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: 9766371
• 负责人: Richard Cameron Craddock
• 金额: $ 54.56万
• 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/9766371
• 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