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.

我获得K25奖的目标是成为一名独立的神经影像学统计学家， 全脑网络分析方面的专业知识，以及致力于 to addressing解决diseases疾病of the brain脑.实现这些目标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非正常脑的全脑动态连接分析。 异常的大脑功能，这是至关重要的了解各种大脑疾病，如阿尔茨海默氏病， 并可能有助于识别生物标志物并为早期预防和治疗提供信息。以前的研究主要是 基于使用来自整个大脑扫描的数据构建的一个平均网络（即，静态连接），但 新出现的证据表明网络拓扑在秒到分钟的尺度上表现出有意义的变化， 除非这些变化被量化，否则会造成理解上的差距。虽然已经有几种方法 尽管有人建议在实地处理这一新方向，但目前还没有一个统一的框架， 准确地估计全脑网络，以及网络变化的动态跨功能 磁共振成像（fMRI）实验，而a）考虑到感兴趣的变量和运动- 诱发的伪影和B）允许单独估计动态。这里提出的新方法将 满足这些需求，并为未来的动态脑网络分析研究提供一套工具。这 研究，沿着我提出的培训计划，将促进我成为一个独立的发展 神经影像学统计学家，擅长大脑网络分析。 培训 建议的培训计划包括四个组成部分：1）职业指导和神经科学和网络 指导委员会的分析培训; 2）教育部分，以建立基本的 在计算神经科学和图像分析的知识; 3）进行创新的研究， 从拟议的培训计划中获得的技能; 4）参与知识和想法的交流， 其他统计学家和神经科学家通过讲习班，会议，系列研讨会和期刊俱乐部。的 培训将使我从一个早期的职业统计学家转变为一个成熟的，独立的，神经影像学 具有全脑网络分析专长的统计学家。计算神经科学的培训， 图像分析将使我成为一个多学科的研究团队的科学家，致力于研究 人脑通过这5年的成长将带来一套技能和一种信心， 我希望我能更精通我正在分析的数据背后的神经科学和生物学。这将最终 与神经科学家和临床医生进行更有效的沟通，改进研究设计， 有据可查的统计分析，并在我今后的工作中对结果作出更全面的解释。