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

基本信息

批准号：
10206140
负责人：
Muriah D Wheelock
金额：
$ 12.63万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10206140
关键词：
Adolescent Adult Advisory Committees Age Architecture Area Award BRAIN initiative Behavior Behavior assessment Behavioral Biological Biometry Brain Brain Mapping Brain region Career Transition Award Cognition Cognitive Complex Data Data Analyses Data Set Development Diffusion Magnetic Resonance Imaging Discipline Disease Documentation Emotional Emotions Endocrine Ensure Foundations Functional Magnetic Resonance Imaging Funding Future Goals Health Human Human Development Intervention Investigation Link Longevity Machine Learning Manuals 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 base behavior measurement brain behavior cloud based 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 skills training software development success tool tool development young adult

项目摘要

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.

项目摘要/摘要确定人脑产生认知，感知和情感的机制量化协调的大脑活动与行为之间的关系。 NIH资助的大脑映射诸如人类连接项目（HCP）和青少年认知和行为等倡议开发（ABCD）研究加速了大脑连通性的产生（即连接组）和行为数据集。当代连接组研究将大脑视为一个大规模，复杂的网络由非顾问但相互联系的大脑区域组成。我们建议利用继承网络连接组的架构是为了探测基本生物学机制发展执行功能和内在症状。为了追求这个研究问题，申请提案将房屋分析中的正式和验证验证为网络级别分析（NLA）工具箱是用于全连接范围关联研究的全面，多功能的工具。提议的NLA 工具箱实现了大脑倡议目标＃5，以“产生概念基础以理解生物学基础通过开发新的理论和数据分析工具的心理过程”。研究重点该职业过渡奖的应用是NLA在开发执行职能机制上的应用和情绪调节，这种多功能分析工具将转化为跨越连接的数据分析物种，整个生命周期以及健康与疾病。作为工具开发的一部分，适用将验证在硅连接组行为关系中使用多种NLA方法，建立灵敏度和与家庭级别的错误率控制网络级别发现的特异性（K99）目标1）。然后，申请人将使用体内人类连接组建立NLA方法的测试可靠性和来自HCP-Young成人队列（n = 1105）的行为数据，并建立大脑网络健康的成人高管和情感功能的基本（K99 AIM 2）。在独立R00阶段，她然后将调查连接组架构的变化，以支持执行和情感的发展使用ABCD纵向连接组和行为数据（n = 〜11,000年龄9-14）的功能（R00 AIM 3）。在K99阶段，她将扩展自己的行为神经科学培训，以包括机器中的培训学习，纵向模型和计算机科学。建立在她在人类大脑中的坚实基础的基础连通性分析，申请人将获得生物统计学和软件最佳实践的高级技能开发以确保她成为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），张（机器学习），巴塞特（Connectome Anallys），Eggebrecht（工具箱开发）， Barch（HCP/ABCD顾问）在所有核心领域提供了专业知识，这些领域涵盖了实验学科和拥有获得独立资金和心理化的年轻科学家的出色记录。