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首页> 美国卫生研究院文献>Frontiers in Neuroinformatics >Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML
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Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML

机译：自动分析（aa）：高效的神经影像工作流程和使用Matlab和XML的并行处理

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Recent years have seen neuroimaging data sets becoming richer, with larger cohorts of participants, a greater variety of acquisition techniques, and increasingly complex analyses. These advances have made data analysis pipelines complicated to set up and run (increasing the risk of human error) and time consuming to execute (restricting what analyses are attempted). Here we present an open-source framework, automatic analysis (aa), to address these concerns. Human efficiency is increased by making code modular and reusable, and managing its execution with a processing engine that tracks what has been completed and what needs to be (re)done. Analysis is accelerated by optional parallel processing of independent tasks on cluster or cloud computing resources. A pipeline comprises a series of modules that each perform a specific task. The processing engine keeps track of the data, calculating a map of upstream and downstream dependencies for each module. Existing modules are available for many analysis tasks, such as SPM-based fMRI preprocessing, individual and group level statistics, voxel-based morphometry, tractography, and multi-voxel pattern analyses (MVPA). However, aa also allows for full customization, and encourages efficient management of code: new modules may be written with only a small code overhead. aa has been used by more than 50 researchers in hundreds of neuroimaging studies comprising thousands of subjects. It has been found to be robust, fast, and efficient, for simple-single subject studies up to multimodal pipelines on hundreds of subjects. It is attractive to both novice and experienced users. aa can reduce the amount of time neuroimaging laboratories spend performing analyses and reduce errors, expanding the range of scientific questions it is practical to address.
机译：近年来，神经影像数据集变得越来越丰富，参与者人数越来越多，采集技术也越来越丰富，分析也越来越复杂。这些进步使数据分析管道的设置和运行变得复杂（增加了人为错误的风险），并且使执行过程耗时（限制了尝试进行的分析）。在这里，我们提出了一个开源框架，即自动分析（aa），以解决这些问题。通过使代码模块化和可重用，并使用跟踪已完成的内容和需要（重新）完成的处理引擎来管理代码的执行，可以提高人员效率。通过对集群或云计算资源上的独立任务进行可选的并行处理，可以加快分析速度。管道包括一系列分别执行特定任务的模块。处理引擎跟踪数据，计算每个模块的上游和下游依赖关系图。现有的模块可用于许多分析任务，例如基于SPM的fMRI预处理，个人和组级别统计，基于体素的形态计量学，体层摄影术和多体素模式分析（MVPA）。但是，aa也允许完全自定义，并鼓励代码的有效管理：只需很少的代码开销就可以编写新模块。 50多个研究人员已使用aa进行数百项涉及成千上万个受试者的神经影像研究。对于简单的单个主题研究，直到数百个主题的多模式流水线，已经发现它是鲁棒，快速和高效的。它对新手和有经验的用户都具有吸引力。 aa可以减少神经影像实验室花费在执行分析上的时间并减少错误，从而扩大了实际需要解决的科学问题的范围。

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