Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML.
Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML.
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DOI：
10.3389/fninf.2014.00090
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发表时间：
2014
影响因子：
3.5
通讯作者：
Peelle JE
中科院分区：
文献类型：
--
作者：
Cusack R;Vicente-Grabovetsky A;Mitchell DJ;Wild CJ;Auer T;Linke AC;Peelle JE
近年来，神经影像学数据集变得越来越丰富，参与者群体越来越大，采集技术越来越多样化，分析也越来越复杂。这些进步使得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），并且执行起来耗时（限制了尝试的分析）。在这里，我们提出了一个开源框架，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）完成的内容的处理引擎来管理其执行，从而提高了人类效率。通过在集群或云计算资源上可选地并行处理独立任务来加速分析。流水线包括一系列模块，每个模块执行特定的任务。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，如基于SPM的fMRI预处理，个人和群体水平的统计，基于体素的形态测量，纤维束成像，和多体素模式分析（MVPA）。然而，aa也允许完全定制，并鼓励有效的代码管理：新模块的编写可能只需要很小的代码开销。AA已被50多名研究人员用于包括数千名受试者的数百项神经成像研究中。它被发现是强大的，快速的，高效的，简单的单一主题的研究高达数百个主题的多模式管道。它对新手和有经验的用户都有吸引力。aa可以减少神经影像实验室进行分析的时间，减少错误，扩大科学问题的范围。
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.
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影响因子：
3.5
作者：
Poldrack RA;Barch DM;Mitchell JP;Wager TD;Wagner AD;Devlin JT;Cumba C;Koyejo O;Milham MP
通讯作者：
Milham MP
影响因子：
4.3
作者：
Kay, Kendrick N.;Rokem, Ariel;Wandell, Brian A.
通讯作者：
Wandell, Brian A.
影响因子：
5.7
作者：
Van Essen, D. C.;Ugurbil, K.;Auerbach, E.;Barch, D.;Behrens, T. E. J.;Bucholz, R.;Chang, A.;Chen, L.;Corbetta, M.;Curtiss, S. W.;Della Penna, S.;Feinberg, D.;Glasser, M. F.;Harel, N.;Heath, A. C.;Larson-Prior, L.;Marcus, D.;Michalareas, G.;Moeller, S.;Oostenveld, R.;Petersen, S. E.;Prior, F.;Schlaggar, B. L.;Smith, S. M.;Snyder, A. Z.;Xu, J.;Yacoub, E.
通讯作者：
Yacoub, E.
影响因子：
5.7
作者：
Calhoun VD;Liu J;Adali T
通讯作者：
Adali T
影响因子：
5.7
作者：
Kundu, Prantik;Inati, Souheil J.;Evans, Jennifer W.;Luh, Wen-Ming;Bandettini, Peter A.
通讯作者：
Bandettini, Peter A.