Implementing best practices in software design for Network Level Analysis

实施网络级分析软件设计的最佳实践

• 批准号: 10839638
• 负责人: Muriah D Wheelock
• 金额: $ 23.33万
• 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 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10839638
• 关键词: Acceleration; Address; Administrative Supplement; Adolescent; Adoption; Age; Algorithms; Architecture; Award; BRAIN initiative; Behavior; Behavior assessment; Behavioral; Biological; Biometry; Brain; Brain region; Career Transition Award; Code; Cognition; Cognition Disorders; Cognitive; Collaborations; Communities; Complement; Complex; Computer Models; Computer software; Data; Data Analyses; Data Set; Development; Diagnostic Errors; Disease; Documentation; Emotions; Engineering; Ensure; Feedback; Funding; Future; Goals; Health; Hour; Human; Individual; Individual Differences; Libraries; Link; Longevity; Manuals; Memory; Methods; Mind; Modeling; Monitor; Output; Parents; Pathway Analysis; Perception; Policies; Process; Production; Programming Languages; Reporting; Reproducibility; Research; Running; Sensitivity and Specificity; Shapes; Software Design; Source; Statistical Data Interpretation; Statistical Methods; Statistical Models; Testing; Time; Universities; Update; Washington; Work; analysis pipeline; analytical tool; cohort; complex data; computing resources; connectome; connectome data; design; emotion regulation; emotional functioning; executive function; experience; graphical user interface; improved; in silico; in vivo; individual variation; innovation; interest; network architecture; neuroimaging; novel; open data; parallel processing; parent grant; response; tool; tool development; usability; young adult

PROJECT SUMMARY Contemporary research views the brain as a large-scale, complex network composed of nonadjacent, yet connected brain regions. Rather than focusing on a limited set of a priori regions of interest, the field of neuroimaging has shifted towards statistical testing on associations across the whole connectome, i.e., at every possible brain connection. However, these connectome-wide association studies have a severe multiple comparisons problem, necessitating statistical methods which can control the false positive rate for associations between behavior and upwards of 50k functional connections. The long-term goal of the Parent BRAIN Initiative R00 (EB029343, ‘Innovative biostatistical approaches to network level analyses of connectome-behavior relationships’) is to create a statistical analysis software that would leverage the inherent network architecture of the connectome in order to probe fundamental biological mechanisms underlying the development of healthy and disordered cognition, behavior, and emotion. Specifically, the parent grant aims 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. 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 during the K99/R00, Dr. Wheelock has validated multiple NLA approaches, establishing sensitivity and specificity of network level findings using in silico connectome-behavior relationships, test-retest reliability of NLA approaches using in vivo human connectome and behavioral data from the HCP-Young Adult cohort, and ongoing work is extending NLA to investigate changes in connectome architecture supporting the development of executive and emotional function using connectome and behavioral data from the ABCD study (N=11,000 age 9-14). In Aim 2 of the R00, NLA toolbox is being updated to reflect object-oriented programming, incorporating longitudinal models and a graphical user interface. The goal of this Administrative Supplement is to improve NLA functionality by implementing several crucial changes. Specifically, funding from this Administrative Supplement, NOT-OD-23-073, will promote refactorization of NLA to improve computational efficiency, and usability by both developers and end users. The goals of this Supplement are to 1) refactor and optimize computational modeling in lower-level programming languages, 2) incorporate error logging and expand documentation, and 3) establish unit and integration testing to improve code merging. Successful completion of these Aims will both complement and extend the impact of the Parent R00, significantly improving the functionality and sustainability of NLA software in keeping with best practices of open science as well as increase accessibility of the software, enabling community-wide adoption of network-analysis methods for connectome-wide association studies.

项目摘要 当代研究认为大脑是一个大规模的复杂网络，由不相邻的，但 连接的大脑区域。与其专注于有限的一组先验感兴趣区域， 神经成像已经转向对整个连接体的关联进行统计测试，即，在每一 可能的大脑连接然而，这些全连接组关联研究有严重的多重性， 比较问题，需要统计方法来控制关联的假阳性率 行为和50 k以上的功能连接之间的关系。家长大脑计划的长期目标 R 00（EB 029343，“连接组行为网络水平分析的创新生物统计方法 关系”）是创建一个统计分析软件，该软件将利用 连接体，以探索健康发育的基本生物学机制， 以及认知、行为和情感的紊乱。具体而言，父母补助金旨在正式化和验证 将分析管道容纳到网络级分析（NLA）工具箱中，作为一个全面、多功能工具使用 在连接组关联研究中。虽然这个职业转型奖的研究重点是在 应用NLA的发展机制的执行功能和情绪调节，这一多功能 分析工具将变革跨物种，跨寿命和健康的连接体数据分析 和疾病作为K99/R 00期间工具开发的一部分，Wheelock博士验证了多个NLA 方法，使用计算机连接组行为建立网络水平发现的灵敏度和特异性 关系，使用体内人类连接体的NLA方法的重测信度和来自 HCP-年轻成人队列，正在进行的工作是扩展NLA，以研究连接体的变化 架构支持执行和情感功能的发展，使用连接体和行为 数据来自ABCD研究（N= 11，000，9-14岁）。在R 00的目标2中，NLA工具箱正在更新，以反映 面向对象的编程，结合纵向模型和图形用户界面。这个目标 行政补充是通过实施几项重要的改革来改进NLA的功能。具体地说， 来自本行政补充文件NOT-OD-23-073的资金将促进NLA的重构， 计算效率以及开发人员和最终用户的可用性。本补编的目标是 1)用低级编程语言重构和优化计算建模，2）合并错误 日志记录和扩展文档，以及3）建立单元和集成测试以改进代码合并。 这些目标的成功完成将极大地补充和扩展母R 00的影响 改进NLA软件的功能和可持续性，以符合开放科学的最佳实践， 并增加软件的可访问性，使社区范围内采用网络分析方法 用于全连接组关联研究。