GLMdenoise: a fast, automated technique for denoising task-based fMRI data
GLMdenoise: a fast, automated technique for denoising task-based fMRI data
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DOI：
10.3389/fnins.2013.00247
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发表时间：
2013-01-01
影响因子：
4.3
通讯作者：
Wandell, Brian A.
中科院分区：
文献类型：
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作者：
Kay, Kendrick N.;Rokem, Ariel;Wandell, Brian A.
在基于任务的功能磁共振成像（fMRI）中，研究人员试图测量与给定任务或条件相关的fMRI信号。在许多情况下，测量该感兴趣的信号受到噪声的限制。在这项研究中，我们提出了GLMdenoise，一种技术，提高信噪比（SNR）进入噪声回归到一般线性模型（GLM）分析的功能磁共振成像数据。通过进行初始模型拟合以确定与实验范例无关的体素，对这些体素的时间序列进行主成分分析（PCA），并使用交叉验证来选择用作噪声回归量的主成分的最佳数量，来导出噪声回归量。由于使用数据恢复，GLMdenoise需要并且最适合涉及多个运行的数据集（其中条件在运行中重复）。我们表明，GLMdenoise一贯提高交叉验证精度的GLM估计各种事件相关的实验数据集，并伴随着大量的SNR增益。为促进该方法的实际应用，给出了实现GLM去噪的MATLAB代码。此外，为了帮助将GLMdenoise与其他去噪方法进行比较，我们提出了Denoise Benchmark（DNB），这是一个用于评估去噪方法的公共数据库和架构。DNB由本文中描述的数据集、能够自动评估去噪方法的代码框架以及几种去噪方法的实现组成，包括GLMdenoise、使用运动参数作为噪声回归量、基于ICA的去噪和RETROICOR/RVHRCOR。使用DNB，我们发现GLMdenoise在我们测试的所有去噪方法中表现最好。
In task-based functional magnetic resonance imaging (fMRI), researchers seek to measure fMRI signals related to a given task or condition. In many circumstances, measuring this signal of interest is limited by noise. In this study, we present GLMdenoise, a technique that improves signal-to-noise ratio (SNR) by entering noise regressors into a general linear model (GLM) analysis of fMRI data. The noise regressors are derived by conducting an initial model fit to determine voxels unrelated to the experimental paradigm, performing principal components analysis (PCA) on the time-series of these voxels, and using cross-validation to select the optimal number of principal components to use as noise regressors. Due to the use of data resampling, GLMdenoise requires and is best suited for datasets involving multiple runs (where conditions repeat across runs). We show that GLMdenoise consistently improves cross-validation accuracy of GLM estimates on a variety of event-related experimental datasets and is accompanied by substantial gains in SNR. To promote practical application of methods, we provide MATLAB code implementing GLMdenoise. Furthermore, to help compare GLMdenoise to other denoising methods, we present the Denoise Benchmark (DNB), a public database and architecture for evaluating denoising methods. The DNB consists of the datasets described in this paper, a code framework that enables automatic evaluation of a denoising method, and implementations of several denoising methods, including GLMdenoise, the use of motion parameters as noise regressors, ICA-based denoising, and RETROICOR/RVHRCOR. Using the DNB, we find that GLMdenoise performs best out of all of the denoising methods we tested.