Bootstrap generation and evaluation of an fMRI simulation database.
Bootstrap generation and evaluation of an fMRI simulation database.
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DOI：
10.1016/j.mri.2009.05.034
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发表时间：
2009-12
影响因子：
2.5
通讯作者：
Evans, Alan C.
中科院分区：
文献类型：
--
作者：
Bellec, Pierre;Perlbarg, Vincent;Evans, Alan C.
计算机模拟在功能性磁共振成像（fMRI）研究中发挥了关键作用，特别是在验证新的数据分析方法方面。许多方法已被用于生成功能磁共振成像模拟，但目前还没有通用的框架来评估这些方法中的每一种方法的真实性。在本文中，一个被称为参数引导的统计技术被用来生成一个模拟数据库，该数据库模仿在一个真实的数据库中找到的参数，其中包括40个主题和5个任务。通过比较真实的和模拟数据库之间的一组统计测量值的分布来评估模拟。两个流行的仿真模型进行了评估，首次通过应用自举框架。第一个模型是多个组件的添加剂混合物，第二个模型实现了非线性运动过程。在这两个模型中，模拟的组件包括以下大脑动力学：基线，生理噪声，神经激活和随机噪声。这些模型被发现成功地再现相对方差的组件和时间自相关的fMRI时间序列。相比之下，空间自相关的水平被发现是非常低的使用添加剂模型。有趣的是，在第二个模型中的运动过程中产生了一些缓慢的时间漂移，并增加了空间自相关的水平。这些实验表明，引导框架是一个强大的新工具，可以精确定位各自的优势和局限性的仿真模型。
Computer simulations have played a critical role in functional magnetic resonance imaging (fMRI) research, notably in the validation of new data analysis methods. Many approaches have been used to generate fMRI simulations, but there is currently no generic framework to assess how realistic each one of these approaches may be. In this paper, a statistical technique called parametric bootstrap was used to generate a simulation database that mimicked the parameters found in a real database, which comprised 40 subjects and 5 tasks. The simulations were evaluated by comparing the distributions of a battery of stastical measures between the real and simulated databases. Two popular simulation models were evaluated for the first time by applying the bootstrap framework. The first model was an additive mixture of multiple components and the second one implemented a non-linear motion process. In both models, the simulated components included the following brain dynamics : a baseline, physiological noise, neural activation and random noise. These models were found to successfully reproduce the relative variance of the components and the temporal autocorrelation of the fMRI time series. By contrast, the level of spatial autocorrelation was found to be drastically low using the additive model. Interestingly, the motion process in the second model intrisically generated some slow time drifts and increased the level of spatial autocorrelations. These experiments demonstrated that the bootstrap framework is a powerful new tool that can pinpoint the respective strengths and limitations of simulation models.
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影响因子：
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