NIPreps: integrating neuroimaging preprocessing workflows across modalities, populations, and species

NIPreps：整合跨模式、人群和物种的神经影像预处理工作流程

• 批准号: 10260312
• 负责人: Oscar Esteban
• 金额: $ 144.58万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-19 至 2024-07-18
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10260312
• 关键词: Address; Algorithms; Animal Model; Atlases; Automobile Driving; BRAIN initiative; Brain; Code; Collection; Communities; Computer Analysis; Computer software; Data; Data Aggregation; Data Analyses; Development; Devices; Diffusion Magnetic Resonance Imaging; Discipline; Documentation; Ecosystem; Educational Materials; Elderly; Ensure; Event; Experimental Designs; Foundations; Functional Magnetic Resonance Imaging; Generations; Human; Image; Investigation; Investments; Libraries; Magnetic Resonance Imaging; Measurement; Measures; Mental disorders; Methodology; Methods; Modality; Monitor; Neurosciences; Pattern; Performance; Population; Positron-Emission Tomography; Process; Reporting; Reproducibility; Research; Research Personnel; Research Support; Software Framework; Solid; Source; Specimen; Spin Labels; Standardization; Statistical Data Interpretation; Statistical Models; Structure; Sum; System; Techniques; Translations; Visual; Visualization; Visualization software; brain health; design; hackathon; heterogenous data; image reconstruction; imaging modality; imaging study; instrument; molecular imaging; morphometry; multimodality; neuroimaging; neuropsychiatric disorder; next generation; nonhuman primate; preclinical imaging; programs; sustainability framework; symposium; tool

Project Summary Despite the rapid advances in the neuroimaging research workflow over the last decade, the enormous variability between and within data types and specimens impedes integrated analyses. Moreover, the availability of a comprehensive portfolio of software libraries and tools has also resulted in a concerning degree of analytical variability. Generalizing the preprocessing — that is, the intermediate step between data generation by the measurement device and the subsequent statistical modeling and analysis — beyond fMRIPrep, we propose a framework called NiPreps (NeuroImaging Preprocessing toolS) that we envision as a workbench for the development of such pipelines. By exclusively addressing the preprocessing of the data, fMRIPrep has successfully allowed researchers to focus their effort and expertise on the portion most relevant to scientific inference (i.e., statistical and computational analyses) and reduce methodological variability. NiPreps expands fMRIPrep to operate on new imaging modalities (diffusion MRI, arterial spin labeling, positron emission tomography, and multi-echo functional MRI) and disciplines (e.g., preclinical imaging). Despite some remarkable analysis workflows that display end-to-end consolidation, integrations across applications (e.g., analyses of human and nonhuman data) remain exceptionally challenging. Hence, we will evolve fMRIPrep into NiPreps, a software framework integrating BIDS and following the BIDS-Apps specifications. First, the project will consolidate the NiPreps foundations, with the generalization of fMRIPrep's driving principles and methods across modalities and domains of application. Second, we will expand the portfolio of end-user NiPreps with dMRIPrep, ASLPrep, PETPrep, and better coverage of multi-echo fMRI by fMRIPrep. Finally, we will address the NiPreps community's consolidation to ensure the sustainability of the framework, converging the communities around each "-Prep" with hackathons and docusprints. In short, NIPreps will pave the way towards next-generation imaging, ultimately allowing neuroscientists to seek a unified statistical framework capable of rigorously integrating cross-application and cross-species data analysis.

项目概要 尽管神经影像研究工作流程在过去十年中取得了快速进步，但巨大的 数据类型和样本之间以及内部的可变性阻碍了综合分析。此外， 全面的软件库和工具组合的可用性也导致了令人担忧的问题 分析变异性的程度。概括预处理——即数据之间的中间步骤 测量设备生成以及随后的统计建模和分析 - 超越 fMRIPrep，我们提出了一个名为 NiPreps（神经成像预处理工具）的框架，我们将其设想为 用于开发此类管道的工作台。通过专门解决数据的预处理问题， fMRIPrep 成功地让研究人员将他们的精力和专业知识集中在最相关的部分 科学推理（即统计和计算分析）并减少方法的可变性。 NiPreps 扩展了 fMRIPrep，以在新的成像模式上运行（扩散 MRI、动脉自旋标记、 正电子发射断层扫描和多回波功能 MRI）和学科（例如临床前成像）。 尽管有一些出色的分析工作流程显示了端到端的整合，但跨领域的集成 应用（例如，人类和非人类数据的分析）仍然极具挑战性。 因此，我们将fMRIPrep演进为NiPreps，一个集成BIDS并遵循以下原则的软件框架： BIDS 应用程序规范。首先，该项目将巩固 NiPreps 基础，并进行泛化 fMRIPrep 跨模式和应用领域的驱动原理和方法。其次，我们将 通过 dMRIPrep、ASLPrep、PETPrep 扩展最终用户 NiPreps 的产品组合，并更好地覆盖 通过 fMRIPrep 进行多回波功能磁共振成像。最后，我们将解决 NiPreps 社区的整合问题，以确保 该框架的可持续性，通过黑客马拉松将每个“-Prep”社区聚集在一起， 文档打印。简而言之，NIPreps 将为下一代成像铺平道路，最终使 神经科学家寻求一个统一的统计框架，能够严格整合跨应用和 跨物种数据分析。