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.

项目概要 当代研究将大脑视为一个大规模、复杂的网络，由不相邻但又 连接的大脑区域而不是关注一组有限的先验感兴趣区域。 神经影像学已经转向对整个连接组之间的关联进行统计测试，即在每个 然而，这些连接组范围的关联研究具有严重的多重性。 比较问题，需要能够控制关联误报率的统计方法 行为与超过 5 万个功能连接之间的关系是 Parent BRAIN Initiative 的长期目标。 R00（EB029343，“连接组行为网络级分析的创新生物统计方法” 关系”）是为了创建一个统计分析软件，该软件将利用固有的网络架构 连接组以探究健康发展的基本生物学机制 具体来说，家长资助的目的是形式化和验证认知、行为和情感。 将分析管道集成到网络级分析 (NLA) 工具箱中，作为综合性、多功能的工具使用 在全连接组关联研究中，该职业转型奖的研究重点是 NLA 在执行功能和情绪调节的发展机制中的应用，这种多功能 分析工具将变革跨物种、跨生命周期和健康领域的连接组数据分析 作为 K99/R00 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级发现的敏感性和特异性 使用体内人类连接组和行为数据的 NLA 方法的关系、重测可靠性 HCP-年轻人队列，正在进行的工作正在扩展 NLA 以调查连接组的变化 使用连接组和行为支持执行和情感功能发展的架构 来自 ABCD 研究的数据（N=11,000 名 9-14 岁） 在 R00 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。 行政补充旨在通过实施几项关键变更来改进 NLA 功能。 本行政补充文件 NOT-OD-23-073 提供的资金将促进 NLA 的重构，以改进 本补充文件的目标是提高计算效率以及开发人员和最终用户的可用性。 1) 用较低级编程语言重构和优化计算模型，2) 合并错误 记录和扩展文档，3) 建立单元和集成测试以改进代码合并。 成功完成这些目标将补充并扩大母公司 R00 的影响，显着 提高 NLA 软件的功能和可持续性，以符合开放科学的最佳实践 并提高软件的可访问性，使网络分析方法能够在社区范围内采用 用于全连接组关联研究。