Joint structural-and-functional MRI analysis for predicting electroconvulsive therapy response in major depressive disorder

联合结构和功能 MRI 分析预测重度抑郁症的电休克治疗反应

• 批准号: 10471260
• 负责人: Lipeng Ning
• 金额: $ 18.42万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10471260
• 关键词: Adverse effects; Advisory Committees; Algorithms; Anatomy; Anterior; Axon; Biological Factors; Brain; Brain region; Clinical; Cognitive; Corpus Callosum; Data; Data Set; Decision Making; Dependence; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Dimensions; Discriminant Analysis; Electroconvulsive Therapy; Etiology; Fiber; Forcep; Foundations; Frequencies; Functional Magnetic Resonance Imaging; Functional disorder; Future; Grant; Guidelines; Image; Intervention; Joints; Knowledge; Magnetic Resonance Imaging; Major Depressive Disorder; Measures; Mental Depression; Mentored Research Scientist Development Award; Mentors; Methods; Neuroanatomy; Pathologic; Pathologic Processes; Pathway interactions; Patients; Pattern; Pharmacotherapy; Prediction of Response to Therapy; Procedures; Process; Property; Reporting; Research; Research Scientist Award; Rest; Severities; Subgroup; System; Techniques; Training; Training Programs; Work; base; brain abnormalities; computer framework; computerized tools; density; electric field; experience; image guided; improved; indexing; individual patient; insight; large datasets; longitudinal dataset; method development; multimodality; neuroimaging; neuroregulation; novel; novel therapeutics; patient subsets; personalized predictions; prognostic tool; prospective; regression algorithm; relating to nervous system; responders and non-responders; response; simulation; tool; tractography; treatment optimization; treatment response; treatment strategy; treatment-resistant depression; white matter

Project Summary Title: Joint structural-and-functional MRI analysis for predicting electroconvulsive therapy response in major depressive disorder Electroconvulsive therapy (ECT) is currently the most effective and fast-acting neuromodulation treatments for major depressive disorder (MDD). Understanding the mechanism of action of ECT-response may be a fruitful strategy to understand the pathophysiology of MDD. But ECT also frequently provokes cognitive adverse effects and is often considered as more invasive than pharmacotherapy. Consequently, there is a critical need to develop reliable prognostic tools for predicting individualized treatment response. The availability of such a predictor could provide guidelines for the clinician to optimize treatment strategy for individual patient and to support patient's decision-making process. In Aim 1 of this project, we propose to develop a computational framework to integrate novel diffusion MRI (dMRI) and resting-state functional MRI (rsfMRI) measures in order to assess the properties of structural and functional connectivity of brain networks. In Aim 2, we will apply the joint analysis of dMRI and rsfMRI to two large datasets with a total of 260 MDD patients and 170 controls to identify depression-related abnormal connections in brain networks. Our approach could provide a novel probabilistic MDD network that reflects heterogeneous brain abnormalities in different biotypes of MDD. In Aim 3, we will focus our analysis on a subgroup of 120 patients treated by ECT. Specifically, we will use the joint structural-and-functional analysis to identify ECT-induced changes in brain connections for responders and non- responders, respectively. Moreover, we will analyze the correlation between changes in brain connections with changes in clinical MDD severities measures, and also with subject-specific E-field stimulated by ECT. Results from this project will support future prospective trials aiming to improve therapeutic response using individualized image-guided ECT in MDD. The training program provided by this K01 Mentored Research Scientist Award provides the critical knowledge and experience to the PI for him to complete the proposed project. Based his strong foundation in multimodal MRI analysis techniques, the PI seeks to obtain focused training on neuroanatomy, clinical aspects of depression and ECT, under the guidance of experienced experts from his mentoring team and advisory committee. In conclusion, this K01 Award will provide the PI with the training and research background to conduct independent research that integrates novel MRI techniques and E-field simulation approaches to develop optimal neuromodulation therapies for individual patients with MDD.

项目摘要 标题：预测电休克治疗反应的结构和功能MRI联合分析 重度抑郁症 电休克治疗（ECT）是目前最有效和快速作用的神经调节治疗， 重度抑郁症（MDD）。了解ECT反应的作用机制可能是一个富有成效的 了解MDD病理生理学的策略。但ECT也经常引起认知方面的不良影响 并且通常被认为比药物治疗更具侵入性。因此，迫切需要 开发可靠的预后工具，用于预测个体化治疗反应。这样一个 预测器可以为临床医生提供指导，以优化个体患者的治疗策略， 支持患者的决策过程。在本项目的目标1中，我们建议开发一个计算 整合新型弥散MRI（dMRI）和静息态功能MRI（rsfMRI）测量的框架， 来评估大脑网络的结构和功能连接的特性。在目标2中，我们将应用 对两个大型数据集（共260名MDD患者和170名对照）进行dMRI和rsfMRI联合分析， 识别大脑网络中与抑郁相关的异常连接。我们的方法可以提供一种新的 概率MDD网络，反映不同生物型MDD的异质性脑异常。在Aim中 3，我们将集中我们的分析在一个亚组的120例患者治疗的ECT。具体来说，我们将使用联合 结构和功能分析，以确定ECT诱导的反应者和非反应者大脑连接的变化。 响应者，分别。此外，我们将分析大脑连接变化与 临床MDD严重度测量的变化，以及ECT刺激的受试者特定电场。结果 该项目将支持未来的前瞻性试验，旨在改善治疗反应， MDD中图像引导ECT。K01指导研究科学家奖提供的培训计划 为PI提供关键知识和经验，以完成拟议项目。根据他 PI在多模式MRI分析技术方面打下了坚实的基础，寻求获得以下方面的重点培训 神经解剖学，抑郁症和ECT的临床方面，从他的经验丰富的专家的指导下， 指导小组和咨询委员会。总之，K01奖将为PI提供培训， 研究背景，进行独立的研究，整合新的MRI技术和电场 模拟方法，为MDD患者个体开发最佳神经调节疗法。