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之间的沟通破坏了（边缘和 更高的认知皮层区域）可能会严重构成情感失调的基础 表征情绪和焦虑症。但是，这种最有启发性的指标 沟通尚未达成共识。此外，由于技术障碍， 目前，没有人类边缘连通性的细粒度测量。网络 - 基于基于的方法可用于探索边缘和皮质子区域连通性（即 connectome），阐明核心基础的离散或共享神经机制 情绪和焦虑症的症状。在拟议的研究中，我们将（1）表征小 健康对照组中的边缘子区域和全脑连接组和主要的个人 通过优化和采用新颖的7-Tesla MRI，抑郁和广泛的动画障碍 协议具有改进的空间分辨率，全脑覆盖范围； （2）辨别基于网络的 通过开发和表征多模式的抑郁和动画生物标志物 综合大脑网络由结构，功能和动态地形组成 新的计算多层方法； （3）经诊断检查如何连接 组织在三个研究小组中表现出来。通过描绘特定的边缘子区域 参与大脑连接组，这将促进我们对如何的理解 Connectome替代品在精神疾病中表现出来。此外，这项工作结合了 大脑活性和同步（功能性MRI）的高分辨率测量，图 大脑的白质结构和解剖联系（扩散MRI）。他们的结合 研究将有助于识别异常网络特征及其对核心的贡献 抑郁和焦虑的症状。最后，转诊方法将发现共享 以及可能提供的抑郁和动画的独特网络机制 改进的诊断和治疗选择。 该职业发展奖将允许关键的免费培训目标 以：（1）获得实践临床经验，以确定临床需求和 进行翻译研究； （2）发展技术高级MRI序列 发展技能； （3）高级网络科学方法中的完善计算技能。这 该奖项提供的拟议的研究和培训将使我能够启动独立 研究计划开发神经影像学方法来研究大脑网络扰动 情绪和动画障碍。