Wavelets and functional magnetic resonance imaging of the human brain
Wavelets and functional magnetic resonance imaging of the human brain
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DOI：
10.1016/j.neuroimage.2004.07.012
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发表时间：
2004-01-01
期刊：
影响因子：
5.7
通讯作者：
Breakspear, M
中科院分区：
文献类型：
--
作者：
Bullmore, ET;Fadili, J;Breakspear, M
离散小波变换（DWT）被广泛用于混血分析和去相关或“非组织时间序列和空间过程的“白氨酸”。小波自然适合分析生物学数据，例如人脑的功能磁共振图像，通常演示规模不变或分形特性。我们简要介绍了DWT的关键特性fMRI。我们尤其关注三个应用：（i）小波系数重新降低或“ 1-D时间序列的波弹力”，2至3-D空间图和4-D时空过程； （ii）假设误差是分数高斯噪声（FGN）的时间序列回归模型的基于小波的估计器； （iii）在频繁主义者和贝叶斯框架中的小波收缩，以支持空间扩展统计图的多解决假设测试。我们得出的结论是，小波域是增强人类fMRI数据统计分析力量的新概念和技术的丰富来源。 （c）2004 Elsevier Inc.保留所有权利。
The discrete wavelet transform (DWT) is widely used for muldresolution analysis and decorrelation or "whitenine' of nonstationary time series and spatial processes. Wavelets are naturally appropriate for analysis of biological data, such as functional magnetic resonance images of the human brain, which often demonstrate scale invariant or fractal properties. We provide a brief formal introduction to key properties of the DWT and review the growing literature on its application to fMRI. We focus on three applications in particular: (i) wavelet coefficient resampting or "wavestrapping' of 1-D time series, 2-to 3-D spatial maps and 4-D spatiotemporal processes; (ii) wavelet-based estimators for signal and noise parameters of time series regression models assuming the errors are fractional Gaussian noise (fGn); and (iii) wavelet shrinkage in frequentist and Bayesian frameworks to support multiresolution hypothesis testing on spatially extended statistic maps. We conclude that the wavelet domain is a rich source of new concepts and techniques to enhance the power of statistical analysis of human fMRI data. (C) 2004 Elsevier Inc. All rights reserved.