Multidimensional brain connectome features of depression and anxiety

抑郁和焦虑的多维脑连接组特征

• 批准号: 10571512
• 负责人: Yael Jacob
• 金额: $ 17.86万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-16 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10571512
• 关键词: Agreement; Amygdaloid structure; Anatomy; Anxiety; Anxiety Disorders; Architecture; Award; Biological Markers; Brain; Brain Mapping; Brain imaging; Brain region; Classification; Clinical; Cognitive; Communication; Complex; Data Set; Dependence; Development; Diagnostic; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Elements; Evaluation; Functional Magnetic Resonance Imaging; Generalized Anxiety Disorder; Goals; Grain; Hippocampus; Human; Impairment; Individual; Information Networks; K-Series Research Career Programs; Light; Link; Literature; Machine Learning; Magnetic Resonance Imaging; Major Depressive Disorder; Maps; Mathematics; Measurement; Measures; Mental Depression; Mental disorders; Methods; Modification; Mood Disorders; Network-based; Noise; Pathway Analysis; Patients; Pattern; Protocols documentation; Research; Resolution; Role; Science; Selection for Treatments; Signal Transduction; Structure; Symptoms; System; Testing; Time; Training; Translational Research; Work; anxiety symptoms; cognitive system; connectome; depressive symptoms; design; diagnostic strategy; effective therapy; emotion dysregulation; emotion regulation; experience; graph theory; improved; machine learning algorithm; mood symptom; multimodality; neural; neural network; neuroimaging; neuromechanism; neuropsychiatry; novel; programs; skill acquisition; skills; theories; tool; treatment strategy; ultra high resolution; white matter

PROJECT SUMMARY Disrupted communication between brain regions responsible for emotion regulation (limbic and higher cognitive cortical regions) may critically underlie the emotional dysregulation that characterizes mood and anxiety disorders. However, the most instructive metric of such communication has yet to be agreed upon. In addition, due to technical barriers, there are currently no fine-grained measurements of limbic subregion connectivity in humans. A network- based approach can be used to explore limbic and cortical subregion connectivity (i.e. connectome), shedding light on the discrete or shared neural mechanisms underlying core symptoms in mood and anxiety disorders. In the proposed study we will (1) characterize small limbic subregions and whole-brain connectomes in healthy controls and individuals with major depressive and generalized anxiety disorder, by optimizing and employing a novel 7-Tesla MRI protocol with improved spatial resolution with whole brain coverage; (2) discern network based biomarkers of depression and anxiety by developing and characterizing a multi-modal integrative brain network consisting of structural, functional and dynamic topographies using a new computational multilayer approach; and (3) transdiagnostically examine how connectome organization manifests across the three study groups. By portraying specific limbic subregion involvement in the brain connectome, this would advance our understanding of how connectome alternations manifest in psychiatric disorders. Furthermore, this work combines high-resolution measures of brain activity and synchronization (functional MRI), with maps of the brain’s white matter architecture and anatomical connections (diffusion MRI). Their combined study will facilitate identification of aberrant network features and their contributions to core symptoms in depression and anxiety. Lastly, the transdiagnostic approach will uncover shared and unique network mechanisms of depression and anxiety that could potentially provide improved diagnostics and treatment selection. This Career Development Award will allow for the critical complimentary training goals centering on: (1) gaining practical clinical experience towards identifying clinical needs and conducting translational research; (2) development of technical advanced MRI sequence development skills; (3) refine computational skills in advanced network science methods. The proposed research and training afforded by this award will allow me to launch an independent research program developing neuroimaging methods to study brain network perturbations in mood and anxiety disorders.

项目摘要 负责情绪调节的大脑区域（边缘系统和大脑皮层）之间的交流中断。 高级认知皮层区域）可能是情绪失调的关键基础， 是情绪和焦虑障碍的特征然而，最具启发性的指标， 通信尚未商定。此外，由于技术壁垒， 目前还没有精细的测量人类边缘亚区的连通性。一个网络- 基于的方法可以用于探索边缘和皮质子区域连接（即， 连接体），揭示了核心的离散或共享的神经机制 情绪和焦虑症的症状。在这项研究中，我们将（1）描述小 边缘亚区和全脑连接组在健康对照组和个体与主要 抑郁症和广泛性焦虑症，通过优化和采用新型7特斯拉MRI 具有全脑覆盖的改进空间分辨率的协议;（2）基于识别网络的 抑郁和焦虑的生物标志物，通过开发和表征多模态 由结构、功能和动态地形组成的综合脑网络， 新的计算多层方法;和（3）transdiagnosically检查如何连接体 三个研究组的组织表现。通过描绘特定的边缘亚区 参与大脑连接体，这将促进我们对如何 连接体改变表现在精神疾病中。此外，这项工作结合了 大脑活动和同步的高分辨率测量（功能性MRI）， 脑的白色结构和解剖连接（弥散MRI）。其合并 研究将有助于确定异常网络特征及其对核心业务的贡献 抑郁和焦虑的症状。最后，跨诊断方法将揭示共享的 以及独特的抑郁和焦虑网络机制， 改进诊断和治疗选择。 这个职业发展奖将允许关键的免费培训目标 围绕：（1）获得临床实践经验，以确定临床需求， 开展转化研究;（2）开发技术先进的MRI序列 开发技能;（3）完善先进网络科学方法中的计算技能。的 这个奖项所提供的拟议研究和培训将使我能够发起一个独立的 研究计划开发神经成像方法，以研究大脑网络扰动， 情绪和焦虑障碍。