Detecting and adjusting for artifacts in fMRI time series data
Detecting and adjusting for artifacts in fMRI time series data
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DOI：
10.1016/j.neuroimage.2005.04.039
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发表时间：
2005-09-01
期刊：
影响因子：
5.7
通讯作者：
Shadmehr, R
中科院分区：
文献类型：
--
作者：
Diedrichsen, J;Shadmehr, R
我们提出一种新方法，用于检测和校正功能性磁共振成像（fMRI）时间序列数据中的噪声和伪影。我们注意到，在fMRI时间序列数据的理论处理中至关重要的平稳方差假设在实际中经常被违反。诸如眼睛、嘴巴或手臂运动等偶发事件会在整个图像中以空间全局模式增加噪声，导致非平稳噪声过程。我们推导出一种约束最大似然（ReML）算法，该算法估计时间序列中每幅图像的噪声方差。然后，这些方差参数被用于获得线性模型回归参数的加权最小二乘估计。我们将这种方法应用于一个具有组块设计的典型fMRI实验，并表明不同图像的噪声估计差异很大，而且我们的方法能够检测到受伪影影响的图像并对其进行适当加权。此外，我们表明噪声过程具有全局空间分布，并且方差的增加是相乘的而不是相加的。新算法显著提高了检测激活区域的能力的灵敏度。这种新方法对于涉及特殊人群（例如儿童或老年人）的研究可能特别有用，因为在这些人群中更有可能发生偶发的、产生伪影的事件。（c）2005爱思唯尔公司。保留所有权利。
We present a new method to detect and adjust for noise and artifacts in functional MRI time series data. We note that the assumption of stationary variance, which is central to the theoretical treatment of fMRI time series data, is often violated in practice. Sporadic events such as eye, mouth, or arm movements can increase noise in a spatially global pattern throughout an image, leading to a nonstationary noise process. We derive a restricted maximum likelihood (ReML) algorithm that estimates the variance of the noise for each image in the time series. These variance parameters are then used to obtain a weighted least squares estimate of the regression parameters of a linear model. We apply this approach to a typical fMRI experiment with a block design and show that the noise estimates strongly vary across different images and that our method detects and appropriately weights images that are affected by artifacts. Furthermore, we show that the noise process has a global spatial distribution and that the variance increase is multiplicative rather than additive. The new algorithm results in significantly increased sensitivity in the ability to detect regions of activation. The new method may be particularly useful for studies that involve special populations (e.g., children or elderly) where sporadic, artifact-generating events are more likely. (c) 2005 Elsevier Inc. All rights reserved.