The Neuroimaging Brain Chart Software Suite

神经影像脑图软件套件

• 批准号: 10581015
• 负责人: Christos Davatzikos
• 金额: $ 97.73万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-15 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10581015
• 关键词: Adopted; Adult; Aging; Artificial Intelligence; Atlases; Back; Brain; Clinical; Communities; Complex; Computer software; Data; Data Analyses; Detection; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Ecosystem; Educational workshop; Functional Magnetic Resonance Imaging; Genomics; Goals; Heterogeneity; High Performance Computing; Image; Individual; Infrastructure; Internet; Knowledge Discovery; Libraries; Machine Learning; Maps; Methods; Modeling; Nerve Degeneration; Neurodegenerative Disorders; Online Systems; Pattern; Persons; Phenotype; Process; Pythons; Research; Research Personnel; Software Tools; Source; Standardization; Structure; System; Testing; Training; Variant; Visual; Visualization; Work; aging brain; analysis pipeline; brain magnetic resonance imaging; cloud based; cluster computing; combat; data sharing; deep learning; deep learning model; machine learning method; machine learning model; meetings; multimodality; nervous system disorder; neuroimaging; neuropsychiatric disorder; novel; programs; structural determinants; tool; web interface; web portal

This study proposes to refine, integrate and disseminate the NeuroImaging Brain Chart (NIBCh) software toolbox and machine learning (ML) model library, an ecosystem of software components enabling constructive integration, statistical harmonization, and ML-centric data analyses across studies. NIBCh enables large-scale analyses of multi-modal brain MRI data by mapping such data into a compact coordinate system of informative neuroimaging signatures implemented by our library of ML models. The axes of this coordinate system represent two types of information: 1) a variety of structural (sMRI and dMRI) and functional connectomic (rsfMRI) imaging derived phenotypes (IDPs), such as multi-scale brain parcelations and brain networks; 2) complex ML-based imaging signatures (ML-IDPs), which capture multi-variate imaging patterns that reflect the heterogeneity of brain aging, neurodegeneration, as well as of neuropsychiatic disorders and have been previously derived from carefully processed and curated data of over 65,000 individuals. Using our software toolboxes (Tbx), researchers will be able to map new data into NIBCh, and hence to use ML-IDP models trained in NIBCh, as well as perform statistical tests against NIBCh normative ranges and compare their results with those of other studies using the same Tbx. The software suite will include a set of containerized pre-processing and analysis pipelines, as well as statistical harmonization and ML inference toolboxes, which will be accessible via a standalone python front-end visualization, as cloud-based containers, and via a web-interface supported by our high-performance computing cluster. Several dissemination plans are discussed, including a github user community, tutorials at major technical and clinical meetings, and support of both standalone pipelines locally or on the cloud, and web-based access of harmonization and ML inference modules. The over-arching primary goal of our program is to provide the software tools that will allow users to contribute to an actively growing community-based dimensional neuroimaging system that will utilize machine learning models to provide rich, yet precise, compact, concise, and informative representations of brain structure, function and connectivity.

本研究旨在完善、整合和推广神经影像脑图（NIBCh）软件 工具箱和机器学习（ML）模型库，一个软件组件生态系统， 建设性的整合，统计协调和ML为中心的数据分析跨研究。NIBCh 通过将多模态脑MRI数据映射到紧凑的坐标中， 由我们的ML模型库实现的信息丰富的神经成像签名系统。这个的轴心 坐标系表示两种类型信息：1）各种结构（sMRI和dMRI）， 功能性连接组学（rsfMRI）成像衍生的表型（IDP），例如多尺度脑包裹 2）复杂的基于ML的成像签名（ML-IDP），它捕获多变量 成像模式，反映了大脑老化，神经退行性变，以及 神经精神障碍，并已事先从仔细处理和策划的数据， 超过65，000人。使用我们的软件工具箱（Tbx），研究人员将能够将新数据映射到 NIBCh，并因此使用在NIBCh中训练的ML-IDP模型，以及针对NIBCh执行统计测试 正常范围，并将其结果与使用相同Tbx的其他研究的结果进行比较。软件 套件将包括一组容器化预处理和分析管道，以及统计 协调和ML推理工具箱，可通过独立的Python前端访问 可视化，作为基于云的容器，并通过我们的高性能 计算集群。讨论了几个传播计划，包括github用户社区，教程 在主要的技术和临床会议上，并支持本地或云上的独立管道， 以及基于Web的协调和ML推理模块的访问。 我们计划的首要目标是提供软件工具，使用户能够 有助于积极发展基于社区的多维神经成像系统， 机器学习模型，以提供丰富，但精确，紧凑，简洁和信息丰富的表示， 大脑结构、功能和连通性。