临床荟萃 ›› 2022, Vol. 37 ›› Issue (8): 748-752.doi: 10.3969/j.issn.1004-583X.2022.08.014
李梓浩1, 吴美妮1, 尹昌浩2, 吴天娇2, 赵维纳2()
收稿日期:
2022-03-26
出版日期:
2022-08-20
发布日期:
2022-09-26
通讯作者:
赵维纳
E-mail:weinzhao@126.com
基金资助:
Received:
2022-03-26
Online:
2022-08-20
Published:
2022-09-26
摘要:
轻度认知障碍(mild cognitive impairment, MCI)是认知功能从正常发展到痴呆的一个阶段,由于其临床预后差,晚期发展到痴呆后缺少有效的治疗方式,故早期对MCI进行识别、评估及干预显得尤为重要。目前,临床中对MCI的识别主要采用神经心理学测试和结构影像学相结合的方法,但无法反映患者神经代谢方面的病理变化。脑电图(electroencephalogram,EEG)可记录和可视化人类的脑电活动,脑电节律的测量可以揭示人类皮层锥体神经元活动的神经同步和去同步机制,反映大脑调节唤醒、警觉及认知功能的状态,对MCI的早期识别起到重要作用。本研究通过对EEG在MCI中的研究进展进行综述,旨在进一步提高MCI的早期识别。
中图分类号:
李梓浩, 吴美妮, 尹昌浩, 吴天娇, 赵维纳. 脑电图在轻度认知功能障碍中的研究进展[J]. 临床荟萃, 2022, 37(8): 748-752.
疾病分类 | 类型 | 特异性改变 |
---|---|---|
MCI | rsEEG | 枕叶顶叶:α功率下降、δ和θ波功率升高、δ和θ波功率比值升高[ |
ERP | P300潜伏期延长[ | |
脑网络 | α频段中,EEG电流源密度连接性降低[ | |
aMCI | rsEEG | 顶叶:β1频段功率下降[ |
ERP | N200和P300的潜伏期增加[ | |
脑网络 | 左侧额叶与枕叶之间、左侧中央区与内侧顶叶之间以及左侧中央与右侧顶叶脑区之间的区域间连接水平降低[ | |
AD | rsEEG | 额叶及颞叶:θ波功率升高[ |
ERP | P200、N100、N200和P300的潜伏期延长、β-ERS振幅降低[ | |
脑网络 | PC神经活动增加,β频段的脑振荡增强,PC和额叶内侧区域之间的功能连接水平改变[ |
表1 认知障碍严重程度与脑电图相关特异性改变
疾病分类 | 类型 | 特异性改变 |
---|---|---|
MCI | rsEEG | 枕叶顶叶:α功率下降、δ和θ波功率升高、δ和θ波功率比值升高[ |
ERP | P300潜伏期延长[ | |
脑网络 | α频段中,EEG电流源密度连接性降低[ | |
aMCI | rsEEG | 顶叶:β1频段功率下降[ |
ERP | N200和P300的潜伏期增加[ | |
脑网络 | 左侧额叶与枕叶之间、左侧中央区与内侧顶叶之间以及左侧中央与右侧顶叶脑区之间的区域间连接水平降低[ | |
AD | rsEEG | 额叶及颞叶:θ波功率升高[ |
ERP | P200、N100、N200和P300的潜伏期延长、β-ERS振幅降低[ | |
脑网络 | PC神经活动增加,β频段的脑振荡增强,PC和额叶内侧区域之间的功能连接水平改变[ |
[1] |
Petersen RC, Doody R, Kurz A, et al. Current concepts in mild cognitive impairment[J]. Arch Neurol, 2001, 58(12): 1985-1992.
doi: 10.1001/archneur.58.12.1985 URL |
[2] |
Canevelli M, Grande G, Lacorte E, et al. Spontaneous reversion of mild cognitive impairment to normal cognition: A systematic review of literature and meta-analysis[J]. J Am Med Dir Assoc, 2016, 17(10):943-948.
doi: 10.1016/j.jamda.2016.06.020 pmid: 27502450 |
[3] |
Zhuang L, Yang Y, Gao J. Cognitive assessment tools for mild cognitive impairment screening[J]. J Neurol, 2021, 268(5):1615-1622.
doi: 10.1007/s00415-019-09506-7 URL |
[4] |
Ingber L, Nunez PL. Neocortical dynamics at multiple scales: EEG standing waves, statistical mechanics, and physical analogs[J]. Math Biosci, 2011, 229(2):160-173.
doi: 10.1016/j.mbs.2010.12.003 pmid: 21167841 |
[5] |
Babiloni C, Barry RJ, Başar E, et al. International Federation of Clinical Neurophysiology (IFCN)-EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies[J]. Clin Neurophysiol, 2020, 131(1):285-307.
doi: S1388-2457(19)31164-2 pmid: 31501011 |
[6] |
Klimesch W, Sauseng P, Hanslmayr S. EEG alpha oscillations: The inhibition-timing hypothesis[J]. Brain Res Rev, 2007, 53(1):63-88.
doi: 10.1016/j.brainresrev.2006.06.003 pmid: 16887192 |
[7] |
Horvath A, Szucs A, Csukly G, et al. EEG and ERP biomarkers of Alzheimer's disease: A critical review[J]. Front Biosci (Landmark Ed), 2018, 23:183-220.
pmid: 28930543 |
[8] |
Babiloni C, Cassetta E, Binetti G, et al. Resting EEG sources correlate with attentional span in mild cognitive impairment and Alzheimer's disease[J]. Eur J Neurosci, 2007, 25(12):3742-3757.
pmid: 17610594 |
[9] |
Bucht G, Adolfsson R, Winblad B. Dementia of the Alzheimer type and multi-infarct dementia: A clinical description and diagnostic problems[J]. J Am Geriatr Soc, 1984, 32(7):491-498.
pmid: 6203954 |
[10] |
Gawel M, Zalewska E, Szmidt-Sałkowska E, et al. The value of quantitative EEG in differential diagnosis of Alzheimer's disease and subcortical vascular dementia[J]. J Neurol Sci, 2009, 283(1-2):127-133.
doi: 10.1016/j.jns.2009.02.332 pmid: 19268969 |
[11] | 莫延红, 孔朝红, 张兆辉. 定量脑电图在大面积脑梗死中的临床应用进展[J]. 中国医药, 2020, 15(7):1140-1143. |
[12] |
Koberda JL. QEEG as a useful tool for the evaluation of early cognitive changes in dementia and traumatic brain injury[J]. Clin EEG Neurosci, 2021, 52(2):119-125.
doi: 10.1177/1550059420914816 URL |
[13] |
Al-Qazzaz NK, Ali S, Ahmad SA, et al. Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis[J]. Med Biol Eng Comput, 2018, 56(1):137-157.
doi: 10.1007/s11517-017-1734-7 pmid: 29119540 |
[14] |
Neto E, Allen EA, Aurlien H, et al. EEG spectral features discriminate between Alzheimer's and vascular dementia[J]. Front Neurol, 2015, 6:25.
doi: 10.3389/fneur.2015.00025 pmid: 25762978 |
[15] | Lv Y, Chen H, Sui Z, et al. Spectrum-specific encephalography standardized low-resolution brain electromagnetic tomography network and gray matter correlations in vascular dementia patients[J]. I J D S N, 2020, 16(1): 1-7. |
[16] |
Rogala J, Kublik E, Krauz R, et al. Resting-state EEG activity predicts frontoparietal network reconfiguration and improved attentional performance[J]. Sci Rep, 2020, 10(1):5064.
doi: 10.1038/s41598-020-61866-7 pmid: 32193502 |
[17] |
Gazibera B, Suljic-Mehmedika E, Serdarevic N, et al. Predictive role of electroencephalography in regard to neurological and cognitive sequelae after acute central nervous system infection[J]. Acta Inform Med, 2019, 27(4): 234-239.
doi: 10.5455/aim.2019.27.234-239 pmid: 32055089 |
[18] |
Choi J, Ku B, You YG, et al. Resting-state prefrontal EEG biomarkers in correlation with MMSE scores in elderly individuals[J]. Sci Rep, 2019, 9(1):10468.
doi: 10.1038/s41598-019-46789-2 pmid: 31320666 |
[19] |
Hünerli D, Emek-Savaş DD, Çavuşoˇglu B, et al. Mild cognitive impairment in Parkinson's disease is associated with decreased P300 amplitude and reduced putamen volume[J]. Clin Neurophysiol, 2019, 130(8):1208-1217.
doi: S1388-2457(19)30450-X pmid: 31163365 |
[20] |
Crunelli V, David F, Lörincz ML, et al. The thalamocortical network as a single slow wave-generating unit[J]. Curr Opin Neurobiol, 2015, 31:72-80.
doi: 10.1016/j.conb.2014.09.001 pmid: 25233254 |
[21] |
Morrison C, Rabipour S, Knoefel F, et al. Auditory event-related potentials in mild cognitive impairment and Alzheimer's disease[J]. Curr Alzheimer Res, 2018, 15(8):702-715.
doi: 10.2174/1567205015666180123123209 pmid: 29359668 |
[22] |
Tarawneh HY, Mulders W, Sohrabi HR, et al. Investigating auditory electrophysiological measures of participants with mild cognitive impairment and Alzheimer's disease: A systematic review and meta-analysis of event-related potential studies[J]. J Alzheimers Dis, 2021, 84(1): 419-448.
doi: 10.3233/JAD-210556 pmid: 34569950 |
[23] |
Cintra M, Ávila RT, Soares TO, et al. Increased N200 and P300 latencies in cognitively impaired elderly carrying ApoE ε-4 allele[J]. Int J Geriatr Psychiatry, 2018, 33(2):e221-e227.
doi: 10.1002/gps.4773 URL |
[24] |
Irimajiri R, Golob EJ, Starr A. ApoE genotype and abnormal auditory cortical potentials in healthy older females[J]. Neurobiol Aging, 2010, 31(10):1799-1804.
doi: 10.1016/j.neurobiolaging.2008.09.005 pmid: 18976833 |
[25] |
Zhang C, Kong M, Wei H, et al. The effect of ApoE ε 4 on clinical and structural MRI markers in prodromal Alzheimer's disease[J]. Quant Imaging Med Surg, 2020, 10(2):464-474.
doi: 10.21037/qims.2020.01.14 URL |
[26] |
Missonnier P, Deiber MP, Gold G, et al. Working memory load-related electroencephalographic parameters can differentiate progressive from stable mild cognitive impairment[J]. Neuroscience, 2007, 150(2):346-356.
pmid: 17996378 |
[27] |
Fernandez R, Monacelli A, Duffy CJ. Visual motion event related potentials distinguish aging and Alzheimer's disease[J]. J Alzheimers Dis, 2013, 36(1):177-183.
doi: 10.3233/JAD-122053 pmid: 23594601 |
[28] |
Kubová Z, Kremlácek J, Valis M, et al. Visual evoked potentials to pattern, motion and cognitive stimuli in Alzheimer's disease[J]. Doc Ophthalmol, 2010, 121(1):37-49.
doi: 10.1007/s10633-010-9230-5 pmid: 20524039 |
[29] |
Bagattini C, Mazza V, Panizza L, et al. Neural dynamics of multiple object processing in mild cognitive impairment and Alzheimer's disease: Future early diagnostic biomarkers?[J]. J Alzheimers Dis, 2017, 59(2):643-654.
doi: 10.3233/JAD-161274 pmid: 28671112 |
[30] |
Bassett DS, Sporns O. Network neuroscience[J]. Nat Neurosci, 2017, 20(3):353-364.
doi: 10.1038/nn.4502 pmid: 28230844 |
[31] |
Edison P. Brain connectivity: Disrupted structural and functional connectivity-cause or effect?[J]. Brain Connect, 2020, 10(5):200-201.
doi: 10.1089/brain.2020.29011.ped pmid: 32573281 |
[32] |
Youssef N, Xiao S, Liu M, et al. Functional brain networks in mild cognitive impairment based on resting electroencephalography signals[J]. Front Comput Neurosci, 2021, 15:698386.
doi: 10.3389/fncom.2021.698386 URL |
[33] |
Tijms BM, Wink AM, de Haan W, et al. Alzheimer's disease: Connecting findings from graph theoretical studies of brain networks[J]. Neurobiol Aging, 2013, 34(8):2023-2036.
doi: 10.1016/j.neurobiolaging.2013.02.020 pmid: 23541878 |
[34] | Gurja JP, Muthukrishnan SP, Tripathi M, et al. Reduced resting-state cortical alpha connectivity reflects distinct functional brain dysconnectivity in Alzheimer's disease and mild cognitive impairment[J]. Brain Connect, 2022, 12(2): 134-145. |
[35] |
Koch G, Bonnì S, Pellicciari MC, et al. Transcranial magnetic stimulation of the precuneus enhances memory and neural activity in prodromal Alzheimer's disease[J]. Neuroimage, 2018, 169:302-311.
doi: S1053-8119(17)31072-8 pmid: 29277405 |
[36] |
Ferreri F, Guerra A, Vollero L, et al. TMS-EEG biomarkers of amnestic mild cognitive impairment due to Alzheimer's disease: A proof-of-concept six years prospective study[J]. Front Aging Neurosci, 2021, 13:737281.
doi: 10.3389/fnagi.2021.737281 URL |
[37] |
Lehmann D, Wackermann J, Michel CM, et al. Space-oriented EEG segmentation reveals changes in brain electric field maps under the influence of a nootropic drug[J]. Psychiatry Res, 1993, 50(4):275-282.
doi: 10.1016/0925-4927(93)90005-3 URL |
[38] | Michel CM, Koenig T. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review[J]. Neuroimage, 2018, 180(Pt B):577-593. |
[39] |
Seitzman BA, Abell M, Bartley SC, et al. Cognitive manipulation of brain electric microstates[J]. Neuroimage, 2017, 146:533-543.
doi: S1053-8119(16)30549-3 pmid: 27742598 |
[40] |
Britz J, Van De Ville D, Michel CM. BOLD correlates of EEG topography reveal rapid resting-state network dynamics[J]. Neuroimage, 2010, 52(4):1162-1170.
doi: 10.1016/j.neuroimage.2010.02.052 pmid: 20188188 |
[41] |
Smailovic U, Koenig T, Laukka EJ, et al. EEG time signature in Alzheimer's disease: Functional brain networks falling apart[J]. Neuroimage Clin, 2019, 24:102046.
doi: 10.1016/j.nicl.2019.102046 URL |
[42] |
Palmqvist S, Schöll M, Strandberg O, et al. Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity[J]. Nat Commun, 2017, 8(1):1214.
doi: 10.1038/s41467-017-01150-x pmid: 29089479 |
[43] |
Tait L, Tamagnini F, Stothart G, et al. EEG microstate complexity for aiding early diagnosis of Alzheimer's disease[J]. Sci Rep, 2020, 10(1):17627.
doi: 10.1038/s41598-020-74790-7 pmid: 33077823 |
[44] |
Musaeus CS, Nielsen MS, Høgh P. Microstates as disease and progression markers in patients with mild cognitive impairment[J]. Front Neurosci, 2019, 13:563.
doi: 10.3389/fnins.2019.00563 URL |
[45] | Musaeus CS, Engedal K, Høgh P, et al. Changes in the left temporal microstate are a sign of cognitive decline in patients with Alzheimer's disease[J]. Brain Behav, 2020, 10(6):e01630. |
[1] | 张佳楠, 孙琳琳, 詹潇燕, 李冰. 血清维生素B12与老年2型糖尿病轻度认知功能障碍的关系[J]. 临床荟萃, 2024, 39(1): 34-37. |
[2] | 马秀云, 朱菊红, 杨斌. 首诊于精神科的脑淀粉样血管病1例[J]. 临床荟萃, 2024, 39(1): 61-64. |
[3] | 贾丽娜, 吴美妮, 尹昌浩. 2型糖尿病认知功能障碍发病机制的研究进展[J]. 临床荟萃, 2023, 38(6): 554-558. |
[4] | 王璐璐, 董露露, 江超, 王九雪, 常雅君, 王天俊. 弥散张量成像评估帕金森病合并非运动症状患者脑微结构的研究进展[J]. 临床荟萃, 2023, 38(2): 189-192. |
[5] | 徐阳, 薛凌. H型高血压合并2型糖尿病患者轻度认知功能障碍的影响因素[J]. 临床荟萃, 2023, 38(10): 887-892. |
[6] | 李瑞珍, 李星辉, 曾璟, 姚晓涛, 杨珂欣, 张展. 高血压认知功能障碍机制的研究进展[J]. 临床荟萃, 2023, 38(1): 88-92. |
[7] | 姚瑶, 褚敏. 糖尿病肾病患者认知功能障碍与血清β淀粉样蛋白的关系[J]. 临床荟萃, 2022, 37(9): 813-816. |
[8] | 李平, 徐曼华, 何兰英, 王立峰, 张培, 宋丽华. 阿立哌唑和奥氮平治疗老年痴呆叠加不同亚型谵妄的对照研究[J]. 临床荟萃, 2022, 37(8): 699-703. |
[9] | 周彦伶, 朱斌斌, 曹盎洋, 罗文君. 虚拟现实技术改善神经认知障碍的研究进展[J]. 临床荟萃, 2022, 37(8): 743-747. |
[10] | 李钟梅, 冉丽, 蒋依, 郭志伟, 母其文. 老年轻度认知障碍患者睡眠障碍与认知功能的关系[J]. 临床荟萃, 2022, 37(7): 607-611. |
[11] | 吴天娇, 常璐, 李梓浩, 徐丹, 尹昌浩, 赵维纳. 微RNA在血管性认知障碍发病机制中的研究进展[J]. 临床荟萃, 2022, 37(4): 364-368. |
[12] | 曲伊平, 梁慧杰, 陈允恩. 重复经颅磁刺激治疗青少年抑郁症认知功能损害的研究进展[J]. 临床荟萃, 2022, 37(10): 957-960. |
[13] | 王延延, 郝又国, 潘广艳. 脑性瘫痪患儿认知功能障碍的研究进展[J]. 临床荟萃, 2021, 36(11): 1046-1051. |
[14] | 原倩1,刘福珍2,朱西琪2. 脑小血管病的研究进展[J]. 临床荟萃, 2020, 35(5): 462-465. |
[15] | 李贵友,刘伦志. 蛋白质结合的尿毒症毒素与血液透析患者认知功能的研究进展[J]. 临床荟萃, 2020, 35(4): 380-384. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||