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.

项目概要 负责情绪调节的大脑区域（边缘系统和边缘系统）之间的沟通中断 较高的认知皮层区域）可能是情绪失调的重要原因 然而，最有指导意义的指标是情绪和焦虑障碍。 此外，由于技术障碍，还存在沟通尚未达成一致的问题。 目前还没有对人类边缘亚区域连接性进行细粒度的测量。 基于方法可用于探索边缘和皮质次区域的连接性（即 连接组），揭示核心底层的离散或共享神经机制 在拟议的研究中，我们将（1）描述小症状。 健康对照者和患有严重疾病的个体的边缘亚区和全脑连接体 通过优化和采用新型 7-Tesla MRI 来治疗抑郁症和广泛性焦虑症 具有改进的空间分辨率和全脑覆盖的协议；（2）基于辨别网络 通过开发和表征多模态来确定抑郁和焦虑的生物标志物 综合大脑网络由结构、功能和动态地形组成，使用 新的计算多层方法；（3）跨诊断检查连接组如何 通过描绘特定的边缘亚区域，组织表现在三个研究组中。 参与大脑连接组，这将增进我们对如何 此外，这项工作结合了精神疾病中的连接组改变。 大脑活动和同步的高分辨率测量（功能性 MRI），以及 大脑的白质结构和解剖连接（扩散 MRI）。 研究将有助于识别异常网络特征及其对核心的贡献 最后，跨诊断方法将揭示抑郁和焦虑的共同症状。 以及抑郁和焦虑的独特网络机制可能会提供 改进诊断和治疗选择。 该职业发展奖将允许实现关键的免费培训目标 重点是：(1) 获得实际临床经验，以确定临床需求和 进行转化研究；（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的计算技能。 该奖项提供的拟议研究和培训将使我能够启动一个独立的 研究开发计划神经成像方法来研究大脑网络扰动 情绪和焦虑障碍。