Yang Albert C, Tsai Shih-Jen, Lin Ching-Po, Peng Chung-Kang
Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center, Harvard Medical School, Harvard University, Boston, MA, United States.
Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.
Front Neurosci. 2018 Jun 13;12:398. doi: 10.3389/fnins.2018.00398. eCollection 2018.
Complexity analysis of resting-state blood oxygen level-dependent (BOLD) signals using entropy methods has attracted considerable attention. However, investigation on the bias of entropy estimates in resting-state functional magnetic resonance imaging (fMRI) signals and a general strategy for selecting entropy parameters is lacking. In this paper, we present a minimizing error approach to reduce the bias of sample entropy (SampEn) and multiscale entropy (MSE) in resting-state fMRI data. The strategy explored a range of parameters that minimized the relative error of SampEn of BOLD signals in cerebrospinal fluids where minimal physiologic information was present, and applied these parameters to calculate SampEn of BOLD signals in gray matter regions. We examined the effect of various parameters on the results of SampEn and MSE analyses of a large normal aging adult cohort (354 healthy subjects aged 21-89 years). The results showed that a tradeoff between pattern length and tolerance factor was necessary to maintain the accuracy of SampEn estimates. Furthermore, an increased relative error of SampEn was associated with an increased coefficient of variation in voxel-wise statistics. Overall, the parameters = 1 and = 0.20-0.45 provided reliable MSE estimates in short resting-state fMRI signals. For a single-scale SampEn analysis, a wide range of parameters was available with data lengths of at least 97 time points. This study provides a minimization error strategy for future studies on the non-linear analysis of resting-state fMRI signals to account for the bias of entropy estimates.
使用熵方法对静息态血氧水平依赖（BOLD）信号进行复杂性分析已引起了相当大的关注。然而，目前缺乏对静息态功能磁共振成像（fMRI）信号中熵估计偏差的研究以及选择熵参数的通用策略。在本文中，我们提出了一种最小化误差的方法，以减少静息态fMRI数据中样本熵（SampEn）和多尺度熵（MSE）的偏差。该策略探索了一系列参数，这些参数能使脑脊液中BOLD信号的SampEn相对误差最小化，因为脑脊液中生理信息最少，并将这些参数应用于计算灰质区域BOLD信号的SampEn。我们研究了各种参数对一个大型正常衰老成年队列（354名年龄在21 - 89岁的健康受试者）的SampEn和MSE分析结果的影响。结果表明，模式长度和容忍因子之间需要进行权衡以维持SampEn估计的准确性。此外，SampEn相对误差的增加与体素级统计中的变异系数增加相关。总体而言，参数m = 1和r = 0.20 - 0.45在短静息态fMRI信号中提供了可靠的MSE估计。对于单尺度SampEn分析，数据长度至少为97个时间点时，有多种参数可供选择。本研究为未来关于静息态fMRI信号非线性分析以考虑熵估计偏差的研究提供了一种最小化误差策略。
