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

• 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
• 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

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性别特异性静息状态功能磁共振成像生物标志物，用于抑郁症的诊断。 抑郁症（MDD），并确定其潜在的遗传相关性。我们的主要目标是确定 与MDD患者相关的性别特异性功能连接差异的位置和程度， 与健康受试者相比，并测试这些性别特异性连接差异是否定义了性别特异性 MDD的亚型。实现这些目标的好处将是双重的：（1）在临床上，实现这些目标将 允许更准确的MDD诊断和治疗反应预测算法，以减少 MDD患者的缓解。(2)在实验室中，我们的工作将指导发现新的性别特异性电路 MDD的机制和治疗。我们的第二个目标是确定性别特异性MDD是否影响连接 可以通过大脑区域基因表达来预测。研究脑基因与 表达和MDD对功能连接性的影响将暗示新的基因参与神经元的回路机制。 MDD和识别基因，与性别相互作用，推动MDD的病理学。为达致这些目标，我们会 使用参数和非参数统计检验来定义性别特异性功能连接差异 抑郁症和健康男性之间，以及抑郁症和健康女性之间。次要分析将检验 用于区分MDD受试者和健康对照的性别特异性分类器（“诊断生物标志物”） 在所有科目上，无论性别如何，分类器的表现都优于训练过的分类器。确定性别特异性的遗传相关性， MDD对功能连接性的影响，我们将使用偏最小二乘回归的多变量技术 定位基因的线性组合，这些基因可以预测男性和女性中与MDD相关的连接性差异。 女性最后，为了识别MDD的性别特异性亚型，我们将使用正则化规范 相关性分析和层次聚类在一个有效的方法亚型发现以前发表 康纳·利斯顿博士的实验室这个项目将填补我们在MDD性别差异方面的知识空白 功能连接和这些差异的潜在遗传相关性。该项目还将涉及 执行具体的培训计划，使我能够在功能磁共振成像分析方面发展具体的技术技能， 机器学习技术，假设检验的概念技能，数据解释和科学 沟通，和临床能力作为一个有执照的医生，所有的指导和指导下， 项目的发起人利斯顿博士和共同发起人弗朗西斯李博士，他们都是有成就的医学科学家。

项目成果

Regional gene expression signatures are associated with sex-specific functional connectivity changes in depression.

• DOI: 10.1038/s41467-022-32617-1
• 发表时间: 2022-09-28
• 期刊: Nature communications
• 影响因子: 16.6