Combining Voice and Genetic Information to Detect Heterogeneity in Major Depressive Disorder

项目摘要 本申请旨在通过结合遗传学和基因治疗来促进我们对重度抑郁症（MDD）的理解。 信息和分析MDD患者的语音模式以识别亚型。MDD是导致 尽管残疾在全世界是一个普遍现象，但相对于其他常见疾病，对其起源的了解较少。 有不太有效的治疗方法，更少的是花在试图了解它是如何产生和如何 目前的治疗方法相对无效，50%的患者难以治愈，许多人遭受痛苦。 严重复发。理解MDD的底层机制已经被认为是一个重要的 全球心理健康的挑战。因此，开发MDD的新治疗方法是公共卫生的主要优先事项。 MDD研究的一个主要挑战是异质性的存在。多种亚型的存在 MDD已被怀疑了很长一段时间，并可能混淆了适当治疗这种疾病的能力 与现有的治疗方法，以及使其难以确定MDD的原因作为发展的前奏， 新疗法然而，发现亚型一直很难。鉴于人们说话的方式可以反映出 情绪的变化，我们希望声音能够预测情绪，因此有可能被用作生物标志物， 承认异质性。在初步的数据显示，结合遗传数据高维声乐 从记录中提取的特征可用于识别亚型。此外，基因数据的使用 允许我们将声音特征输入到没有记录的大型生物库中， 声音特征和一系列临床重要指标之间的关系。我们探索 诊断MDD，预测严重程度和其他临床特征。将我们的方法应用于 将告知临床管理，改善诊断，完善治疗，并帮助开发新的 治疗

PROJECT SUMMARY This application aims to advance our understanding of major depressive disorder (MDD) by combining genetic information and analyzing speech patterns of those with MDD to identify subtypes. MDD is the leading cause of disability throughout the world, yet, relative to other common disorders, less is known about its origins. There are less effective treatments and much less is spent on trying to understand how it arises and how to cure it. Current treatments are relatively ineffective, with up 50% of patients refractory and many suffering severe recurrence. Understanding the mechanisms underlying MDD has been recognized as a grand challenge in global mental health. Thus, developing new treatments for MDD is a major priority for public health. A major challenge for MDD research is the presence of heterogeneity. The existence of multiple subtypes of MDD has been suspected for a long time, and likely confounds the ability to treat the disorder appropriately with existing treatments, as well as making it hard to identify the causes of MDD as a prelude to developing new treatments. However finding subtypes has been hard. Given that the way people talk can reflect alterations in mood, we expect voice to be able to predict mood, and hence potentially be used as biomarker to recognize heterogeneity. In preliminary data show that in combination with genetic data high-dimensional vocal features extracted from recordings can be used to identify subtypes. Furthermore, the use of genetic data allows us to impute voice features into large biobanks where no recordings exist, making it possible to explore the relationship between vocal features and a rich array of clinically important indicators. We explore the power of voice to make a diagnosis of MDD, to predict severity and other clinical features. Applying our approach to will inform clinical management, improving diagnosis, refine treatment and aid the development of new treatments