Remote computational phenotyping of behavioral and affective dynamics in major depression

重度抑郁症行为和情感动态的远程计算表型

• 批准号: 10449259
• 负责人: Robb Brooks Rutledge
• 金额: $ 79.77万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10449259
• 关键词: Affect; Affective; Amygdaloid structure; Anhedonia; Anterior; Behavior; Behavioral; Behavioral Assay; Behavioral Model; Biological Assay; Brain; Cellular Phone; Clinical; Communities; Complement; Computer Models; Corpus striatum structure; Data; Data Collection; Decision Making; Dimensions; Disease; Environment; Functional Magnetic Resonance Imaging; Heterogeneity; Individual; Intervention; Learning; Life; Link; MRI Scans; Magnetic Resonance Imaging; Major Depressive Disorder; Maps; Measures; Mental Depression; Mental disorders; Modeling; Moods; Neural Network Simulation; Patient Self-Report; Patients; Persons; Phenotype; Policies; Positive Valence; Prevalence; Process; Psychological reinforcement; Reporting; Research Domain Criteria; Rest; Rewards; Risk; Sampling; Statistical Models; Symptoms; System; Testing; Time; base; behavioral phenotyping; common symptom; cost; depressive symptoms; effective therapy; experience; improved; long short term memory; neural circuit; neuroimaging; recruit; recurrent neural network; remote grading; simulation; sleep quality; success; theories

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.

项目总结/摘要 重度抑郁症是一种高度衰弱性疾病，影响全球3亿多人。治疗 分配可能涉及冗长的试错过程，其被症状异质性复杂化。的 研究领域标准（RDoC）矩阵为调查精神疾病提供了一个框架， 整合了多个层次的分析。抑郁症状与RDoC阳性密切相关 配价系统（PVS）结构域，但目前尚不清楚PVS结构如何与常见的抑郁症相关。 症状包括情绪低落、快感缺乏和冷漠。行为和情感的计算探针 动力学作为一种“计算表型”个体的手段显示出很大的希望，并提供了一种方法， 验证PVS结构与症状异质性的关系。智能手机的普及使其成为一种 远程测试的理想平台。我们建议使用智能手机收集纵向数据，用于三个“游戏化” 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的计算的可靠性， 行为和情感动力学分析，以了解抑郁症状的异质性。我们将 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（情绪低落， 快感缺乏和冷漠）在这两种环境（目标1）。然后我们将招募大量的患者样本， 中度抑郁症状（n=400），并使用智能手机远程测试长达12个月（Aim 2）的情况。我们将测试行为和情感计算参数是否与 抑郁症状随着时间我们还将使用数据驱动的递归神经网络方法来识别 我们的数据的其他特征与抑郁症状有关。最后，我们将收集MRI扫描和实验室内 Aim 2中患者子样本（n=200）的数据，并询问奖励敏感性和奖励预测是否 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 将测试抑郁亚型之间的映射，这些亚型由大脑网络连接、行为和 情感计算参数和抑郁症状。利用计算模型，我们可以 回路、行为和自我报告的水平之间的关系，以及对症状异质性的映射测试， 加强我们对RDoC结构的理解，并为更有效和及时地 干预措施来治疗抑郁症。