Disentangling Multispectral Functional Connectivity With Wavelets
Disentangling Multispectral Functional Connectivity With Wavelets
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DOI：
10.3389/fnins.2018.00812
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发表时间：
2018-11-06
影响因子：
4.3
通讯作者：
Keilholz, Shella
中科院分区：
文献类型：
--
作者：
Billings, Jacob C. W.;Thompson, Garth J.;Keilholz, Shella
脑连接组学领域通过表征自发脑活动的趋势来发展我们对大脑内在组织的理解。自发血氧水平依赖性功能磁共振成像（BOLD-fMRI）波动的线性相关性通常被用作功能连接（FC）的度量，也就是说，作为描述两个大脑区域随着时间的推移如何相似的数量。鉴于BOLD-fMRI信号的自然频谱标度，将BOLD-fMRI表示为在多个尺度上发生的多个过程可能是有用的。小波域提出了一个变换空间，非常适合于多尺度系统的检查，小波基集是从一个自相似的时间和频率界定的内核重新缩放构造。在本研究中，我们利用小波变换来研究波动的全脑BOLD-fMRI连接的小波光谱尺度的函数在一个样本（N = 31）的休息健康的人类志愿者。信息论准则测量谱定界FC图之间的相关性。光谱间图形结构的体素比较说明了跨光谱带的优先功能网络的发展。
The field of brain connectomics develops our understanding of the brain's intrinsic organization by characterizing trends in spontaneous brain activity. Linear correlations in spontaneous blood-oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI) fluctuations are often used as measures of functional connectivity (FC), that is, as a quantity describing how similarly two brain regions behave over time. Given the natural spectral scaling of BOLD-fMRI signals, it may be useful to represent BOLD-fMRI as multiple processes occurring over multiple scales. The wavelet domain presents a transform space well suited to the examination of multiscale systems as the wavelet basis set is constructed from a self-similar rescaling of a time and frequency delimited kernel. In the present study, we utilize wavelet transforms to examine fluctuations in whole-brain BOLD-fMRI connectivity as a function of wavelet spectral scale in a sample (N = 31) of resting healthy human volunteers. Information theoretic criteria measure relatedness between spectrally-delimited FC graphs. Voxelwise comparisons of between-spectra graph structures illustrate the development of preferential functional networks across spectral bands.