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

结合声音和遗传信息来检测重度抑郁症的异质性

基本信息

批准号：
10410474
负责人：
JONATHAN FLINT
金额：
$ 66.78万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-14 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10410474
关键词：
Affect Alleles Anxiety Behavioral Genetics Biological Markers Biology Case-Control Studies China Chinese Classification Clinical Clinical Management Collection Data Data Set Depressed mood Development Diagnosis Disease Disease remission Drug Prescriptions Engineering Ensure Far East Frequencies Genetic Genetic study Genomics Genotype Heritability Heterogeneity Interview Investigation Linkage Disequilibrium Major Depressive Disorder Manuals Maps Mental Depression Mental Health Mental disorders Methodology Methods Modeling Moods Morphologic artifacts Neurobiology Participant Patients Pattern Persons Pharmacological Treatment Phenotype Population Psychiatry Recurrence Refractory Research Resources Sampling Scheme Severities Severity of illness Signal Transduction Specificity Speech Suicide System Testing Time Ursidae Family Voice Voice Quality Woman accurate diagnosis base biobank clinical application clinical phenotype comorbidity computer science data sharing deep neural network disability disorder subtype effective therapy efficacy evaluation flexibility genetic analysis genetic architecture genetic information genetic predictors improved innovation insight long short term memory multidimensional data neuroimaging preservation psychogenetics public health priorities statistics targeted treatment trait transfer learning treatment response vector

项目摘要

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

项目摘要该应用旨在通过结合遗传来提高我们对重大抑郁症（MDD）的理解信息和分析具有MDD患者的语音模式以识别亚型。 MDD是主要原因全世界的残疾人都相对于其他常见疾病，对其起源的了解较少。有效的治疗方法较少，而花在试图理解它的出现以及如何的方式上花费了更少治愈它。目前的治疗相对无效，有50％的患者难治性和许多痛苦严重的复发。了解MDD的基础机制已被认为是宏伟的全球心理健康的挑战。因此，为MDD开发新的治疗方法是公共卫生的主要优先事项。 MDD研究的主要挑战是存在异质性。存在多种亚型的存在 MDD长期怀疑，可能会使适当治疗该疾病的能力混淆使用现有治疗方法，并使很难确定MDD的原因是发展的前奏新治疗。但是，找到亚型很难。鉴于人们说话的方式可以反映情绪改变，我们希望声音能够预测情绪，因此有可能用作生物标志物识别异质性。在初步数据中表明，结合遗传数据高维声音从录音中提取的功能可用于识别亚型。此外，使用遗传数据允许我们将语音功能算作不存在录音的大型生物库，从而可以探索人声特征与一系列临床上重要的指标之间的关系。我们探索力量诊断为MDD的语音，以预测严重程度和其他临床特征。将我们的方法应用于将为临床管理提供信息，改善诊断，完善治疗并帮助开发新的治疗