Ma Yuyu, Liang Xiaoyu, Wu Huanqi, Lu Hao, Li Yong, Liu Changzeng, Gao Yang, Xiang Min, Yu Dexin, Ning Xiaolin
Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, 37 Xueyuan Rd., Haidian District, Beijing 100083, China.
Hangzhou Institute of National Extremely-Weak Magnetic Field Infrastructure, 465 Binan Rd., Binjiang District, Hangzhou 310051, China.
Bioengineering (Basel). 2024 Dec 12;11(12):1258. doi: 10.3390/bioengineering11121258.
Optically pumped magnetometer magnetoencephalography (OPM-MEG) represents a novel method for recording neural signals in the brain, offering the potential to measure critical neuroimaging characteristics such as effective brain networks. Effective brain networks describe the causal relationships and information flow between brain regions. In constructing effective brain networks using Granger causality, the noise in the multivariate autoregressive model (MVAR) is typically assumed to follow a Gaussian distribution. However, in experimental measurements, the statistical characteristics of noise are difficult to ascertain. In this paper, a Granger causality method based on a cost-reference particle filter (CRPF) is proposed for constructing effective brain networks under unknown noise conditions. Simulation results show that the average estimation errors of the MVAR model coefficients using the CRPF method are reduced by 53.4% and 82.4% compared to the Kalman filter (KF) and maximum correntropy filter (MCF) under Gaussian noise, respectively. The CRPF method reduces the average estimation errors by 88.1% and 85.8% compared to the MCF under alpha-stable distribution noise and the KF method under pink noise conditions, respectively. In an experiment, the CRPF method recoversthe latent characteristics of effective connectivity of benchmark somatosensory stimulation data in rats, human finger movement, and auditory oddball paradigms measured using OPM-MEG, which is in excellent agreement with known physiology. The simulation and experimental results demonstrate the effectiveness of the proposed algorithm and OPM-MEG for measuring effective brain networks.
光泵磁力计脑磁图（OPM-MEG）是一种用于记录大脑神经信号的新方法，具有测量诸如有效脑网络等关键神经成像特征的潜力。有效脑网络描述了脑区之间的因果关系和信息流。在使用格兰杰因果关系构建有效脑网络时，通常假设多元自回归模型（MVAR）中的噪声服从高斯分布。然而，在实验测量中，噪声的统计特征很难确定。本文提出了一种基于代价参考粒子滤波器（CRPF）的格兰杰因果关系方法，用于在未知噪声条件下构建有效脑网络。仿真结果表明，在高斯噪声下，使用CRPF方法时MVAR模型系数的平均估计误差与卡尔曼滤波器（KF）和最大相关熵滤波器（MCF）相比分别降低了53.4%和82.4%。在α稳定分布噪声下，与MCF相比，CRPF方法的平均估计误差降低了88.1%；在粉红噪声条件下，与KF方法相比，平均估计误差降低了85.8%。在一项实验中，CRPF方法恢复了使用OPM-MEG测量的大鼠基准体感刺激数据、人类手指运动和听觉奇球范式中有效连接性的潜在特征，这与已知生理学非常吻合。仿真和实验结果证明了所提算法和OPM-MEG在测量有效脑网络方面的有效性。
