摘 要：为正确识别和定位致痫灶，将颞叶癫痫患者、健康对照组的脑磁图信号按照脑区进行划分，运用核格兰杰因果算法，分别计算两组人群两两脑区之间的核格兰杰因果的强度和方向，研究不同人群脑区之间的神经信息流动变化情况。结果提示，相对于健康对照组而言，颞叶癫痫患者具有更强的网络连接，且信息流多从 MLT、MRT 脑区流向其他脑区，这可为致痫灶的定位提供有力辅助依据，为癫痫的诊治提供新的研究思路与途径。
关键词：核格兰杰因果；癫痫；脑磁图
DOI:10.19850/j.cnki.2096-4706.2021.09.020
基金项目：江苏第二师范学院科学研究项目 （JSSNU18ZD01）
中图分类号：TN911.6；R318 文献标识码：A 文章编号：2096-4706（2021）09-0075-04
Nuclear Granger Causality Analysis of Epileptic Magnetoencephalogram
WANG Qiong，YU Jiahong，YU Liqin，ZHANG Qingyun
（School of Physics and Electronic Engineering，Jiangsu Second Normal University，Nanjing 211200，China）
Abstract：Aiming to correctly identify and locate the seizure focus，the magnetoencephalography signals of patients with temporallobeepilepsy and healthy control group were divided according to brain regions. The intensity and direction of nuclear Granger causality between two brain regions of the two groups were calculated by using nuclear Granger causality algorithm，and the changes of neural information flow between brain regions of different populatiseizure focus studied. The results indicate that compared with the healthy control group，patients with temporallobeepilepsy have stronger network connection，and the information flow mostly flows from MLT and MRT brain regions to other brain regions，which can provide a strong auxiliary basis for the localization of seizure focus and provide new research ideas and approaches for the diagnosis and treatment of epilepsy.
Keywords：nuclear Granger causality；epilepsy；magnetoencephalogram
参考文献：
[1] ERIC V D，ZWEIPHENNING W J E M，JANSEN F E，et al. Brain Network Organization in Focal Epilepsy：A Systematic Review and Meta-Analysis [J/OL].PloS One，2014，9（12）：1-21[2021- 03-16].https://journals.plos.org/plosone/article?id=10.1371/journal. pone.0114606.
[2] HILLEBRAND A，BARNES GR. Beamformer Analysis of MEG Data [J].International Review of Neurobiology，2005（68）： 149-171.
[3] GRANGER C. Investigating Causal Relations by Econometric Models and Cross-Spectral Methods [J].Econometrica，1969，37（3）： 424-438.
[4] DESHPANDE G，LACONTE S，PELTIER S，et al. Connectivity Analysis of Human Functional MRI Data：From Linear to Nonlinear and Static to Dynamic [C]//International Conference on Medical Imaging & Augmented Reality.Shanghai：Springer-Verlag， 2006：17-24.
[5] MARINAZZO D，PELLICORO M，STRAMAGLIA S. Nonlinear parametric model for Granger causality of time series [J/OL]. PHYSICAL REVIEW Ecovering statistical，nonlinear，biological， and soft matter physics，2006，73（6）：1-6[2021-03-16].https:// journals.aps.org/pre/abstract/10.1103/PhysRevE.73.066216.
[6] MARINAZZO D，PELLICORO M，STRAMAGLIA S. Kernel-Granger causality and the analysis of dynamical networks [J/OL]. Physical Review E，2008，77（5）：1-9.[2021-03-16].https://journals. aps.org/pre/abstract/10.1103/PhysRevE.77.056215.
[7] 杨淳沨，向文涛，伍家松，等 . 基于通用赤池信息量准则 改进维纳 - 格兰杰因果索引算法的颅内脑电效应连通性研究 [J]. 生 物医学工程学杂志，2018（5）：665-671.
作者简介：王琼（1988—），女，汉族，江苏宿迁人，讲师， 硕士，研究方向：生物医学信号分析与处理。