们数据中与抑郁症状相关的其他特征。最后，我们将收集核磁共振扫描和实验室内 来自目标2的患者(n=200)的子样本中的数据，并询问奖励敏感性和奖励预测 错误，所有三个任务的特征，映射到一致的神经回路和抑郁症状(目标3)。我们 将测试由大脑网络连接定义的抑郁亚型之间的映射，行为和 情感计算参数和抑郁症状。使用计算模型，我们可以架起 在电路、行为和自我报告的级别之间，以及对症状异质性的映射测试之间， 加强我们对RDoC结构的了解，为更有效和及时地 治疗抑郁症的干预措施。

PROJECT SUMMARY / ABSTRACT Major depression is a highly debilitating disorder affecting over 300 million people worldwide. Treatment assignment can involve a lengthy trial-and-error process complicated by symptom heterogeneity. The Research Domain Criteria (RDoC) matrix provides a framework for investigating psychiatric disorders that integrates across multiple levels of analysis. Depressive symptoms are closely linked to the RDoC Positive Valence Systems (PVS) domain, but it is unknown how PVS constructs relate to common depressive symptoms including low mood, anhedonia, and apathy. Computational probes of behavioral and affective dynamics show great promise as a means of ‘computationally phenotyping’ individuals and providing a way to validate PVS constructs in relation to symptom heterogeneity. The ubiquity of smartphones makes them an ideal platform for remote testing. We propose to collect longitudinal data using smartphones for three ‘gamified’ tasks that measure risky decision making, probabilistic reinforcement learning, and reward-effort trade-offs and concurrent fluctuations in affective state. We will establish the reliability of remotely collected computational assays of behavioral and affective dynamics for understanding heterogeneity in depressive symptoms. We will first test a community sample (n=200) both in the lab and remotely by smartphone to verify that behavioral and affective computational parameters have the same relationship to depressive symptoms (low mood, anhedonia, and apathy) in both environments (Aim 1). We will then recruit a large sample of patients with moderate depressive symptoms (n=400) and test them remotely using smartphones for up to 12 months (Aim 2). We will test whether behavioral and affective computational parameters are related to changes in depressive symptoms over time. We will also use data-driven recurrent neural network approaches to identify additional features of our data related to depressive symptoms. Finally, we will collect MRI scans and in-lab data in a subsample of patients (n=200) from Aim 2 and ask whether reward sensitivity and reward prediction error, features of all three tasks, map onto consistent neural circuitry and depressive symptoms (Aim 3). We will test for a mapping between depression subtypes defined by brain network connectivity, behavioral and affective computational parameters, and depressive symptoms. Using computational models, we can bridge between levels of circuits, behavior, and self-report, and test for a mapping onto heterogeneity in symptoms, enhancing our understanding of RDoC constructs and paving the way for more effective and timely interventions to treat depression.