Analyzing fMRI experiments with structural adaptive smoothing procedures
Analyzing fMRI experiments with structural adaptive smoothing procedures
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DOI：
10.1016/j.neuroimage.2006.06.029
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发表时间：
2006-10-15
期刊：
影响因子：
5.7
通讯作者：
Spokoiny, Vladimir
中科院分区：
文献类型：
--
作者：
Tabelow, Karsten;Polzehl, Joerg;Spokoiny, Vladimir
来自功能磁共振成像(FMRI)的数据由低信噪比的脑图像的时间序列组成。为了减少噪声和改善信号检测，对fMRI数据进行了空间平滑。然而，通常应用的高斯滤波器以丢失关于激活区域的空间范围和形状的信息为代价来实现这一点。我们建议使用Polzehl和Spokony介绍的传播-分离程序[Polzehl，J.，Spokoiny，V.(2006)]。局部似然估计的传播分离方法。可能吧。理论上来说。字段135、335-362]。结果表明，该方法显著改善了激活区域的空间范围和形状信息，具有相似的降噪效果。为了完成统计分析，信号检测基于随机场理论定义的阈值。通过仿真算例和实验数据分析，说明了自适应平滑和非自适应平滑的效果。(C)2006 Elsevier Inc.保留所有权利。
Data from functional magnetic resonance imaging (fMRI) consist of time series of brain images that are characterized by a low signal-to-noise ratio. In order to reduce noise and to improve signal detection, the fMRI data are spatially smoothed. However, the common application of a Gaussian filter does this at the cost of loss of information on spatial extent and shape of the activation area. We suggest to use the propagation-separation procedures introduced by Polzehl and Spokoiny [Polzehl, J., Spokoiny, V. (2006). Propagation-separation approach for local likelihood estimation. Probab. Theory Relat. Fields 135, 335-362] instead. We show that this significantly improves the information on the spatial extent and shape of the activation region with similar results for the noise reduction. To complete the statistical analysis, signal detection is based on thresholds defined by random field theory. Effects of adaptive and non-adaptive smoothing are illustrated by artificial examples and an analysis of experimental data. (c) 2006 Elsevier Inc. All rights reserved.