北京生物醫(yī)學(xué)工程

基于靜息態(tài)腦電圖的偏頭痛患者腦網(wǎng)絡(luò)變化

Brain network changes of patients with migraine based on resting state electroencephalogram

作者：周燕權(quán)利阮江海

單位：簡陽市人民醫(yī)院神經(jīng)內(nèi)科(四川簡陽641400)； 西南醫(yī)科大學(xué)附屬醫(yī)院神經(jīng)內(nèi)科(四川瀘州 646000) 通信作者：阮江海0 E-mail ：jianghai.man@ swmu.edu.cn

關(guān)鍵詞：偏頭痛;腦網(wǎng)絡(luò);圖論分析;默認模式網(wǎng)絡(luò);靜息態(tài);腦電圖

分類號：R318. 04

出版年·卷·期（頁碼）：2022·41·1（17-23）

摘要：

目的對偏頭痛患者靜息態(tài)腦電進行圖論及默認模式網(wǎng)絡(luò)(default mode network,DMN)連接存在的可能改變進行分析。方法首先獲取2016年1月至2019年6月在簡陽市人民醫(yī)院神經(jīng)內(nèi)科接受16導(dǎo)常規(guī)腦電圖(electroencephalogram,EEG)檢查已經(jīng)臨床診斷為偏頭痛但腦電圖結(jié)果未見異常的患者73例作為病例組，同時選取73例年齡、性別匹配的健康者作為對照組，截取兩組受試者靜息態(tài) EEG數(shù)據(jù)進行分析，然后對數(shù)據(jù)進行預(yù)處理，預(yù)處理后通過相位同步分析方法計算鎖相值(phase locking value,PLV)構(gòu)建電極導(dǎo)聯(lián)連接矩陣，并計算網(wǎng)絡(luò)屬性參數(shù),再運用精確低分辨率電磁斷層成像 (exact low resolution brain electromagnetic tomography, eLORETA)方法探索兩組受試者 DMN 連接差異。結(jié)果與健康者相比，偏頭痛組在全頻段、delta頻段及betal頻段,左側(cè)額區(qū)與右側(cè)頂區(qū)存在大量連接增強的邊;在theta頻段，左側(cè)額區(qū)與左側(cè)頂枕區(qū)之間存在少量連接減弱的邊。圖論分析結(jié)果顯示，病例組EEG的聚類系數(shù)、全局效率和局部效率高于健康組(P<0. 05)。兩組受試者DMN網(wǎng)絡(luò)連接的差異主要表現(xiàn)在alpha2頻段:在該頻段,偏頭痛組患者雙側(cè)頂下小葉之間的連接顯著增強(P<0. 05)。結(jié)論偏頭痛伴隨著腦網(wǎng)絡(luò)連接的異常改變。通過對靜息態(tài)EEG進行網(wǎng)絡(luò)連接分析，可能識別臨床腦電報告中不能發(fā)現(xiàn)的潛在異常，這可為今后偏頭痛的臨床診治提供新的思路。

Objective To explore the changes of brain network and default mode network ( DMN) in patients with Migraine. Methods We reviewed the subjects who received EEG examination in the department of Neurology in JianYang People' s Hospital from January 2016 to June 2019. 73 patients were diagnosed with Migraine and had normal EEG results were included. 73 age-and sex-matched healthy physical examination controls were included as control group. The resting state EEG signals of the two groups of subjects were intercepted for analysis. Phase locking value ( PLV) was calculated by phase synchronization analysis method to construct electrode lead connection matrix, and calculate network attribute parameters ( clustering coefficient, path length, global efficiency, local efficiency) , and then exact low-resolution brain electromagnetic tomography ( Exact low resolution brain electromagnetic tomography, eLORETA) software was employed to analyzes the difference in DMN connection between the two groups of subjects. Results The results of graph theory analysis showed that the clustering coefficients, global and local efficiencies between the EEG leads of Migraine were higher than those of the healthy group (P<0.05). Compared with healthy patients, patients with Migraine in the full band, delta band and betal band, there are a large number of edges with enhanced connection on the left frontal region and the right parietal region. In theta band, there are a few weakened edges between the left frontal region and the left top pillow region. The eLORETA method was used to analyze the default mode network connection. The results showed that the difference in the DMN network connection between the two groups of subjects was mainly in the alpha2 band: the connection between the bilateral inferior parietal lobule of the patients Migraine was significantly enhanced ( P < 0. 05 ). Conclusions Migraine is accompanied by abnormal changes in brain network. By analyzing the resting state of the EEG through graph theory and functional connection analysis, it is possible to identify potential abnormalities that cannot be found in clinical EEG report, which provides new insight for diagnosis and treatment of Migraine in future.

參考文獻：

?1. Yu S, Liu R, Zhao G, et al. The prevalence and burden of primary headaches in china: a population-based door-to-door survey[J]. Headache: The Journal of Head and Face Pain, 2012, 52(4): 582-591.

?2. Headache Classification Committee of the International Headache Society (IHS).The international classification of headache disorders [J]. 3rd edition.?Cephalalgia, 2018, 38(1): 1-211.

?3. ?Buzsaki G, Draguhn A. Neuronal oscillations in cortical networks[J].Science,2004, 304(5679): 1926-1929.

?4. Salinas E, Sejnowski TJ. Correlated neuronal activity and the flow of neural information[J].?:Nature Reviews Neuroscience, 2001, 2(8): 539-550.

?5. 李明愛，南琳，孫炎珺. 基于鎖相值和圖論的腦功能網(wǎng)絡(luò)特征提取方法[J]. 北京生物醫(yī)學(xué)工程,2019, 38(1): 15-21.

?Li MA, Nan L，Sun YF. Feature extraction method of brain functional network based on phase-locked value and graph theory[J]. Beijing Biomedical Engineering, 2019.38 (1): 15-21.

?6. Aydore S, Pantazis D, Leahy RM. A note on the phase locking value and its properties[J]. Neuroimage, 2013, 74: 231-244.

?7. Yuan Y, Pang N, Chen Y, et al. A phase-locking analysis of neuronal firing rhythms with transcranial magneto-acoustical stimulation based on the hodgkin-huxley neuron model[J]. Front Comput Neurosci, 2017, 11: 1.

?8. Jian W, Chen M, Mcfarland D J. Use of phase-locking value in sensorimotor rhythm-based brain-computer interface: zero-phase coupling and effects of spatial filters[J]. Computer Science, Interdisciplinary Applications, 2017, 55(11): 1915-1926.

9. de Tommaso M, Trotta G, Vecchio E, et al. Brain networking analysis in migraine with and without aura[J]. Journal of Headache and Pain, 2017, 18(1): 98.

10. Farago P, Tuka B, Toth E, et al. Interictal brain activity differs in migraine with and without aura: resting state fMRI study[J]. Journal of Headache and Pain, 2017, 18(1): 8.

11. Chen Z, Chen X, Liu M, et al. Altered functional connectivity of amygdala underlying the neuromechanism of migraine pathogenesis[J]. Journal of Headache and Pain, 2017, 18(1): 7.

12. Zhang J, Su J, Wang M, et al. Increased default mode network connectivity and increased regional homogeneity in migraineurs without aura[J]. Journal of Headache and Pain, 2016, 17(1): 98.

13. Pascual-Marqui RD, Lehmann D, Koukkou M, et al. Assessing interactions in the brain with exact low-resolution electromagnetic tomography[J]. Philosophical transactions - Royal Society. Mathematical, Physical and Engineering Sciences, 2011, 369(1952): 3768-3784.

14. Ikeda S, Ishii R, Pascual-Marqui RD, et al. Automated source estimation of scalp EEG epileptic activity using eLORETA kurtosis analysis[J]. Neuropsychobiology, 2019, 77(2): 101-109.

15. Fuchs M, Kastner J, Wagner M, et al. A standardized boundary element method volume conductor model[J]. Clinical Neurophysiology, 2002, 113(5): 702-712.

16. Mazziotta J, Toga A, Evans A, et al. A probabilistic atlas and reference system for the human brain: international consortium for brain mapping (ICBM)[J]. Philosophical transactions of the Royal Society of London. Series B, Biological Sciences, 2001, 356(1412): 1293-1322.

17. Vitacco D, Brandeis D, Pascual-Marqui R, et al. Correspondence of event-related potential tomography and functional magnetic resonance imaging during language processing[J]. Human Brain Mapping, 2002, 17(1): 4-12.

18. Mulert C, Jager L, Schmitt R, et al. Integration of fMRI and simultaneous EEG: towards a comprehensive understanding of localization and time-course of brain activity in target detection[J]. Neuroimage, 2004, 22(1): 83-94.

19. Harrison B J, Pujol J, Lopez-Sola M, et al. Consistency and functional specialization in the default mode brain network[J]. Proceedings of the National Academy of Sciences of the United States of America，2008，105（28）：9781:9786.

20. Whitton AE, Deccy S, Ironside M L, et al. Electroencephalography source functional connectivity reveals abnormal high-frequency communication among large-scale functional networks in depression[J]. Cognitive Control Network Homogeneity and Executive Functions in Late-Life Depression, 2018, 3(1): 50-58.

21. Hata M, Kazui H, Tanaka T, et al. Functional connectivity assessed by resting state EEG correlates with cognitive decline of Alzheimer's disease - An eLORETA study[J]. Clinical Neurophysiology, 2016, 127(2): 1269-1278.

22. Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples[J]. Human Brain Mapping ，2002, 15(1):?1-25.

23. Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples[J].?Human Brain Mapping, 2002, 15(1): 1-25.

24. Babiloni C, Babiloni F, Carducci F, et al. Functional frontoparietal connectivity during short-term memory as revealed by high-resolution EEG coherence analysis[J]. Behavioral Sciences, 2004, 118(4): 687-697.

25. Charles A, Brennan KC. The neurobiology of migraine[J]. Handbook of Clinical Neurology, 2010, 97: 99-108.

26. Bzdok D, Hartwigsen G, Reid A, et al. Left inferior parietal lobe engagement in social cognition and language[J]. Neuroscience and Biobehavioral Reviews, 2016, 68: 319-334.

27. Arora A, Weiss B, Schurz M, et al. Left inferior-parietal lobe activity in perspective tasks: identity statements[J]. Frontiers in Human Neuroscience, 2015, 9: 360.

28. Favoni V, Sambati L, Oppi F, et al. Recurrent reversible cognitive impairment in a cluster headache patient: a case report[J]. Headache: The Journal of Head and Face Pain, 2018, 58(9): 1472-1474.

29. Qu P, Yu J, Xia L, et al. Cognitive performance and the alteration of neuroendocrine hormones in chronic tension-type headache[J]. Pain Practice, 2018, 18(1): 8-17.

30. Mathur VA, Khan S A, Keaser M L, et al. Altered cognition-related brain activity and interactions with acute pain in migraine[J]. NeuroImage: Clinical, 2015, 7: 347-358.

31. 鄒燕，王松偉，李霽，等. 腦默認模式網(wǎng)絡(luò)顯示臨床慢性疼痛程度變化的研究[J]. 中國醫(yī)學(xué)計算機成像雜志,2017, 23(6): 504-507.

?Zou Y, Wang SW, Li G, et al. Study on the change of clinical chronic pain degree shown by brain default mode network [J]. Chinese Journal of Medical computer Imaging, 2017,23 (6): 504-507.

服務(wù)與反饋：

【文章下載】【加入收藏】

提示：您還未登錄，請登錄！點此登錄

51黑料吃瓜在线观看,51黑料官网|51黑料捷克街头搭讪_51黑料入口最新视频