基于多尺度排列熵的精神分裂症MEG信号分析

doi:10.3969/j.issn.0258-8021.2016.06.004

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摘要通过应用多尺度排列熵分析方法对精神分裂症患者和健康正常人的静息态脑磁图(MEG)信号进行信号的复杂性定量分析与对比,发现:在两组样本的多尺度排列熵均值比较中,精神分裂症患者93.82%通道上的多尺度排列熵值高于正常对照组;精神分裂症患者的MEG信号的多尺度排列熵在19个通道上存在显著性差异(P<0.05,FDR校正),这些差异通道集中分布在脑区上的颞叶和额叶等部位。这个发现与既往的MRI和EEG等神经影像学对精神分裂症的研究结果相一致。这些特征提示MEG信号的复杂性测度或许可以为精神分裂症患者的诊断及判别提供新的辅助参考依据,有助于了解及分析精神分裂症的症状表现及其病理机制,为精神分裂症的病理分析和临床诊断方法的研究提供新的研究思路与途径。

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	陈振宇黄晓霞

关键词 ：脑磁图, 精神分裂症, 静息态, 多尺度排列熵

Abstract：Multiscale permutation entropy was utilized to analyze complexity measure of resting state MEG recordings from schizophrenic patients and healthy control subjects in this paper. By comparing and analyzing the mean value of multiscale permutation entropy between schizophrenia and control subjects, we found that multiscale permutation entropy of the schizophrenia′s MEG in 93.82% channels was higher than that of the control subjects. In addition, the experimental result showed that multiscale permutation entropy of schizophrenics′ MEG recordings had significant differences in 19 channels (P<0.05, FDR correction). These channels were mainly distributed on the temporal and frontal, especially the edge region of temporal, which was consistent with the results of previous schizophrenia neuroimaging studies such as MRI and EEG. These features can be served to provide important reference for the further researches of schizophrenia pathological mechanism analysis and clinical diagnosis method.

Key words： MEG schizophrenia resting state multiscale permutation entropy

收稿日期: 2015-12-25

PACS:

R318

基金资助:教育部留学回国人员科研启动基金(教外司留[2014]1685号)

引用本文:

陈振宇黄晓霞. 基于多尺度排列熵的精神分裂症MEG信号分析[J]. 中国生物医学工程学报, 2016, 35(6): 665-670.
Chen Zhenyu Huang Xiaoxia. Magnetoencephalography Analysis of Schizophrenia Using Multiscale Permutation Entropy. Chinese Journal of Biomedical Engineering, 2016, 35(6): 665-670.

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http://cjbme.csbme.org/CN/10.3969/j.issn.0258-8021.2016.06.004 或 http://cjbme.csbme.org/CN/Y2016/V35/I6/665

[1] Insel TR. Rethinking schizophrenia[J]. Nature, 2010,468(7321):187-193.
[2] 安德鲁斯. 精神障碍的处理[M].3版. 肖泽萍,徐一峰,译.上海:上海科学技术出版社,2002:304-307.
[3] 栗文靖, 许培扬, 纪志刚. 精神分裂症研究进展[J]. 实用临床医药杂志, 2011, 15(5):123-125.
[4] Vrba J. Magnetoencephalography: the art of finding a needle in a haystack[J]. Physica C Superconductivity, 2002, 368(1):1-9.
[5] Babloyantz A. Evidence of chaotic dynamics of brain activity during the sleep cycle[J]. Physics Letters A, 1985, 111(3):152-156.
[6] Anninos PA, Adamopoulos AV, Kotini A, et al. Nonlinear analysis of brain activity in magnetic influenced Parkinson patients.[J]. Brain Topography, 2000, 13(2):135-144.
[7] Jeong J, Kim DJ, Chae JH, et al. Nonlinear analysis of the EEG of schizophrenics with optimal embedding dimension.[J]. Medical Engineering & Physics, 1998, 20(9):669-676.
[8] Christoph B, Bernd P. Permutation entropy: a natural complexity measure for time series.[J]. Physical Review Letters, 2002, 88 (17): 174102.
[9] Rathleff MS, Samani A, Olesen CG, et al. Inverse relationship between the complexity of midfoot kinematics and muscle activation in patients with medial tibial stress syndrome.[J]. Journal of Electromyography& Kinesiology, 2011, 21(4):638-644.
[10] Taherkhani F, Rahmani M, Taherkhani F, et al. Permutation entropy and detrend fluctuation analysis for the natural complexity of cardiac heart interbeat signals[J]. Physica A Statistical Mechanics & Its Applications, 2013, 392(14):3106-3112.
[11] Nicolaou N, Georgiou J. The use of permutation entropy to characterize sleep electroencephalograms.[J]. Clinical Eeg & Neuroscience, 2011, 42(42):24-28.
[12] Fehr T, Kissler J, Moratti S, et al. Source distribution of neuromagnetic slow waves and MEG-delta activity in schizophrenic patients.[J]. Biol Psychiatry, 2001, 50(2):108-116(9).
[13] Wienbruch C, Moratti S, Elbert T, et al. Source distribution of neuromagnetic slow wave activity in schizophrenic and depressive patients[J]. Clinical Neurophysiology, 2003, 114(11):2052-2060.
[14] Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of complex physiologic time series.[J]. Physical Review Letters, 2002, 89(6):705-708.
[15] Nicolaou N, Georgiou J. Permutation Entropy: A new feature for Brain-Computer Interfaces[C]//2010 IEEE. Biomedical Circuits and Systems Conference (BioCAS) Paphos: IEEE, 2010:49-52.
[16] Ouyang Gaoxiang, Li Jing, Liu Xianzeng, et al. Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis[J]. Epilepsy Research, 2013, 104(3):246-252.
[17] 王春方, 孙长城, 王勇军,等. 精神障碍疾病的神经生理信号复杂度研究进展[J]. 国际生物医学工程杂志, 2015, 38(5):310-313.
[18] Fernández A, Gómez C, Hornero R, et al. Complexity and schizophrenia[J]. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 2012, 45:267-276.
[19] Robyn H, Crow TJ, Dick P, et al. Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies.[J]. American Journal of Psychiatry, 2006, 162(12):2233-2245.
[20] Noriomi K, Shenton ME, Salisbury DF, et al. Middle and inferior temporal gyrus gray matter volume abnormalities in first-episode schizophrenia: an MRI study.[J]. American Journal of Psychiatry, 2006, 163(12): 2103-2110.
[21] Liu Haihong, Liu Zhening, Liang Meng, et al. Decreased regional homogeneity in schizophrenia: a resting state functional magnetic resonance imaging study.[J]. Neuroreport, 2006, 17(1):19-22
[22] 胡茂荣. 精神分裂症认知、脑灰质和白质内表型研究[D]. 长沙:中南大学, 2012.
[23] Kanaan RAA, Jin-Suh K, Kaufmann WE, et al. Diffusion tensor imaging in schizophrenia.[J]. Biological Psychiatry, 2006, 58(12): 921-929.
[24] Kim DJ, Jeong J, Chae JH, et al. An estimation of the first positive Lyapunov exponent of the EEG in patients with schizophrenia.[J]. Psychiatry Research, 2000, 98(3):177-189.