Sex-Specific Functional Connectivity Changes in Major Depressive Disorder

重度抑郁症中性别特异性功能连接的变化

基本信息

批准号：
10427185
负责人：
Aleksandr Talishinsky
金额：
$ 4.96万
依托单位：
WEILL MEDICAL COLL OF CORNELL UNIV
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-04 至 2023-06-03
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10427185
关键词：
Affect Amygdaloid structure Anterior Anxiety Atlases Automobile Driving Autopsy Biological Biological Markers Brain Clinic Clinical Clinical Competence Communication Data Data Analyses Data Set Depressed mood Diagnostic Disease Disease remission Exhibits Female Functional Magnetic Resonance Imaging Functional disorder Future Gene Combinations Gene Expression Gene Expression Profile Genes Genetic Genetic Risk Genetic Transcription Goals Heterogeneity Human Individual Differences Insula of Reil Knowledge Laboratories Least-Squares Analysis Location Machine Learning Magnetic Resonance Imaging Major Depressive Disorder Mental disorders Mentorship Methods Molecular Neurobiology Nucleus Accumbens Pathway interactions Patients Pattern Physicians Prediction of Response to Therapy Prevalence Psychiatry Publishing Reporting Rest Risk Risk Factors Sampling Scanning Schizophrenia Scientist Selection for Treatments Sex Differences Suicide Symptoms Synaptic Transmission Syndrome Technical Expertise Techniques Testing Thick Time Tissue-Specific Gene Expression Training United States Woman Work accurate diagnostics anxiety symptoms anxious autism spectrum disorder base biomarker-driven comorbidity diagnostic biomarker disability disorder subtype experience genome wide association study guided inquiry individualized medicine interest male men neuroimaging neuropsychiatric disorder novel personalized approach precision medicine predict clinical outcome prediction algorithm predictive marker regional difference risk variant rumination secondary analysis sex sexual dimorphism skills symptomatology tool

项目摘要

PROJECT SUMMARY The goal of this project is to develop sex-specific resting state fMRI biomarkers for major depressive disorder (MDD) and to determine their underlying genetic correlates. Our primary aim is to determine the locations and the degree of sex-specific functional connectivity differences associated with MDD patients as compared to healthy subjects, and to test whether those sex-specific connectivity differences define sex-specific subtypes of MDD. The benefit of achieving these goals will be twofold: (1) in the clinic, achieving these goals will allow for more accurate diagnostic and treatment-response predictive algorithms for MDD, to reduce time to remission in MDD patients. (2) In the laboratory, our work will guide discovery of novel sex-specific circuit mechanisms and treatments for MDD. Our second aim is to determine if sex-specific MDD effects on connectivity can be predicted by regional gene expression in the brain. Investigating the relationship between brain gene expression and MDD effects on functional connectivity would implicate novel genes in the circuit mechanism of MDD and identify genes that interact with sex in driving MDD symptomatology. To achieve these aims, we will use parametric and non-parametric statistical testing to define sex-specific functional connectivity differences between depressed and healthy men, and between depressed and healthy women. Secondary analyses will test whether sex-specific classifiers for differentiating MDD subjects and healthy controls (“diagnostic biomarkers”) outperform classifiers trained on all subjects independent of sex. To identify genetic correlates of sex-specific MDD effects on functional connectivity, we will use the multivariate technique of partial least squares regression to locate linear combinations of genes which can predict MDD-related connectivity differences in males and in females. Finally, to identify sex-specific subtypes of MDD, we will use a combination of regularized canonical correlation analysis and hierarchical clustering in a validated method for subtype discovery previously published in Dr. Conor Liston’s lab. This project will fill a substantial gap in our knowledge of sex differences in MDD functional connectivity and the underlying genetic correlates of those differences. This project will also involve the execution of a concrete training plan to allow me to develop concrete technical skills in fMRI analysis and machine learning techniques, conceptual skill in hypothesis testing, data interpretation, and scientific communication, and clinical competency as a licensed physician, all under the guidance and mentorship of the project’s sponsor Dr. Liston and co-sponsor Dr. Francis Lee, who are both accomplished physician-scientists.

项目摘要该项目的目标是开发特定性别的休息状态fMRI生物标志物的主要抑郁症疾病（MDD）并确定其潜在的遗传相关性。我们的主要目的是确定位置和与MDD患者相关的性别特异性功能连通性差异的程度与健康受试者相比，并测试这些特定性别的连通性差异是否定义了性别特定 MDD的亚型。实现这些目标的好处将是双重的：（1）在诊所中，实现这些目标将允许MDD的更准确的诊断和治疗响应预测算法，以减少时间 MDD患者的缓解。（2）在实验室中，我们的工作将指导发现新颖的性别电路 MDD的机制和处理。我们的第二个目的是确定特定于性别的MDD是否对连通性影响可以通过大脑中的区域基因表达来预测。研究脑基因之间的关系表达和MDD对功能连接性的影响将暗示新的基因 MDD并确定在驱动MDD症状学中与性相互作用的基因。为了实现这些目标，我们将使用参数和非参数统计测试来定义性别特定的功能连通性差异在沮丧和健康的男人之间以及沮丧和健康的女性之间。次级分析将测试是否用于区分MDD受试者和健康对照的性别特定分类器（“诊断生物标志物”）优于对所有与性别无关的主题进行培训的分类器。确定性别特异性的遗传相关性 MDD对功能连接性的影响，我们将使用部分最小二乘回归的多元技术定位基因的线性组合，这些组合可以预测男性和中MDD相关的连通性差异女性。最后，要识别MDD的性别特异性亚型，我们将使用正规化规范的组合相关分析和分层聚类以验证的亚型发现的验证方法先前发布在Conor Liston博士的实验室中。该项目将填补我们对MDD性别差异的了解这些差异的功能连通性和潜在的遗传相关性。这个项目也将涉及执行具体培训计划，使我能够在fMRI分析和机器学习技术，假设检验中的概念技能，数据解释和科学沟通和临床能力作为有执照的身体，所有这些都在指导和心态下 Project的赞助商Liston博士和共同赞助者Francis Lee博士都是有成就的身体科学家。