■神经成像技术经历了爆炸性的增长,并改变了对健康和疾病中神经机制的研究。然而,考虑到处理神经影像数据的复杂工具的多样性,该领域在方法集成方面面临挑战,特别是跨多种模式和物种。具体来说,研究人员通常不得不依赖限制可重复性的孤立方法,具有特殊的数据组织和有限的软件互操作性。
■为了应对这些挑战,我们开发了定量神经成像环境和工具箱(QuNex),用于一致的端到端处理和分析的平台。QuNex为神经成像分析提供了几种新颖的功能,包括一个“交钥匙”命令,用于可重复部署自定义工作流,从加载原始数据到生成分析特征。
■该平台可实现多模态,社区开发的神经成像软件,通过一个带有软件开发工具包(SDK)的扩展框架无缝集成社区工具。严重的,它支持高吞吐量,高性能计算环境中的并行处理,无论是在本地还是在云中。值得注意的是,QuNex已经成功地处理了神经成像联盟的超过10,000次扫描,包括多个临床数据集。此外,QuNex通过内聚的翻译平台实现了人类和非人类工作流的集成。
■集体,这一努力将显著影响神经成像方法在采集方法之间的整合,管道,数据集,计算环境,和物种。在这个平台上建设将使更快,可扩展,以及神经成像技术对健康和疾病的可复制影响。
UNASSIGNED: Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability.
UNASSIGNED: To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a \"turnkey\" command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features.
UNASSIGNED: The platform enables interoperable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia, including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform.
UNASSIGNED: Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease.