Name: microstate and omega complexity analyses of the resting-state electroencephalography
Uploaded: 2018-06-15T12:30:00
Description: Watch this Scientific Journal Video about Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography at JoVE.com

这篇文章得到了中国国家自然科学基金 (31671141) 的支持。

本议定书经当地道德委员会批准。所有参与者和他们的父母都签署了这个实验的知情同意表格。
1. 主题
<ol><li>仅包括15个健康的男性青春期的科目, 年龄从14年到22岁 (意味着标准偏差: 18.3 @ 2.8 年)。 注意: 目前的协议, 分析微状态和欧米茄的复杂性已经发展为健康的对象, 但不限于这个组只。</li>
</ol>2. 脑电图数据记录
<ol><li>让受试者坐在一个安静的, 温?...

<ol>
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H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+changes+of+ICA-derived+EEG+functional+connectivity+in+the+resting+state.">Dynamic changes of ICA-derived EEG functional connectivity in the resting state.</a> Hum Brain Mapp. 34 (4), 852-868 (2013).</li><li>Imperatori, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Aberrant+EEG+functional+connectivity+and+EEG+power+spectra+in+resting+state+post-traumatic+stress+disorder:+a+sLORETA+study.">Aberrant EEG functional connectivity and EEG power spectra in resting state post-traumatic stress disorder: a sLORETA study.</a> Biol Psychol. 102, 10-17 (2014).</li><li>Gao, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Altered+Resting-State+EEG+Microstate+Parameters+and+Enhanced+Spatial+Complexity+in+Male+Adolescent+Patients+with+Mild+Spastic+Diplegia.">Altered Resting-State EEG Microstate Parameters and Enhanced Spatial Complexity in Male Adolescent Patients with Mild Spastic Diplegia.</a> Brain Topogr. 30 (2), 233-244 (2017).</li><li>Seitzman, B. 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M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BOLD+correlates+of+EEG+topography+reveal+rapid+resting-state+network+dynamics.">BOLD correlates of EEG topography reveal rapid resting-state network dynamics.</a> Neuroimage. 52 (4), 1162-1170 (2010).</li><li>Musso, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spontaneous+brain+activity+and+EEG+microstates.+A+novel+EEG/fMRI+analysis+approach+to+explore+resting-state+networks.">Spontaneous brain activity and EEG microstates. A novel EEG/fMRI analysis approach to explore resting-state networks.</a> Neuroimage. 52 (4), 1149-1161 (2010).</li><li>Strelets, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chronic+schizophrenics+with+positive+symptomatology+have+shortened+EEG+microstate+durations.">Chronic schizophrenics with positive symptomatology have shortened EEG microstate durations.</a> Clin Neurophysiol. 114 (11), 2043-2051 (2003).</li><li>Kikuchi, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG+microstate+analysis+in+drug-naive+patients+with+panic+disorder.">EEG microstate analysis in drug-naive patients with panic disorder.</a> PLoS One. 6 (7), e22912 (2011).</li><li>Santarnecchi, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG+Microstate+Correlates+of+Fluid+Intelligence+and+Response+to+Cognitive+Training.">EEG Microstate Correlates of Fluid Intelligence and Response to Cognitive Training.</a> Brain Topogr. , (2017).</li><li>Schlegel, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG+microstates+during+resting+represent+personality+differences.">EEG microstates during resting represent personality differences.</a> Brain Topogr. 25 (1), 20-26 (2012).</li><li>Wackermann, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Beyond+mapping:+estimating+complexity+of+multichannel+EEG+recordings.">Beyond mapping: estimating complexity of multichannel EEG recordings.</a> Acta Neurobiol Exp (Wars). 56 (1), 197-208 (1996).</li><li>Jung, T. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Removing+electroencephalographic+artifacts+by+blind+source+separation.">Removing electroencephalographic artifacts by blind source separation.</a> Psychophysiology. 37 (2), 163-178 (2000).</li><li>Pascual-Marqui, R. D., Michel, C. M., Lehmann, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Segmentation+of+brain+electrical+activity+into+microstates:+model+estimation+and+validation.">Segmentation of brain electrical activity into microstates: model estimation and validation.</a> IEEE Trans Biomed Eng. 42 (7), 658-665 (1995).</li><li>Pascual-Marqui, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Standardized+low-resolution+brain+electromagnetic+tomography+(sLORETA):+technical+details.">Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details.</a> Methods Find Exp Clin Pharmacol. 24, 5-12 (2002).</li><li>Hu, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+primary+somatosensory+cortex+contributes+to+the+latest+part+of+the+cortical+response+elicited+by+nociceptive+somatosensory+stimuli+in+humans.">The primary somatosensory cortex contributes to the latest part of the cortical response elicited by nociceptive somatosensory stimuli in humans.</a> Neuroimage. 84, 383-393 (2014).</li><li>Murray, M. M., Brunet, D., Michel, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Topographic+ERP+analyses:+a+step-by-step+tutorial+review.">Topographic ERP analyses: a step-by-step tutorial review.</a> Brain Topogr. 20 (4), 249-264 (2008).</li><li>Van de Ville, D., Britz, J., Michel, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG+microstate+sequences+in+healthy+humans+at+rest+reveal+scale-free+dynamics.">EEG microstate sequences in healthy humans at rest reveal scale-free dynamics.</a> Proc Natl Acad Sci U S A. 107 (42), 18179-18184 (2010).</li><li>Gonuguntla, V., Mallipeddi, R., Veluvolu, K. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+emotion+associated+brain+functional+network+with+phase+locking+value.">Identification of emotion associated brain functional network with phase locking value.</a> Conf Proc IEEE Eng Med Biol Soc. 2016, 4515-4518 (2016).</li><li>Wen, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Resting-state+EEG+coupling+analysis+of+amnestic+mild+cognitive+impairment+with+type+2+diabetes+mellitus+by+using+permutation+conditional+mutual+information.">Resting-state EEG coupling analysis of amnestic mild cognitive impairment with type 2 diabetes mellitus by using permutation conditional mutual information.</a> Clin Neurophysiol. 127 (1), 335-348 (2016).</li><li>Rosales, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+efficient+implementation+of+the+synchronization+likelihood+algorithm+for+functional+connectivity.">An efficient implementation of the synchronization likelihood algorithm for functional connectivity.</a> Neuroinformatics. 13 (2), 245-258 (2015).</li><li>Jia, H., Peng, W., Hu, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+approach+to+identify+time-frequency+oscillatory+features+in+electrocortical+signals.">A novel approach to identify time-frequency oscillatory features in electrocortical signals.</a> J Neurosci Methods. 253, 18-27 (2015).</li><li>Wackermann, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+characterization+of+brain+electrical+activity+by+means+of+the+&#937;+complexity+production+rate.">Global characterization of brain electrical activity by means of the &#937; complexity production rate.</a> Brain Topogr. 18, 135 (2005).</li><li>Wackermann, J., Putz, P., Ga&#223;ler, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unfolding+EEG+spatial+complexity+as+a+function+of+frequency.">Unfolding EEG spatial complexity as a function of frequency.</a> Brain Topogr. 16 (2), 124 (2003).</li></ol>

本文详细介绍了两种脑电图分析方法 (即微状态分析法和欧米茄复杂度法), 分别测量人脑的时间复杂度和空间复杂性。在议定书内有几个关键步骤应予提及。首先, 在计算微状态和欧米茄复杂度之前, 必须对 EEG 数据进行清理。其次, 在计算微状态和欧米茄复杂度之前, 应 remontaged 对脑电图数据进行平均参考。第三, 在微状态和欧米茄复杂度计算之前, 必须将连续脑电图数据分割成时代。每个纪元的长...

脑电图 (eeg) 已被广泛用于记录人脑的电活动在临床诊断和科学研究, 因为它是无创的, 低成本的, 有非常高的时间分辨率1。为了研究静止状态下的脑电图信号, 研究人员开发了许多脑电图技术 (如功率谱分析、功能连接分析)2、3。其中, 微状态分析和欧米茄复杂性分析可以很好地利用脑电信号4中固有的时空信息。
以往的研究表明, 尽管脑电信号的地形分布随着时间的推移在闭眼或睁开的静止状态下变化, 但序列的瞬时图显示了景观的不连续变化, 即稳定交替期在某些准稳定脑电图之间有短的过渡期地形5。微状态被定义作为这些情节与准稳定 EEG 地形, 为时在80和 120 ms1之间。由于不同的电势景观必须由不同的神经源产生, 这些微状态可能成为心理状态的基本块, 可以被认为是 "思想和情感的原子"6。采用现代模式分类算法, 连续观察到四个静止脑电图微状态类, 被标记为 a 类、B 类、C 类和 D 类7。此外, 研究人员发现, 这四微状态类静止脑电图数据与已知的功能系统密切联系在许多静止状态 fMRI (功能磁共振成像) 研究8,9.因此, 微状态分析为研究人脑的静止状态网络 (RSNs) 提供了一种新的方法。此外, 每微状态类的平均持续时间和发生频率, 四微状态地图的地形形状都受到一些脑部疾病4,10,11的影响,并且与流体智力12和个性13相关。
在另一方面, 多通道脑电图的传统功能连接只能描述两个头皮电极之间的功能连接, 从而无法评估整个头皮或某一脑区内的全球功能连通性。Wackermann (1996)14提出的欧米茄复杂性, 并通过结合主成分分析 (PCA) 和香农熵的方法计算, 已被用来量化宽带全球同步的空间分布的脑区。为了评估每个频带的欧米茄复杂度, 傅立叶变换通常是作为初始步骤25进行的。
微状态和欧米茄复杂性可以用来反映两个紧密相连的概念,即时态复杂性和空间复杂性4。由于微状态类代表人脑中的某些心理操作, 它们可以反映神经元振荡的时间结构。更低的持续时间和更高的发生率每秒必须表明更高的时态复杂性。欧米茄复杂性与大脑中独立神经源的数量呈正相关, 因此通常被认为是空间复杂度4的指标。
本文详细介绍了脑电图微状态分析和欧米茄复杂性分析的协议。脑电图微状态和欧米茄复杂性分析为测量脑活动的时间和空间复杂性提供了机会。

<table>
	<tbody>
		<tr>
			<td>ANT 20 channels EEG/ERP system</td>
			<td>ASA-Lab, ANT B.V., Netherlands</td>
			<td></td>
			<td>company web address: 
			http://www.ant-neuro.com/</td>
		</tr>
		<tr>
			<td>EEGLAB plugin for Microstates</td>
			<td>Thomas Koenig</td>
			<td></td>
			<td>https://sccn.ucsd.edu/wiki/EEGLAB_Extensions_and_plug-ins</td>
		</tr>
		<tr>
			<td>sLORETA</td>
			<td>Roberto D. Pascual-Marqui</td>
			<td></td>
			<td>http://www.uzh.ch/keyinst/loreta.htm</td>
		</tr>
		<tr>
			<td>MATLAB 2010a</td>
			<td>The MathWorks Inc.</td>
			<td></td>
			<td>company web address: 
			http://www.mathworks.com/</td>
		</tr>
		<tr>
			<td>eeglab</td>
			<td>Swartz Center for Computational Neuroscience, University of California, San Diego</td>
			<td></td>
			<td>https://sccn.ucsd.edu/eeglab/index.php</td>
		</tr>
	</tbody>
</table>

microstate and omega complexity analyses of the resting-state electroencephalography

微状态和欧米茄的复杂性是两个无参考脑电图 (eeg) 措施, 可以代表的时间和空间复杂的脑电图数据, 并已广泛用于调查神经机制的一些脑部疾病。本文的目的是描述脑电图微状态和欧米茄复杂性分析的基本协议。这两种方法的主要优点是可以消除传统频谱分析固有的参考依赖问题。此外, 微状态分析充分利用了静止状态脑电图的高时间分辨力, 得到的四微状态类可以分别匹配相应的休眠状态网络。欧米茄复杂性的特点是整个大脑或特定大脑区域的空间复杂度, 这与传统的复杂度测量方法相比, 在单个信道中的信号复杂度具有明显的优越性。这两种脑电图测量方法可以相互补充, 分别从时间和空间领域对大脑的复杂性进行研究。

脑电图微状态
图 1显示了大平均规范化的微状态映射。这里所确定的四微状态类的电势景观与以前的研究4中所发现的类似。
表 1显示了健康受试者微状态参数的平均值和标准差 (SD)。对于微状态 a 类, 其发生率为 3.44, 1.29 次/秒, 持?...

Watch this Scientific Journal Video about Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography at JoVE.com

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

本文描述了基于脑电图 (eeg) 微状态分析和欧米茄复杂性分析的协议, 这是两种无参考脑电图的方法, 对探讨脑功能紊乱的神经机制具有十分重要的价值。

静止状态脑电图的微状态和欧米茄复杂性分析

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of ...

This semester have answered 10 questions in the field of brain disorder, such as how to identify an efficient pair of markers to a code of bios in today's diagnosis. The main advantage of this technique is that it could emanate as a reference of the pending problem when you're adhering to traditional EEG analysis. The applications of this technique extend toward our understanding of resting-states networks of human brain.To begin this procedure, import the raw EEG data to the EEG lab software. Next, load the channel location file into the EEG lab software to obtain the spatial locations of these electrodes. To remove the reference electrodes, an option of Select data in channel range of the popup dialog box, select only the recording electrodes, and do not select the reference electrodes so that the reference electrodes can be removed.To band pass filter the EEG data between 0.5 and 80 hertz, in the popup dialog box, choose 0.5 for the lower edge of the frequency pass band hertz and choose 80 for the higher edge of the frequency pass band hertz. Then click Ok.To remove the powerline noise with a notch filter between 49 and 51 hertz, in the popup dialog box, choose 49 for the lower edge of the frequency pass band hertz and choose 51 for the higher edge of the frequency pass band hertz. Then select the option of Notch filter the data instead of pass band, and click Ok.To remove eye movements, click on Tools, then click Artifact removal using AAR 1.3, and EOG removal using BSS.To remove EMG, click Tools, then click Artifact removal using AAR 1.3 and EMG removal using BSS. Next, segment the preprocessed continuous EEG data into epochs with an epoch length of two seconds. A window will pop up that allows the saving of the segmented EEG data.Then import the segmented EEG data to the EEG lab software and reject EEG epochs with amplitude values exceeding plus or minus 80 microvolts at any electrode. Next, save the preprocessed EEG data. In this procedure for each subject, load the preprocessed EEG data, convert reference channels to common average reference, and band pass filter the EEG data between two and 20 hertz.Next, identify the four microstate maps in each subject. In the popup dialog box, choose three for the min number of classes, choose six for the max number of classes, choose 50 for the number of restarts, choose max number of maps to use, and select the options of GFP peak only, and No polarity. Then, click the Ok button.Subsequently, save the EEG data of each subject after identifying its own microstate maps. Import the EEG data sets of all subjects saved in the last step at once. Then identify the group level microstate maps.In the popup dialog box, select the data sets of all subjects in the option, Choose sets for averaging. In the option, Name of mean, give a name for the group level microstate maps, then click the button Ok.This will create a new data set named as GrandMean which stores the group level microstate maps. Manually sort the order of four group level microstate maps according to their classic order.In the popup, select More, and then the number of maps shown becomes four. After that, select Man sort. In the popup dialog box, enter the new order of four group level microstate maps and click Close.Following that, sort the order of the four microstate maps of each subject, save the microstate parameters for each subject which will invoke two popup dialog boxes sequentially. In the first dialog box, select the data sets of all subjects. In the second dialog box, select 4-Classes for option, Number of classes.Select the options of Fitting only on GFP peaks and Remove potentially truncated microstates. Next, choose 30 for the label smoothing window, ms, and choose One for the the Non-Smoothness penalty, then click Ok.A CSV file which stores submicrostate parameters will be saved on the computer. These images show that microstate Class A and B have a right frontal to left occipital orientation, and a left frontal to right occipital orientation respectively.Microstate Class C and D have symmetric topographies but prefrontal to occipital orientation, and front to central to occipital orientation were observed respectively. This table shows the mean and standard deviation of microstate parameters of the healthy subjects. Once mastered, this technique can be done in one hour if it is performed properly.While attempting this procedure, it's important to remember that the EEG data should be preprocessed carefully. Following this procedure, other methods like source localization can be performed in order to answer additional questions such as where these microstate maps come from. This technique paved a way for researchers in the field of brain science to expose disease as in human brain.

Research

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Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG

调查婴儿和成人使用密集阵脑电图的社会认知（ D脑电图）

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Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex

调查深皮层的功​​能和皮层下结构采用立体定向脑电图：由前扣带皮层的经验教训

Stereotactic Electroencephalography (SEEG) is a technique used to localize seizure foci in patients with medically intractable epilepsy. This procedure ...

Assessment of Hippocampal Dendritic Complexity in Aged Mice Using the Golgi-Cox Method

使用高尔基Cox方法评估老年小鼠海马树突状复杂性

Dendritic spines are the protuberances from the neuronal dendritic shafts that contain&#160; excitatory synapses. The morphological and branching ...

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

啮齿动物联合局部脑电图和局部电位的同步记录

Although electroencephalography (EEG) is widely used as a non-invasive technique for recording neural activities of the brain, our understanding of the ...

Quantitative Analysis of Neuronal Dendritic Arborization Complexity in Drosophila

Quantitative Analysis of Neuronal Dendritic Arborization Complexity in Drosophila

嗜德维人神经元树突状树突状复杂度的定量分析

Dendrites are the branched projections of a neuron, and dendritic morphology reflects synaptic organization during the development of the nervous system. ...

Combined Transcranial Magnetic Stimulation and Electroencephalography of the Dorsolateral Prefrontal Cortex

侧前额叶皮质联合经颅磁刺激与脑电图的研究

Transcranial magnetic stimulation (TMS) is a non-invasive method that produces neural excitation in the cortex by means of brief, time-varying magnetic ...

Collection of Frozen Rodent Brain Regions for Downstream Analyses

用于下游分析的冷冻啮齿动物大脑区域集合

As our understanding of neurobiology has progressed, molecular analyses are often performed on small brain areas such as the medial prefrontal cortex ...