Signal and Image Processing Institute, University of Southern California, Los Angeles 90089 USA.
Neuroscience Graduate Program, University of Southern California, Los Angeles 90089 USA.
Med Image Anal. 2020 Apr;61:101635. doi: 10.1016/j.media.2020.101635. Epub 2020 Jan 7.
Characterizing functional brain connectivity using resting functional magnetic resonance imaging (fMRI) is challenging due to the relatively small Blood-Oxygen-Level Dependent contrast and low signal-to-noise ratio. Denoising using surface-based Laplace-Beltrami (LB) or volumetric Gaussian filtering tends to blur boundaries between different functional areas. To overcome this issue, a time-based Non-Local Means (tNLM) filtering method was previously developed to denoise fMRI data while preserving spatial structure. The kernel and parameters that define the tNLM filter need to be optimized for each application. Here we present a novel Global PDF-based tNLM filtering (GPDF) algorithm that uses a data-driven kernel function based on a Bayes factor to optimize filtering for spatial delineation of functional connectivity in resting fMRI data. We demonstrate its performance relative to Gaussian spatial filtering and the original tNLM filtering via simulations. We also compare the effects of GPDF filtering against LB filtering using individual in-vivo resting fMRI datasets. Our results show that LB filtering tends to blur signals across boundaries between adjacent functional regions. In contrast, GPDF filtering enables improved noise reduction without blurring adjacent functional regions. These results indicate that GPDF may be a useful preprocessing tool for analyses of brain connectivity and network topology in individual fMRI recordings.
使用静息功能磁共振成像 (fMRI) 来描述功能脑连接具有挑战性，这是由于血氧水平依赖对比度相对较小和信噪比低。使用基于表面的拉普拉斯-贝尔特拉米 (LB) 或容积高斯滤波进行去噪往往会使不同功能区域之间的边界模糊。为了克服这个问题，先前开发了基于时间的非局部均值 (tNLM) 滤波方法来对 fMRI 数据进行去噪，同时保留空间结构。定义 tNLM 滤波器的核和参数需要针对每个应用进行优化。在这里，我们提出了一种新颖的基于全局 PDF 的 tNLM 滤波 (GPDF) 算法，该算法使用基于贝叶斯因子的数据驱动核函数来优化滤波，以实现静息 fMRI 数据中功能连接的空间描绘。我们通过模拟来展示其相对于高斯空间滤波和原始 tNLM 滤波的性能。我们还比较了 GPDF 滤波与使用个体静息 fMRI 数据集的 LB 滤波的效果。我们的结果表明，LB 滤波往往会使相邻功能区域之间的信号模糊。相比之下，GPDF 滤波可以在不模糊相邻功能区域的情况下实现更好的降噪效果。这些结果表明，GPDF 可能是个体 fMRI 记录中脑连接和网络拓扑分析的有用预处理工具。
