C-PAC: A configurable, compute-optimized, cloud-enabled neuroimaging analysis software for reproducible translational and comparative

C-PAC：一种可配置、计算优化、支持云的神经影像分析软件，用于可重复的转化和比较

• 批准号: 9894275
• 负责人: Richard Cameron Craddock
• 金额: $ 2.31万
• 依托单位: CHILD MIND INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9894275
• 关键词: Address; Adoption; Anatomy; Animal Model; Architecture; Award; Behavior; Brain; Brain imaging; Capital; Code; Communities; Comparative Study; Computer software; Consumption; Data; Data Analyses; Data Set; Development; Documentation; Electrodes; Electrophysiology (science); Environment; Funding; High Performance Computing; Human; Image; Image Analysis; Individual; Learning; Link; Location; Machine Learning; Magnetic Resonance Imaging; Maintenance; Measures; Methods; Modeling; Modification; Monkeys; Outcome; Output; Pattern; Persons; Phenotype; Population; Process; Pythons; Readability; Reproducibility; Research Personnel; Rodent; Scientific Inquiry; Software Design; Statistical Methods; Structure; Techniques; Testing; Text; Time; Tissues; Training; United States National Institutes of Health; Validity of Results; Work; analysis pipeline; animal data; base; brain research; cloud based; comparative; computing resources; connectome; cost; data sharing; data structure; denoising; design; flexibility; graphical user interface; hackathon; human imaging; image processing; improved; innovative neurotechnologies; investigator training; learning strategy; multimodality; neuroimaging; neuroinformatics; neuroregulation; open source; software as a service; supervised learning; tool; unsupervised learning

ABSTRACT The BRAIN Initiative is designed to leverage sophisticated neuromodulation, electrophysiological recording, and macroscale neuroimaging techniques in human and non-human animal models in order to develop a multilevel understanding of human brain function. However, the necessary tools for organizing, processing and analyzing neuroimaging data generated through these efforts are not widely available as coherent and easy-to- use software packages. Gaps are particularly apparent for nonhuman data (i.e., monkey, rodent), as most of the existing processing and analytic software packages are specifically designed for human imaging. Methods have been proposed for addressing the challenges inherent to the processing of nonhuman data (e.g., brain extraction, tissue segmentation, spatial normalization, brain parcellation, temporal denoising); to date, these have not been readily integrated into an easy-to-use, robust, and reproducible analysis package. Similarly, many of the sophisticated machine learning and modeling methods developed for neuroimaging analyses are inaccessible to most researchers because they have not been integrated into easy-to-use pipeline software. As a result, translational and comparative neuroimaging researchers patch together neuroinformatics pipelines that use various combinations of disparate software packages and in-house code. We propose to extend the Configurable Pipeline for the Analysis of Connectomes (C-PAC) open-source software to provide robust and reproducible pipelines for functional and structural MRI data. We will integrate the various disparate image processing and analysis methods used to handle the challenges of nonhuman imaging data, into a single, open source, configurable, easy-to-use end-to-end analysis pipeline package that is accessible locally or via the cloud. The end product will not only improve the quality, transparency and reproducibility of nonhuman translational and comparative imaging, but also enable new avenues of scientific inquiry through our inclusion of methods that are yet to be applied to nonhuman imaging data (e.g., gradient- based cortical parcellation methods, hyperalignment). Specific aims of the proposed work include to: 1) Integrate neuroimaging processing and analysis methods optimized for BRAIN Initiative data, 2) Implement strategies for carrying out comparative studies of human and non-human populations, and 3) Extend C-PAC to include cutting-edge analytical strategies for identifying mechanisms of brain function. All development will occur “in the open” using GitHub and other collaborative tools to maximally involve participation in the C-PAC project. Annual hackathons will be held to collaborate with investigators from BRAIN Initiative awards and other neuroinformatics development projects to integrate their tools with C-PAC. Hands-on training will be held to train investigators on optimal use of the newly developed tools.

摘要 大脑计划旨在利用复杂的神经调制、电生理记录、 以及在人类和非人类动物模型中的宏观神经成像技术，以开发 对人脑功能的多层次了解。然而，组织、处理和管理所需的工具 分析通过这些努力产生的神经成像数据并不是广泛可用的连贯和易于- 使用软件包。对于非人类数据(即猴子、啮齿动物)来说，差距尤其明显，因为大多数 现有的处理和分析软件包是专门为人体成像设计的。方法 已经提出了用于解决处理非人类数据(例如，大脑)所固有的挑战 提取、组织分割、空间归一化、大脑分割、时间去噪)；迄今为止，这些 还没有容易地集成到易于使用、健壮和可重现的分析包中。同样， 为神经成像分析开发的许多复杂的机器学习和建模方法包括 大多数研究人员无法访问，因为它们尚未集成到易于使用的管道软件中。AS 结果，翻译和比较神经成像研究人员将神经信息学管道拼凑在一起 使用不同的软件包和内部代码的各种组合。 我们建议扩展可配置管道来分析连接(C-PAC)开源 软件，为功能和结构MRI数据提供健壮且可重现的管道。我们将整合 用于应对非人类挑战的各种不同的图像处理和分析方法 将数据映像到单个开源、可配置、易于使用的端到端分析流水线包中， 可在本地或通过云访问。最终的产品不仅将提高质量、透明度和 非人类翻译和比较成像的重复性，但也使科学的新途径 通过我们将尚未应用于非人类成像数据的方法(例如，梯度- 基于皮质分割方法、超对齐)。拟议工作的具体目标包括：1) 集成针对大脑计划数据进行优化的神经成像处理和分析方法，2)实施 对人类和非人类群体进行比较研究的战略，以及3)将C-PAC扩展到 包括用于识别大脑功能机制的尖端分析策略。所有发展都将 使用GitHub和其他协作工具以最大限度地参与C-PAC 项目。一年一度的黑客马拉松将与Brain Initiative Awards和其他机构的调查人员合作 神经信息学开发项目，将其工具与C-PAC集成。将举办动手培训，以 对调查人员进行培训，使他们了解如何优化使用新开发的工具。