Statistical Methods for Whole-Brain Dynamic Connectivity Analysis

全脑动态连接分析的统计方法

• 批准号: 10594266
• 负责人: Heather Marie Shappell
• 金额: $ 14.45万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10594266
• 关键词: Accounting; Address; Alzheimer' s Disease; Award; Behavior; Biology; Brain; Brain Diseases; Brain region; Brain scan; Clinical; Cognition; Communication; Complex; Computer software; Data; Data Analyses; Data Collection; Dedications; Detection; Diagnosis; Education; Educational workshop; Environment; Exhibits; Foundations; Functional Magnetic Resonance Imaging; Future; Goals; Growth; Head; Human; Image; Image Analysis; Individual; Interdisciplinary Study; Journals; Knowledge; Lead; Medicine; Mentors; Methodology; Methods; Modeling; Morphologic artifacts; Motion; Neurologic; Neurosciences; Participant; Pathway Analysis; Patient-Focused Outcomes; Predisposition; Prevention; Research; Research Activity; Research Design; Research Project Grants; Scanning; Scientist; Series; Slide; Statistical Data Interpretation; Statistical Methods; Structure; System; Techniques; Time; Training; Training Activity; Training Programs; Variant; Vocational Guidance; Work; biomarker identification; brain dysfunction; career; computational neuroscience; detection limit; early detection biomarkers; experimental study; forest; functional MRI scan; improved; infancy; innovation; insight; interest; markov model; member; neuroimaging; novel; response; skills; study population; symposium; theories; tool

My objective for the K25 award is to establish myself as an independent neuroimaging statistician, with expertise in whole-brain network analyses and an integral member of multidisciplinary research teams devoted to addressing diseases of the brain. Attaining these goals will require didactic training and research guidance. Research We will develop new methodology to improve whole-brain dynamic connectivity analyses of normal and abnormal brain function, which is vital for understanding various brain disorders, such as Alzheimer’s Disease, and may help identify biomarkers and inform early prevention and treatment. Previous studies are largely based on one average network constructed using data from an entire brain scan (i.e., static connectivity), but emerging evidence suggests network topology exhibits meaningful variations on the second to minute scale, creating a gap in understanding unless these variations are quantified. While several methods have been proposed to address this new direction in the field, there does not yet exist a unifying framework that accurately estimates whole-brain networks, as well as the dynamics of network change across a functional magnetic resonance imaging (fMRI) experiment, while a) accounting for variables of interest and motion- induced artifacts and b) allowing for individual estimates of dynamics. The novel methods proposed here will address these needs and provide a set of tools for future dynamic brain network analysis research. This research, along with my proposed training plan, will facilitate my progression toward becoming an independent neuroimaging statistician with expertise in brain network analysis. Training The proposed training program involves four components: 1) career guidance and neuroscience and network analysis training from a mentoring committee; 2) an educational component to establish fundamental knowledge in computational neuroscience and image analysis; 3) performing innovative research using the skills gained from the proposed training plan and; 4) participating in the exchange of knowledge and ideas with other statisticians and neuroscientists through workshops, conferences, seminar series, and journal clubs. The training will enable me to shift from an early career statistician to an established, independent, neuroimaging statistician with expertise in whole-brain network analyses. The training in computational neuroscience and image analysis will allow me to become a multidisciplinary research team scientist dedicated to studying the human brain. The growth gained through this 5-year period will lead to a skill set, and a confidence, that allows me to be more well-versed in the neuroscience and biology behind the data I am analyzing. This will ultimately lead to more effective communication with neuroscientists and clinicians, improved study design, more informed statistical analyses, and a more comprehensive interpretation of the results in my future work.

我对K25奖的目标是将自己确立为独立的神经影像学家 全脑网络分析方面的专业知识和跨学科研究团队不可或缺的成员 解决大脑的疾病。实现这些目标将需要教学培训和研究指导。 研究 我们将开发新的方法来改善正常和正常的全脑动态连通性分析 异常的大脑功能，这对于理解各种脑部疾病（例如阿尔茨海默氏病）至关重要 并可能有助于识别生物标志物并为早期的预防和治疗提供信息。以前的研究主要是 基于一个使用来自整个大脑扫描的数据构建的平均网络（即静态连接性），但 新兴证据表明，网络拓扑表现出在第二至微小尺度上的有意义的变化， 除非量化这些变化，否则会在理解中造成差距。虽然已经有几种方法 提议解决该领域的新方向，尚未存在一个统一的框架 准确估计全脑网络，以及跨功能上的网络变化的动力学 磁共振成像（fMRI）实验，而a）考虑了感兴趣的变量和运动 - 诱导的伪影和b）允许单个动力学估计。这里提出的新方法将 满足这些需求，并为将来的动态大脑网络分析研究提供一组工具。这 研究以及我提出的培训计划，将有助于成为独立的发展 神经影像学家在大脑网络分析方面具有专业知识。 训练 拟议的培训计划涉及四个组成部分：1）职业指导和神经科学和网络 心理委员会的分析培训； 2）建立基本的教育部分 计算神经科学和图像分析方面的知识； 3）使用 从建议的培训计划中获得的技能； 4）与 其他统计学家和神经科学家通过研讨会，会议，开创性系列和期刊俱乐部。 培训将使我能够从早期的职业统计学家转变为已建立的独立，神经影像 统计学家在全脑网络分析中具有专业知识。计算神经科学和 图像分析将使我成为一个多学科研究团队的科学家，致力于研究 人脑。在这个五年期间的增长将导致技能和信心，以允许 我要对我分析的数据背后的神经科学和生物学变得更加精通。最终将 导致与神经科学家和临床医生进行更有效的沟通，改进的研究设计，更多 知情的统计分析，以及对我未来工作中结果的更全面的解释。