Analysis of dynamic brain imaging data
Analysis of dynamic brain imaging data
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DOI：
10.1016/s0006-3495(99)77236-x
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发表时间：
1999-02-01
影响因子：
3.4
通讯作者：
Pesaran, B
中科院分区：
文献类型：
--
作者：
Mitra, PP;Pesaran, B
用于探测脑功能的现代成像技术，包括功能性磁共振成像、内在和外在对比光学成像以及脑磁图，产生具有复杂内容的大数据集。在本文中，我们开发了适当的技术，分析和可视化的成像数据分离的信号从噪声和信号的特征。开发的技术失败的一般类别的多变量时间序列分析，特别是我们广泛使用的多锥度框架的频谱分析。我们制定了具体的协议，功能磁共振成像，光学成像和脑磁图数据的分析，并说明了这些成像方式所产生的真实的数据集的应用程序的技术。一般而言，分析协议包括两个不同的阶段：“噪声”表征和抑制，以及“信号”表征和可视化。我们的研究的一个重要的一般性结论是一个基于频率的表示，与短，移动的分析窗口，占非平稳性的数据的实用程序。特别值得注意的是：1）分解技术（空间频率奇异值分解）的发展，这被证明是一个有用的手段，表征图像数据，和2）的算法的发展，基于多锥度方法，用于去除近似周期性的生理伪影所产生的心脏和呼吸源。
Modern imaging techniques for probing brain function, including functional magnetic resonance imaging, intrinsic and extrinsic contrast optical imaging, and magnetoencephalography, generate large data sets with complex content. In this paper we develop appropriate techniques for analysis and Visualization of such imaging data to separate the signal from the noise and characterize the signal. The techniques developed fail into the general category of multivariate time series analysis, and in particular we extensively use the multitaper framework of spectral analysis. We develop specific protocols for the analysis of fMRI, optical imaging, and MEG data, and illustrate the techniques by applications to real data sets generated by these imaging modalities. in general, the analysis protocols involve two distinct stages: "noise" characterization and suppression, and "signal" characterization and visualization. An important general conclusion of our study is the utility of a frequency-based representation, with short, moving analysis windows to account for nonstationarity in the data. Of particular note are 1) the development of a decomposition technique (space-frequency singular value decomposition) that is shown to be a useful means of characterizing the image data, and 2) the development of an algorithm, based on multitaper methods, for the removal of approximately periodic physiological artifacts arising from cardiac and respiratory sources.