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

连接组-行为关系网络级分析的创新生物统计方法

• 批准号: 10700129
• 负责人: Muriah D Wheelock
• 金额: $ 24.9万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-08 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10700129
• 关键词: Acceleration; Adolescent; Adult; Advisory Committees; Age; Architecture; Area; Award; BRAIN initiative; Behavior; Behavior assessment; Behavioral; Biological; Biometry; Brain; Brain Mapping; Brain region; Career Transition Award; Cloud Computing; Cognition; Cognitive; Complex; Data; Data Analyses; Data Set; Development; Diffusion Magnetic Resonance Imaging; Discipline; Disease; Documentation; Emotional; Emotions; Endocrine; Ensure; Family; Foundations; Functional Magnetic Resonance Imaging; Funding; Future; Goals; Health; Human; Human Development; Intervention; Investigation; Link; Longevity; Machine Learning; Manuals; Maps; Measures; Mental Processes; Mentors; Mentorship; Methods; Modeling; Neurosciences; Pathway interactions; Perception; Performance; Phase; Procedures; Process; Production; Psychopathology; Publishing; ROC Curve; Reaction Time; Reproducibility; Reproducibility of Results; Research; Research Personnel; Scanning; Scientist; Sensitivity and Specificity; Software Engineering; Statistical Data Interpretation; Structure; Symptoms; Techniques; Testing; Training; United States National Institutes of Health; Update; Validation; Writing; analysis pipeline; analytical method; analytical tool; behavior measurement; brain behavior; cognitive development; cognitive reappraisal; cohort; computer science; connectome; connectome data; dimensional analysis; emotion regulation; emotional functioning; executive function; graphical user interface; improved; in silico; in vivo; indexing; innovation; longitudinal dataset; longitudinal design; network architecture; neuroimaging; post intervention; programs; skills; software development; success; tool; tool development; young adult

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.

项目总结/摘要 确定人类大脑产生认知、感知和情感的机制， 量化协调的大脑活动和行为之间的关系。NIH资助的脑图谱 人类连接组计划（HCP）和青少年认知和行为计划（ACCP） 发展（ABCD）研究，加速了大的大脑连接（即连接体）的产生， 行为数据集。当代连接体研究将大脑视为一个大规模的复杂网络 由不相邻但相连的大脑区域组成。我们建议利用固有的网络 连接体的结构，以探索基本的生物学机制， 执行功能和内化症状的发展。为了研究这个问题， 应用程序建议将内部分析管道正式化并验证为网络级分析（NLA） 工具箱作为一个全面的，多功能的工具，用于连接范围内的关联研究。拟议的NLA 工具箱实现了BRAIN计划的目标#5“为理解生物学基础提供概念基础 通过开发新的理论和数据分析工具来研究心理过程”。虽然研究重点 这个职业过渡奖的重点是将NLA应用于执行功能的发展机制 和情绪调节，这个多功能分析工具将对整个连接体数据分析产生变革性影响 物种，整个生命周期，以及健康和疾病。作为工具开发的一部分，申请人将验证 使用计算机连接组-行为关系的多种NLA方法，并建立灵敏度， 与全连接组控制的家系错误率（K99）相比，网络水平发现的特异性 目标1）。然后，申请方将使用体内人连接体建立NLA方法的重测信度 和来自HCP-年轻成人队列（N=1105）的行为数据，并建立大脑网络 基础健康成人执行和情感功能（K99目标2）。在独立R 00阶段，她 然后将研究连接体结构的变化，支持执行和情感的发展， 使用ABCD纵向连接体和行为数据（N=~ 11，000，9-14岁）（R 00 Aim 3）。 在K99阶段，她将扩展她在行为神经科学方面的培训，包括机器训练。 学习、纵向模型和计算机科学。在她对人类大脑的研究基础上 连通性分析，申请人将获得生物统计学和软件最佳实践的高级技能 以确保她作为一名独立研究人员的成功。咨询委员会，包括博士。 Smyser（功能连接），Marcus（软件工程），Fair（发育神经科学），Todorov （生物统计学），Zhang（机器学习），Bassett（连接体分析），Eggebrecht（工具箱开发）， 和巴奇（HCP/ABCD顾问）提供跨越实验学科的所有核心领域的专业知识， 在获得独立资助和指导年轻科学家方面拥有出色的记录。