4.7 Article

SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability

期刊

NEUROIMAGE
卷 59, 期 4, 页码 4160-4167

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2011.11.088

关键词

Simulation; fMRI; Group analysis

资金

  1. NIH [1R01-EB006841, 1R01-EB005846, 2R01-EB000840, 1 P20 RR021938-01]
  2. DOE [DE-FG02-08ER64581]
  3. Direct For Computer & Info Scie & Enginr
  4. Div Of Information & Intelligent Systems [1017718] Funding Source: National Science Foundation
  5. Division of Computing and Communication Foundations
  6. Direct For Computer & Info Scie & Enginr [1116944, 1117056] Funding Source: National Science Foundation

向作者/读者索取更多资源

We introduce SimTB, a MATLAB toolbox designed to simulate functional magnetic resonance imaging (fMRI) datasets under a model of spatiotemporal separability. The toolbox meets the increasing need of the fMRI community to more comprehensively understand the effects of complex processing strategies by providing a ground truth that estimation methods may be compared against. SimTB captures the fundamental structure of real data, but data generation is fully parameterized and fully controlled by the user, allowing for accurate and precise comparisons. The toolbox offers a wealth of options regarding the number and configuration of spatial sources, implementation of experimental paradigms, inclusion of tissue-specific properties, addition of noise and head movement, and much more. A straightforward data generation method and short computation time (3-10 seconds for each dataset) allow a practitioner to simulate and analyze many datasets to potentially understand a problem from many angles. Beginning MATLAB users can use the SimTB graphical user interface (GUI) to design and execute simulations while experienced users can write batch scripts to automate and customize this process. The toolbox is freely available at http://mialab.mm.org/software together with sample scripts and tutorials. (C) 2011 Elsevier Inc. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据