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，复杂的网络 连接的大脑区域。而不是专注于有限的先验区域，而是 神经影像已经朝着整个连接组的关联的统计测试转向 可能的大脑连接。但是，这些全连接组的关联研究具有严重的倍数 比较问题，必要的统计方法，可以控制关​​联的误报率 在行为和50k功能连接之间。父母大脑计划的长期目标 R00（EB029343，'连接组行为网络分析的创新生物统计学方法 关系’）是创建一个统计分析软件，该软件将利用继承的网络体系结构 连接组以探测健康发展的基本生物学机制 以及无序的认知，行为和情感。具体而言，父母赠款旨在正式和验证 房屋分析管道进入网络级别分析（NLA）工具箱，作为一种全面的多功能工具 在全连接组的关联研究中。虽然该职业过渡奖的研究重点是 NLA应用于执行功能和情感调节的制定机制，该多才多艺 分析工具将转化为跨物种，整个寿命和健康的连接数据分析 和疾病。作为K99/R00工具开发的一部分，Wheelock博士验证了多个NLA 使用硅连接组中的方法，建立网络级别发现的敏感性和特异性 关系，使用体内人类连接组的NLA方法的测试 - 重测可靠性以及来自 HCP-Young成人队列和正在进行的工作正在扩展NLA，以调查Connectome的变化 使用Connectome和行为的建筑支持执行和情感功能的发展 来自ABCD研究的数据（n = 11,000年龄9-14岁）。在R00的AIM 2中，NLA工具箱正在更新以反映 面向对象的编程，编码纵向模型和图形用户界面。目标的目标 行政补充是通过实施几个关键变化来提高NLA功能。具体来说， 该行政补充的资金，NOT-OD-23-073，将促进NLA的重构以改进 开发人员和最终用户的计算效率以及可用性。该补充的目标是 1）重构和优化低级编程语言中的计算建模，2）合并错误 记录和扩展文档，以及3）建立单元和集成测试以改善代码合并。 这些目标的成功完成将完成并扩大父母R00的影响 改善NLA软件的功能和可持续性，以与开放科学的最佳实践保持一致 并提高软件的可访问性，从而实现社区范围的网络分析方法的采用 用于整个连接组的关联研究。