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

项目摘要 尽管神经成像研究工作流程在过去十年中取得了快速进展，但巨大的 数据类型和样本之间和内部的差异妨碍了综合分析。此外， 提供全面的软件库和工具组合也导致了一个令人担忧的问题 分析的可变性程度。泛化预处理--即数据之间的中间步骤 由测量设备生成以及随后的统计建模和分析-超越 FMRIPrep，我们提出了一个称为NiPreps(神经成像预处理工具)的框架，我们将其设想为一种 开发这类管道的工作台。通过独占地寻址数据的预处理， FMRIPrep成功地让研究人员将他们的努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)和减少方法的可变性。 NiPreps扩展fMRIPrep以在新的成像模式上操作(扩散MRI、动脉自旋标记、 正电子发射断层扫描和多回波功能磁共振成像)和学科(例如，临床前成像)。 尽管一些出色的分析工作流显示了端到端整合，但 应用程序(例如，分析人类和非人类数据)仍然具有极大的挑战性。 因此，我们将把fMRIPrep演变为NiPreps，这是一个集成了投标和遵循 出价-应用程序规格。首先，该项目将巩固NiPreps的基础，具有普遍性 FMRIPrep的驱动原理和方法跨模式和应用领域。第二，我们将 通过dMRIPrep、ASLPrep、PETPrep和更好的覆盖范围，扩展终端用户NiPrep产品组合 FMRIPrep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 框架的可持续性，通过黑客马拉松和 文件指纹。简而言之，NIPreps将为下一代成像铺平道路，最终允许 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。

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.