Innovative biostatistical approaches to network level analyses of connectome-behavior relationships

项目摘要/摘要 决定人脑产生认知、感知和情绪的机制取决于 在量化了大脑协调活动和行为之间的关系之后。美国国立卫生研究院资助的脑成像 诸如人类连接组项目(HCP)和青少年认知和行为等倡议 开发(ABCD)研究，加速了大型大脑连接(即连接体)的产生 行为数据集。当代连接组研究将大脑视为一个大规模、复杂的网络 由不相邻但相互连接的大脑区域组成的。我们建议利用固有的网络 连接体的结构，以探索潜在的基本生物学机制 执行功能的发展和内化症状。为了追寻这一研究问题，这 应用程序建议将内部分析管道正式化为网络级别分析(NLA)并进行验证 工具箱是一种全面、通用的工具，用于连接组范围的关联研究。拟议的NLA 工具箱实现了大脑计划的目标5，即“为理解生物学基础产生概念基础 通过开发新的理论和数据分析工具，研究心理过程“。虽然研究的重点是 这个职业过渡奖的内容是关于NLA在执行功能发展机制中的应用 和情绪调节，这一多功能的分析工具将转变为Connectome数据分析 在物种、寿命、健康和疾病方面都是如此。作为工具开发的一部分，申请者将验证 在计算机连接组-行为关系中使用多种NLA方法，并建立敏感性和 网络水平发现的特异性与连接组范围的家庭错误率控制(K99)相比较 目标1)。然后，申请者将使用活体人类连接体建立NLA方法的测试-重测可靠性 和来自HCP-Young成人队列(N=1105)的行为数据，并建立大脑网络 基本健康的成人执行和情绪功能(K99目标2)。在独立的R00阶段，她 然后将调查支持执行力和情绪化发展的连接体架构的变化 使用ABCD纵向连接体和行为数据(N=~11,000岁，9-14岁)(R00目标3)。 在K99阶段，她将扩展她在行为神经科学方面的培训，以包括机器培训 学习、纵向模型和计算机科学。建立在她在人脑中的坚实基础上 在连通性分析方面，申请人将获得生物统计学方面的高级技能和软件方面的最佳实践 为了确保她作为一名独立研究人员的成功，她的发展。该咨询委员会成员包括艾哈迈德博士。 Smyser(功能连接)、Marcus(软件工程)、Fair(发展神经科学)、Todorov (生物统计学)，张(机器学习)，Bassett(连接组分析)，Eggebrecht(工具箱开发)， 和BARCH(HCP/ABCD顾问)提供跨越实验学科的所有核心领域的专业知识和 拥有获得独立资助和指导年轻科学家的良好记录。

PROJECT SUMMARY/ABSTRACT Determining the mechanisms by which the human brain generates cognition, perception, and emotion hinges upon quantifying the relationships between coordinated brain activity and behavior. NIH-funded brain mapping initiatives such as the Human Connectome Project (HCP) and the Adolescent Cognitive and Behavioral Development (ABCD) study, have accelerated the production of large brain connectivity (i.e. connectome) and behavioral datasets. Contemporary connectome research views the brain as a large-scale, complex network composed of nonadjacent, yet connected brain regions. We propose to leverage the inherent network architecture of the connectome in order to probe fundamental biological mechanisms underlying the development of executive function and internalizing symptoms. In pursuit of this research question, this application proposes to formalize and validate in house analysis pipelines into a Network Level Analysis (NLA) toolbox as a comprehensive, versatile tool for use in connectome-wide association studies. The proposed NLA toolbox fulfills BRAIN Initiative goal #5 to “Produce conceptual foundations for understanding the biological basis of mental processes through development of new theoretical and data analysis tools”. While the research focus of this career transition award is on the application of NLA to developmental mechanisms of executive function and emotion regulation, this versatile analytic tool will be transformative to connectome data analysis across species, across the lifespan, and in health and disease. As part of tool development, the applicant will validate multiple NLA approaches using in silico connectome-behavior relationships and establish sensitivity and specificity of network level findings as compared to the connectome-wide control of familywise error rate (K99 Aim 1). The applicant will then establish test-retest reliability of NLA approaches using in vivo human connectome and behavioral data available from the HCP-Young Adult cohort (N=1105), and establish brain networks underlying healthy adult executive and emotional function (K99 Aim 2). During the independent R00 phase, she will then investigate changes in connectome architecture supporting the development of executive and emotional function using the ABCD longitudinal connectome and behavioral data (N=~11,000 age 9-14) (R00 Aim 3). During the K99 phase she will extend her training in behavioral neuroscience to include training in machine learning, longitudinal models, and computer science. Building on her strong foundation in human brain connectivity analysis, the applicant will gain advanced skills in biostatistics and best practices in software development to ensure her success as an independent researcher. The advisory committee, including Drs. Smyser (functional connectivity), Marcus (software engineering), Fair (developmental neuroscience), Todorov (biostatistics), Zhang (machine learning), Bassett (connectome analysis), Eggebrecht (toolbox development), and Barch (HCP/ABCD consultant) provide expertise in all core areas spanning experimental disciplines and possess an excellent record of obtaining independent funding and mentoring young scientists.