结合声音和遗传信息来检测重度抑郁症的异质性-猫眼课题宝

结合声音和遗传信息来检测重度抑郁症的异质性

批准号：

10656229

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

项目状态：

未结题

起止时间：

2020-08-14 至 2025-05-31

来源：

https://reporter.nih.gov/project-details/10656229

关键词：

Affect Alleles Anxiety Behavioral 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 Far East Frequencies Frustration Genetic Genetic study Genomics Genotype Heritability Heterogeneity Interview 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 System Testing Time Translational Research Voice Voice Quality Woman accurate diagnosis biobank clinical application clinical phenotype clinical translation comorbidity computer science data sharing deep neural network disability disorder subtype effective therapy efficacy evaluation flexibility genetic architecture genetic information genetic predictors improved innovation insight long short term memory multidimensional data neuroimaging preservation public health priorities statistics suicidal targeted treatment trait transfer learning treatment response vector

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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

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