Temporal non-local means filtering for studies of intrinsic brain connectivity from individual resting fMRI
Temporal non-local means filtering for studies of intrinsic brain connectivity from individual resting fMRI
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DOI：
10.1016/j.media.2020.101635
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发表时间：
2020-04-01
影响因子：
10.9
通讯作者：
Leahy, Richard M.
中科院分区：
文献类型：
--
作者：
Li, Jian;Choi, Soyoung;Leahy, Richard M.
使用静息功能磁共振成像（fMRI）表征功能性脑连接是具有挑战性的，这是由于相对小的血氧水平依赖对比度和低信噪比。使用基于表面的Laplace-Beltrami（LB）或体积高斯滤波的去噪倾向于模糊不同功能区域之间的边界。为了克服这个问题，一个基于时间的非局部均值（tNLM）滤波方法先前开发的功能磁共振成像数据去噪，同时保持空间结构。定义tNLM过滤器的内核和参数需要针对每个应用程序进行优化。在这里，我们提出了一种新的全球PDF为基础的tNLM滤波（GPDF）算法，使用数据驱动的核函数的基础上贝叶斯因子，以优化过滤的空间描绘休息功能性磁共振成像数据的功能连接。我们通过仿真证明了它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过滤对LB过滤使用个人在体内静息功能磁共振成像数据集的影响。我们的研究结果表明，LB滤波倾向于模糊相邻功能区域之间的边界信号。相比之下，GPDF滤波能够在不模糊相邻功能区域的情况下实现改进的降噪。这些结果表明，GPDF可能是一个有用的预处理工具，分析大脑的连接和网络拓扑结构在个人的功能磁共振成像记录。(C)2020爱思唯尔B. V.保留所有权利。
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. (C) 2020 Elsevier B.V. All rights reserved.