Smith Robert X, Jann Kay, Ances Beau, Wang Danny J J
Laboratory of FMRI Technology (LOFT), Department of Neurology, Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, California.
Department of Neurology, School of Medicine, Washington University in Saint Louis, Saint Louis, Missouri.
Hum Brain Mapp. 2015 Sep;36(9):3603-20. doi: 10.1002/hbm.22865. Epub 2015 Jun 12.
One of the major findings from multimodal neuroimaging studies in the past decade is that the human brain is anatomically and functionally organized into large-scale networks. In resting state fMRI (rs-fMRI), spatial patterns emerge when temporal correlations between various brain regions are tallied, evidencing networks of ongoing intercortical cooperation. However, the dynamic structure governing the brain's spontaneous activity is far less understood due to the short and noisy nature of the rs-fMRI signal. Here, we develop a wavelet-based regularity analysis based on noise estimation capabilities of the wavelet transform to measure recurrent temporal pattern stability within the rs-fMRI signal across multiple temporal scales. The method consists of performing a stationary wavelet transform to preserve signal structure, followed by construction of "lagged" subsequences to adjust for correlated features, and finally the calculation of sample entropy across wavelet scales based on an "objective" estimate of noise level at each scale. We found that the brain's default mode network (DMN) areas manifest a higher level of irregularity in rs-fMRI time series than rest of the brain. In 25 aged subjects with mild cognitive impairment and 25 matched healthy controls, wavelet-based regularity analysis showed improved sensitivity in detecting changes in the regularity of rs-fMRI signals between the two groups within the DMN and executive control networks, compared with standard multiscale entropy analysis. Wavelet-based regularity analysis based on noise estimation capabilities of the wavelet transform is a promising technique to characterize the dynamic structure of rs-fMRI as well as other biological signals.
过去十年多模态神经影像学研究的主要发现之一是，人类大脑在解剖学和功能上被组织成大规模网络。在静息态功能磁共振成像（rs-fMRI）中，当对不同脑区之间的时间相关性进行统计时，空间模式就会出现，这证明了持续的皮质间合作网络的存在。然而，由于rs-fMRI信号短且有噪声的特性，控制大脑自发活动的动态结构还远未被充分理解。在此，我们基于小波变换的噪声估计能力开发了一种基于小波的规律性分析方法，以测量rs-fMRI信号在多个时间尺度上的递归时间模式稳定性。该方法包括执行平稳小波变换以保留信号结构，接着构建“滞后”子序列以调整相关特征，最后基于每个尺度噪声水平的“客观”估计计算跨小波尺度的样本熵。我们发现，大脑的默认模式网络（DMN）区域在rs-fMRI时间序列中表现出比大脑其他区域更高水平的不规则性。在25名患有轻度认知障碍的老年受试者和25名匹配的健康对照中，与标准多尺度熵分析相比，基于小波的规律性分析在检测DMN和执行控制网络内两组之间rs-fMRI信号规律性变化方面显示出更高的灵敏度。基于小波变换噪声估计能力的基于小波的规律性分析是一种很有前景的技术，可用于表征rs-fMRI以及其他生物信号的动态结构。
