Name: microstate and omega complexity analyses of the resting-state electroencephalography
Uploaded: 2018-06-15T12:30:00.000+00:00
Duration: 6 min 40 s
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, 温控室的舒适的椅子上, 在那里记录脑电图数据。利用蚂蚁脑电图系统在本协议中采集20通道 eeg 数据。</li>
	<li>把帽子放在主题头上。<ol>
<li>在这项研究中, 使用中等大小的帽子, 因为它适合大多数青少年或男性科目。对于儿童, 请测量每个科目的头部周长, 并确定适当大小的帽子。</li>
		<li>将电极直拉在大约50% 的距离 inion 和 nasion 之间, 50% 的距离之间的左和右之间的听觉凹痕。分别将参考电极放在左、右乳突骨上。根据国际10-20 系统, 将其他头皮电极置于标准位置。 注:20 通道的电极系统和 eeg 放大器足以进行脑电图微状态分析和欧米茄复杂度分析。</li>
	</ol>
</li>
	<li>通过电极插入钝针, 填充所有电极与导电凝胶。使用导电凝胶降低阻抗。保持所有电极阻抗低于 10 kiloohms (kΩ)。<ol>
<li>在此期间, 为主题提供一些娱乐 (例如, 放映短片)。如果使用干电极或盐水电极, 则跳过注射导电凝胶的步骤。</li>
	</ol>
</li>
	<li>在录音过程中, 指导受试者闭上眼睛放松, 持续5分钟. 使用数字化软件对脑电图信号进行数字化和记录。使用至少250个样品的取样率. 使用比0.1 和 80 Hz 宽的在线过滤器。 注意: 虽然大多数商用电极放大器系统使用的是有源电极系统, 它可以提高 eeg 信号的信噪比, 但在脑电图记录过程中, 不要将此系统靠近任何电子设备。</li>
</ol>3. 脑电图数据预处理
注: 脑电图数据可以使用各种开源软件或商用软体进行预处理。下面提供的说明是特定于 EEGLAB 的。这只是许多可用选项中的一个来预处理脑电图数据。
<ol><li>将原始脑电图数据导入 eeg 软件 (如 EEGLAB) (文件 |导入数据 |使用 EEGLAB 函数和插件)。 注: 从各种电极放大器系统记录的原始脑电图数据可由 EEGLAB 识别, 如艾吉、蚂蚁、脑视记录仪和 Neuroscan。</li>
	<li>将通道位置文件加载到 EEG 软件中 (编辑 |通道位置)。尽管 eeg 数据和通道名称已导入 EEGLAB, 但为了获得这些电极的空间位置, 请将通道位置文件导入 eeg 软件。</li>
	<li>删除参考电极 (编辑 |选择数据 |在通道范围内选择数据)。在弹出对话框中的 "选择通道范围内的数据" 选项中, 只选择记录电极, 不要选择参考电极, 以便可以删除参考电极。参考电极记录的数据不是 "真实" 的大脑信号, 因为这两个电极分别放在左和右乳突骨上。</li>
	<li>带通过滤器脑电图数据在0.5 和 80 Hz 之间 (工具 |筛选数据 |基本 FIR 过滤器 [新建, 默认])。在弹出对话框中, 为 "频率传递带 (hz)" 的 "下边缘" 选择 5, 并为 "频率传递带 (hz)" 的 "较高边缘" 选择80。然后单击 "确定" 按钮。</li>
	<li>在49和 51 Hz 之间删除带有凹槽过滤器的电源线噪音 (工具 |筛选数据 |基本 FIR 过滤器 [新建, 默认])。在弹出对话框中, 为 "频率传递带 (hz)" 的下边缘选择 49, 然后选择 "频率传递带 (hz) 的更高边缘" 的 51, 然后选择 "凹槽过滤数据而不是传递带" 选项。然后单击 "确定" 按钮。</li>
	<li>使用盲源分离 (BSS) 算法15, 纠正由眼球运动、肌电信号 (肌电图) 或任何其他非生理工件污染的数据部分。对于眼球运动, 点击工具 |使用 AAR 1.3 删除工件 |EOG 去除 |使用 BSS;对于肌电信号, 工具 |使用 AAR 1.3 删除工件 |使用 BSS 去除肌电信号。</li>
	<li>将预处理后的连续脑电图数据分割成世纪, 以2秒为纪元长度。为此, 写 '脑电图 = eeg_regepochs (eeg, ' 复发 ', 2, ' 极限 ', [0 2], ' rmbase ', 南); pop_saveset (EEG)', 然后击中键盘的输入键。一个窗口将弹出, 允许保存分段脑电图数据。</li>
	<li>将分段脑电图数据导入脑电图软件 (文件 |加载现有数据集)。</li>
	<li>拒绝脑电图时代与振幅值超过80µV 在任何电极 (工具 |拒绝数据世纪 |拒绝数据 [所有方法])。</li>
	<li>保存预处理的脑电图数据 (文件 |将当前数据集另存为)。</li>
</ol>4. 脑电图微状态分析
注: 经典 k-均值聚类算法的一个改进版本用于微状态类分析16, 它包含一个自下而上的过程和一个自下而上的过程。在自底向上的过程中, 用空间相关作为聚类准则来识别群级微状态类。然后在自下而上的过程中, 每组每个主题的每个地形图都被分配到具有最大空间相关性的 EEG 微状态类。对于静止状态脑电图微状态分析, 地形图的极性通常被忽略。脑电图微状态类分析可以使用各种开源软件, 如 CARTOOL, sLORETA, 艾玛和 MapWin。下面提供的说明是特定的 EEGLAB 插件微状态。这个 EEGLAB 插件可以从 https://sccn.ucsd.edu/wiki/EEGLAB_Extensions_and_plug-ins 下载。
<ol><li>对于每个主题, 加载预处理的脑电图数据 (文件 |加载现有数据集), 将参考通道转换为普通平均引用 (工具 |重新参考), 和带通滤波器的脑电图数据之间的2和 20 Hz (工具 |筛选数据 |基本 FIR 过滤器 [新建, 默认])。</li>
	<li>确定每个主题中的四微状态映射 (工具 |微状态 |识别微状态地图)。在弹出对话框中, 为 "最小类数" 选择 "3", 为 "最大类数" 选择 6, 选择 "重新启动次数" 50, 选择 "要使用的最大映射数", 然后选择 "GFP 峰值" 和 "无极性" 选项。然后单击 "确定" 按钮。</li>
	<li>保存每个主题的脑电图数据后, 确定自己的微状态地图 (文件 |将当前数据集另存为)。</li>
	<li>导入最后一步中保存的所有主题的 EEG 数据集 (文件 |加载现有数据集)。</li>
	<li>识别组级微状态映射 (工具 |微状态 |平均微状态映射跨数据集)。在弹出对话框中, 选择 "选择平均值集" 选项中所有主题的数据集。在 "平均名称" 选项中, 给出组级微状态映射的名称。默认名称为 "GrandMean"。然后单击 "确定" 按钮。这将创建一个名为 "GrandMean" 的新数据集, 它存储组级微状态映射。</li>
	<li>根据他们的经典顺序手动排序四组级微状态地图 (剧情 |编辑微状态地图)。在弹出式中, 选择 "更多", 然后显示的地图数变为四。选择 "人类排序"。在弹出对话框中, 输入四组级微状态映射的新顺序。然后单击 "关闭"。</li>
	<li>排序每个主题的四微状态地图的顺序 (工具 |微状态 |按平均值对单个微状态映射进行排序)。</li>
	<li>保存每个主题的微状态参数 (工具 |微状态 |在数据集 [平均模板映射] 中量化微状态, 它将依次调用两个弹出对话框。<ol>
<li>在第一个对话框中, 选择所有主题的数据集。在第二个对话框中, 为选项 "类数" 选择 "4 类", 选择 "仅适用于 GFP 峰值" 和 "删除可能截断的微状态" 选项, 选择 "30" 作为 "标签平滑窗口 (ms)", 然后为 "非平滑性" 选择1。惩罚 "。然后单击 "确定"。存储微状态参数的 csv 文件将保存在计算机上。</li>
	</ol>
</li>
</ol>5. 欧米茄复杂性分析
<ol><li>使用自定义脚本保存每个纪元的 EEG 数据和每个主题的 ASCII 或 txt 格式。在补充材料中可以找到步骤5.1 的自定义脚本示例。 注: 如果计算了全局欧米茄复杂性, 则需要所有头皮电极的 EEG 数据以 ASCII 或 txt 格式导出。如果区域欧米茄复杂性被计算, 仅出口电极的 EEG 数据在那头皮区域。例如, 为了计算前欧米茄复杂度, 只导出前区电极的脑电图数据 (即 Fp1、Fp2、F7、F3、熔、F4 和 F8);为了计算后欧米茄复杂度, 只导出后区电极的脑电图数据 (即 T5、T6、P3、P4、Pz、O1 和 O2)。</li>
	<li>使用 sLORETA 软件17计算所有离散频率的欧米茄复杂度 (实用程序 |全局连接性)。此软件可在 http://www.uzh.ch/keyinst/loreta.htm 中使用。</li>
	<li>使用自定义脚本26计算每个频带的欧米茄复杂性。在我们的例子中, 计算以下八频带的欧米茄复杂度作为每个频率限制中的平均值, 这是三角洲 (0.5-3.5 hz), θ (4-7, 5 hz), alpha1 (8-10 hz), alpha2 (10.5-13.5 hz), beta1 (14-18 hz), beta2 (18.5-30 hz), gamma1 (30.5-48 hz), 和 gamma2 (52-80 Hz)4。在补充材料中可以找到步骤5.3 的自定义脚本示例。</li>
</ol>

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作者没有什么可透露的。

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。每个纪元的长度应该是2秒. 最后, 可用于微状态分析的软件包括 Cartool (https://sites.google.com/site/cartoolcommunity/about)、sLORETA (http://www.uzh.ch/keyinst/loreta.htm) 和 MapWin (http://www.thomaskoenig.ch/index.php/software/mapwin)。本文以 EEGLAB 中的一个插件为例, 进行了微状态分析。
虽然这里进行的微状态分析应用于静止状态脑电图数据, 它可以很容易地应用于事件相关电位 (ERPs), 这将有助于我们发现更多的信息的时间课程的不同认知操作的认知实验, 并提供一个无参考的方法来执行 ERP 分析18,19。注意, 对于静止状态脑电图, 地形图的极性通常被忽略;然而, 对于 ERPs, 不应忽视地形图的极性。这个脑电图插件的一个小限制是, 它只能用于静止状态脑电图。对于 ERPs, 软件 Cartool 可能是最好的选择之一。欧米茄复杂度值从1到 N。如果欧米茄复杂度计算为 1, 则揭示了某一脑区内的最大全局功能连通性;而如果欧米茄复杂度等于 N, 则发现某一脑区内的最小全局功能连通性。因此, 如果我们想统计地测试不同大脑区域的欧米茄复杂性, 那么在这些区域中选择的电极数量必须相等, 因为电极的数量会显著影响欧米茄复杂性估计的价值。
为了研究静止的脑电图, 研究人员开发了许多脑电图技术 (例如, 功率谱分析, 功能连接分析)2,3。与这些传统技术相比, 微状态分析充分利用了 EEG 技术的最佳时间分辨率。四确定的微状态类被发现与在许多静止状态 fMRI 研究8,20: 听觉 (微状态), 视觉 (微状态 B), 部分认知的四个被研究的功能系统相关。控制和部分缺省模式 (微状态 C) 和背部注意 (微状态 D)。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。与传统的脑电图技术相比, 欧米茄复杂度可以描述在某一脑区4的全球功能连通性。传统的功能连接只能描述两个头皮电极之间的功能连通性。
然而, 两种脑电图技术也有几个局限性, 应该提到。首先, 现有的微状态分析通常在宽带 eeg 信号上进行, 因此不能充分利用 eeg 技术的丰富频率信息。此外, 这四微状态类及相关指标的功能意义至今尚不清楚。其次, 欧米茄复杂度只能检测线性依赖性。它不能检测头皮区域之间的非线性依赖性, 可以通过一些传统的功能连接度量 (如锁相值、互信息和同步可能性) 来量化21、22 ,23。
今后, 微状态分析应应用于源定位技术 (如 sLORETA、爱河、波束形成), 这将大大提高脑电信号的空间分辨率。虽然微状态分析在静止脑电图和 ERPs 中得到了广泛的应用, 但只有很少的研究将此技术应用于时频域。例如, 贾等。24提出了一种基于地形分割分析的方法, 以优化和自动识别详细的时频特征。这种方法可以有效地利用振荡活动的空间信息。然而, 这些应用还远未成熟。对于欧米茄复杂度, 规范化的欧米茄复杂性是非常需要的, 因为欧米茄复杂度估计值取决于选择的电极数量。今后, 应将其应用于时频域。

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:&lt;br/&gt; 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:&lt;br/&gt; 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, 持续时间为 72 13 毫秒. 对于微状态 B 类, 发生率为 3.54 @ 0.85 次/秒, 持续时间为 71 @ 18 毫秒. 微状态类 C 的发生率为3.85 秒/秒, 持续时间为 69 9 毫秒, 为微状态类 D, 发生率为 3.41 @ 0.78 次/秒, 持续时间为 66 @ 11 毫秒。
欧米茄复杂性
表 2给出了健康学科中每个频段的全球欧米茄复杂度的值 (平均 * SD)。对于三角洲波段, 全球欧米茄的复杂性是 6.39 1.34。对于θ波段, 全球欧米茄的复杂性是 5.46 0.85。对于 alpha-1 波段, 全球欧米茄的复杂性是 3.47 0.8。对于 alpha-2 波段, 全球欧米茄的复杂性是 3.87 0.70。对于 beta-1 波段, 全球欧米茄的复杂性是 5.36 0.84。对于 beta-2 波段, 全球欧米茄的复杂性是 6.16 0.83。对于 gamma-1 波段, 全球欧米茄的复杂性是 6.95 1.07。对于 gamma-2 波段, 全球欧米茄的复杂性是 6.88 1.39。
表 2显示了健康受试者各频带前欧米茄复杂度的值 (平均 * SD)。对于三角洲波段, 前欧米茄复杂度为 4.84, 1.7。对于θ波段, 前欧米茄复杂度为 4.23 1.48。对于 alpha-1 带, 前欧米茄复杂度为 3.44, 1.09。对于 alpha-2 带, 前欧米茄复杂度为 3.87, 0.97。对于 beta-1 带, 前欧米茄复杂度为 3.74, 0.81。对于 beta-2 带, 前欧米茄复杂度为 2.94, 0.59。对于 gamma-1 带, 前欧米茄复杂度为 1.98, 0.24。对于 gamma-2 带, 前欧米茄复杂度为 3.02, 0.59。
表 2显示了健康受试者各频段后欧米茄复杂度的值 (平均值)。对于三角洲波段, 后欧米茄复杂度为 3.71, 1.48。对于θ波段, 后欧米茄复杂度为 2.47 0.85。对于 alpha-1 波段, 后欧米茄复杂度为 2.11 0.9。对于 alpha-2 波段, 后欧米茄复杂度为 3.16 1.42。对于 beta-1 波段, 后欧米茄复杂度为 4.32 1.67。对于 beta-2 波段, 后欧米茄复杂度为 3.84 1.04。对于 gamma-1 波段, 后欧米茄复杂度为 2.17 0.37。对于 gamma-2 波段, 后欧米茄复杂度为 2.99 0.53。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56452/56452fig1.jpg"> 图1。健康学科中静止状态脑电图四微状态类 (A D) 的平均规范化地形图.微状态类 a 和 B 有一个右额向左枕方向和左额到右枕方向, 分别。微状态类 C 和 D 有对称地形, 但前额向枕方向和 frontocentral 枕方向分别观察。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56452/56452fig1large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody>
<tr>
<td></td>
			<td colspan="8">微状态类</td>
		</tr>
<tr>
<td></td>
			<td colspan="2">一个</td>
			<td colspan="2">B</td>
			<td colspan="2">C</td>
			<td colspan="2">D</td>
		</tr>
<tr>
<td></td>
			<td>意味 着</td>
			<td>Sd</td>
			<td>意味 着</td>
			<td>Sd</td>
			<td>意味 着</td>
			<td>Sd</td>
			<td>意味 着</td>
			<td>Sd</td>
		</tr>
<tr>
<td>发生/秒</td>
			<td>3.44</td>
			<td>1.29</td>
			<td>3.54</td>
			<td>0.85</td>
			<td>3.85</td>
			<td>0.63</td>
			<td>3.41</td>
			<td>0.78</td>
		</tr>
<tr>
<td>持续时间 (毫秒)</td>
			<td>72</td>
			<td>13</td>
			<td>71</td>
			<td>18</td>
			<td>69</td>
			<td>9</td>
			<td>66</td>
			<td>11</td>
		</tr>
</tbody></table>表1。健康受试者的微状态参数 (n=15)。本表显示了四微状态类的发生率和持续时间的平均值和标准偏差 (SD)。
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody>
<tr>
<td></td>
			<td colspan="2">全球欧米茄复杂性</td>
			<td colspan="2">前欧米茄复杂性</td>
			<td colspan="2">后欧米茄复杂性</td>
		</tr>
<tr>
<td>频带</td>
			<td>意味 着</td>
			<td>Sd</td>
			<td>意味 着</td>
			<td>Sd</td>
			<td>意味 着</td>
			<td>Sd</td>
		</tr>
<tr>
<td>三角洲</td>
			<td>6.39</td>
			<td>1.34</td>
			<td>4.84</td>
			<td>1。7</td>
			<td>3.71</td>
			<td>1.48</td>
		</tr>
<tr>
<td>Θ</td>
			<td>5.46</td>
			<td>0.85</td>
			<td>4.23</td>
			<td>1.48</td>
			<td>2.47</td>
			<td>0.85</td>
		</tr>
<tr>
<td>Alpha-1</td>
			<td>3.47</td>
			<td>0。8</td>
			<td>3.44</td>
			<td>1.09</td>
			<td>2.11</td>
			<td>0。9</td>
		</tr>
<tr>
<td>Alpha-2</td>
			<td>3.87</td>
			<td>0。7</td>
			<td>3.87</td>
			<td>0.97</td>
			<td>3.16</td>
			<td>1.42</td>
		</tr>
<tr>
<td>Beta-1</td>
			<td>5.36</td>
			<td>0.84</td>
			<td>3.74</td>
			<td>0.81</td>
			<td>4.32</td>
			<td>1.67</td>
		</tr>
<tr>
<td>Beta-2</td>
			<td>6.16</td>
			<td>0.83</td>
			<td>2.94</td>
			<td>0.59</td>
			<td>3.84</td>
			<td>1.04</td>
		</tr>
<tr>
<td>Gamma-1</td>
			<td>6.95</td>
			<td>1.07</td>
			<td>1.98</td>
			<td>0.24</td>
			<td>2.17</td>
			<td>0.37</td>
		</tr>
<tr>
<td>Gamma-2</td>
			<td>6.88</td>
			<td>1.39</td>
			<td>3.02</td>
			<td>0.59</td>
			<td>2.99</td>
			<td>0.53</td>
		</tr>
</tbody></table>表2。健康学科的全球、前后欧米茄复杂性 (n=15)。本表分别显示了八频段 (三角洲、θ、alpha-1、alpha-2、beta-1、beta-2、gamma-1、gamma-2) 的全球、前后欧米茄复杂度的平均值和标准差 (SD)。
补充文件.为了运行本手稿中使用的脚本, 请在 MATLAB 环境中打开脚本, 然后将所有内容复制到命令窗口中, 然后按 "Enter" 键。请注意, 这些脚本只适用于我们的数据集。在将脚本应用于其他数据集时, 需要进行某些修改。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56452/example_scripts.zip">请单击此处下载此文件.</a>

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 ...

microstate-omega-complexity-analyses-resting-state

Nanyang Technological University

Research

JoVE Journal

Neuroscience

10.1K 次观看.  Peking University People's Hospital. 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。

视频: Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Spatial intelligence is often linked to success in engineering education and engineering professions. The use of electroencephalography enables comparative calculation of individuals&#39; neural efficiency as they perform successive tasks requiring spatial ability to derive solutions. Neural efficiency here is defined as having less beta activation, and therefore expending fewer neural resources, to perform a task in comparison to other groups or other tasks. For inter-task comparisons of tasks with similar durations, these measurements may enable a comparison of task type difficulty. For intra-participant and inter-participant comparisons, these measurements provide potential insight into the participant&#39;s level of spatial ability and different engineering problem solving tasks. Performance on the selected tasks can be analyzed and correlated with beta activities. This work presents a detailed research protocol studying the neural efficiency of students engaged in the solving of typical spatial ability and Statics problems. Students completed problems specific to the Mental Cutting Test (MCT), Purdue Spatial Visualization test of Rotations (PSVT:R), and Statics. While engaged in solving these problems, participants&#39; brain waves were measured with EEG allowing data to be collected regarding alpha and beta brain wave activation and use. The work looks to correlate functional performance on pure spatial tasks with spatially intensive engineering tasks to identify the pathways to successful performance in engineering and the resulting improvements in engineering education that may follow.

This manuscript describes an approach to measure neural activity of humans while solving spatially focused engineering problems. The electroencephalogram methodology helps interpret beta brain wave measurements in terms of neural efficiency, with the aim of ultimately enabling comparisons of task performance both between problem types and between participants.

利用脑电图测量任务特定神经效率的比较:空间智能任务

Spatial intelligence is often linked to success in engineering education and engineering professions. The use of electroencephalography enables ...

科研

行为学

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Theta activity is generated in the septohippocampal system and can be recorded using deep intrahippocampal electrodes and implantable electroencephalography (EEG) radiotelemetry or tether system approaches. Pharmacologically, hippocampal theta is heterogeneous (see dualistic theory) and can be differentiated into type I and type II theta. These individual EEG subtypes are related to specific cognitive and behavioral states, such as arousal, exploration, learning and memory, higher integrative functions, etc. In neurodegenerative diseases such as Alzheimer&#39;s, structural and functional alterations of the septohippocampal system can result in impaired theta activity/oscillations. A standard quantitative analysis of the hippocampal EEG includes a Fast-Fourier-Transformation (FFT)-based frequency analysis. However, this procedure does not provide details about theta activity in general and highly-organized theta oscillations in particular. In order to obtain detailed information on highly-organized theta oscillations in the hippocampus, we have developed a new analytical approach. This approach allows for time- and cost-effective quantification of the duration of highly-organized theta oscillations and their frequency characteristics.

Theta activity in the hippocampus is related to specific cognitive and behavioral stages. Here, we describe an analytical method to detect highly-organized theta oscillations within the hippocampus using a time-frequency (i.e., wavelet analysis)-based approach.

高度组织的Theta振荡在鼠脑的自动检测

Theta activity is generated in the septohippocampal system and can be recorded using deep intrahippocampal electrodes and implantable ...

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain signal. Typically, researchers represent the brain's response as the mean across repeated experimental trials and disregard signal fluctuations over time as "noise". However, it is becoming clear that brain signal variability conveys meaningful functional information about neural network dynamics. This article describes the novel method of multiscale entropy (MSE) for quantifying brain signal variability. MSE may be particularly informative of neural network dynamics because it shows timescale dependence and sensitivity to linear and nonlinear dynamics in the data.

Neuroimaging researchers typically consider the brain's response as the mean activity across repeated experimental trials and disregard signal variability over time as "noise". However, it is becoming clear that there is signal in that noise. This article describes the novel method of multiscale entropy for quantifying brain signal variability in the time domain.

脑电图神经影像数据：事件相关电位，光谱功率，和多尺度熵的应用

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain ...

神经科学

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

The effect of a stimulus to the brain depends not only on the parameters of the stimulus but also on the dynamics of brain activity at the time of the stimulation. The combination of electroencephalography (EEG) and transcranial magnetic stimulation (TMS) in a real-time brain state-dependent stimulation system allows the study of relations of dynamics of brain activity, cortical excitability, and plasticity induction. Here, we demonstrate a newly developed method to synchronize the timing of brain stimulation with the phase of ongoing EEG oscillations using a real-time data analysis system. This real-time EEG-triggered TMS of the human motor cortex, when TMS is synchronized with the surface EEG negative peak of the sensorimotor &#181;-alpha (8-14 Hz) rhythm, has shown differential corticospinal excitability and plasticity effects. The utilization of this method suggests that real-time information about the instantaneous brain state can be used for efficacious plasticity induction. Additionally, this approach enables personalized EEG-synchronized brain stimulation which may lead to the development of more effective therapeutic brain stimulation protocols.

This paper describes real-time electroencephalography-triggered transcranial magnetic stimulation to study and modulate human brain networks.

脑状态依赖性脑刺激与实时脑脑图触发颅内磁刺激

The effect of a stimulus to the brain depends not only on the parameters of the stimulus but also on the dynamics of brain activity at the time of the ...

High-density Electroencephalographic Acquisition in a Rodent Model Using Low-cost and Open-source Resources

Advanced electroencephalographic analysis techniques requiring high spatial resolution, including electrical source imaging and measures of network connectivity, are applicable to an expanding variety of questions in neuroscience. Performing these kinds of analyses in a rodent model requires higher electrode density than traditional screw electrodes can accomplish. While higher-density electroencephalographic montages for rodents exist, they are of limited availability to most researchers, are not robust enough for repeated experiments over an extended period of time, or are limited to use in anesthetized rodents.1-3 A proposed low-cost method for constructing a durable, high-count, transcranial electrode array, consisting of bilaterally implantable headpieces is investigated as a means to perform advanced electroencephalogram analyses in mice or rats.

Procedures for headpiece fabrication and surgical implantation necessary to produce high signal to noise, low-impedance electroencephalographic and electromyographic signals are presented. While the methodology is useful in both rats and mice, this manuscript focuses on the more challenging implementation for the smaller mouse skull. Freely moving mice are only tethered to cables via a common adapter during recording. One version of this electrode system that includes 26 electroencephalographic channels and 4 electromyographic channels is described below.

Instructions for the low-cost construction and surgical implantation of a chronic transcranial high-density electroencephalographic montage into mice are provided. Signal recording, extraction, and processing techniques are also described.

高密度的脑电图采集在啮齿动物模型使用低成本的开源资源

Advanced electroencephalographic analysis techniques requiring high spatial resolution, including electrical source imaging and measures of network ...

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Three vigilance states dominate mammalian life: wakefulness, non-rapid eye movement (non-REM) sleep, and REM sleep. As more neural correlates of behavior are identified in freely moving animals, this three-fold subdivision becomes too simplistic. During wakefulness, ensembles of global and local cortical activities, together with peripheral parameters such as pupillary diameter and sympathovagal balance, define various degrees of arousal. It remains unclear the extent to which sleep also forms a continuum of brain states-within which the degree of resilience to sensory stimuli and arousability, and perhaps other sleep functions, vary gradually-and how peripheral physiological states co-vary. Research advancing the methods to monitor multiple parameters during sleep, as well as attributing to constellations of these functional attributes, is central to refining our understanding of sleep as a multifunctional process during which many beneficial effects must be executed. Identifying novel parameters characterizing sleep states will open opportunities for novel diagnostic avenues in sleep disorders.
We present a procedure to describe dynamic variations of mouse non-REM sleep states via the combined monitoring and analysis of electroencephalogram (EEG)/electrocorticogram (ECoG), electromyogram (EMG), and electrocardiogram (ECG) signals using standard polysomnographic recording techniques. Using this approach, we found that mouse non-REM sleep is organized into cycles of coordinated neural and cardiac oscillations that generate successive 25-s intervals of high and low fragility to external stimuli. Therefore, central and autonomic nervous systems are coordinated to form behaviorally distinct sleep states during consolidated non-REM sleep. We present surgical manipulations for polysomnographic (i.e., EEG/EMG combined with ECG) monitoring to track these cycles in the freely sleeping mouse, the analysis to quantify their dynamics, and the acoustic stimulation protocols to assess their role in the likelihood of waking up. Our approach has already been extended to human sleep and promises to unravel common organizing principles of non-REM sleep states in mammals.

Here, we present experimental and analytical procedures to describe the temporal dynamics of the neural and cardiac variables of non-REM sleep in mice, which modulate sleep responsiveness to acoustic stimuli.

量化睡眠小鼠的光谱功率和心率的慢速动力学

Three vigilance states dominate mammalian life: wakefulness, non-rapid eye movement (non-REM) sleep, and REM sleep. As more neural correlates of behavior ...

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Approximately 30% of epilepsy patients are refractory to antiepileptic drugs. In these cases, surgery is the only alternative to eliminate/control seizures. However, a significant minority of patients continues to exhibit post-operative seizures, even in those cases in which the suspected source of seizures has been correctly localized and resected. The protocol presented here combines a clinical procedure routinely employed during the pre-operative evaluation of temporal lobe epilepsy (TLE) patients with a novel technique for network analysis. The method allows for the evaluation of the temporal evolution of mesial network parameters. The bilateral insertion of foramen ovale electrodes (FOE) into the ambient cistern simultaneously records electrocortical activity at several mesial areas in the temporal lobe. Furthermore, network methodology applied to the recorded time series tracks the temporal evolution of the mesial networks both interictally and during the seizures. In this way, the presented protocol offers a unique way to visualize and quantify measures that considers the relationships between several mesial areas instead of a single area.

This protocol describes a procedure to track the evolution of mesial network measures in temporal lobe epilepsy (TLE) patients. It is based on the combination of intracranial recordings with a novel numerical technique for data analysis. Specifically, we present a protocol for network analyses of foramen ovale recordings.

在耐药性颞叶癫痫患者卵圆孔电极记录的网络分析

Approximately 30% of epilepsy patients are refractory to antiepileptic drugs. In these cases, surgery is the only alternative to eliminate/control ...

医学

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Crucial to the success of epilepsy surgery is the availability of a robust biomarker that identifies the Epileptogenic Zone (EZ). High Frequency Oscillations (HFOs) have emerged as potential presurgical biomarkers for the identification of the EZ in addition to Interictal Epileptiform Discharges (IEDs) and ictal activity. Although they are promising to localize the EZ, they are not yet suited for the diagnosis or monitoring of epilepsy in clinical practice. Primary barriers remain: the lack of a formal and global definition for HFOs; the consequent heterogeneity of methodological approaches used for their study; and the practical difficulties to detect and localize them noninvasively from scalp recordings. Here, we present a methodology for the recording, detection, and localization of interictal HFOs from pediatric patients with refractory epilepsy. We report representative data of HFOs detected noninvasively from interictal scalp EEG and MEG from two children undergoing surgery.
The underlying generators of HFOs were localized by solving the inverse problem and their localization was compared to the Seizure Onset Zone (SOZ) as this was defined by the epileptologists. For both patients, Interictal Epileptogenic Discharges (IEDs) and HFOs were localized with source imaging at concordant locations. For one patient, intracranial EEG (iEEG) data were also available. For this patient, we found that the HFOs localization was concordant between noninvasive and invasive methods. The comparison of iEEG with the results from scalp recordings served to validate these findings. To our best knowledge, this is the first study that presents the source localization of scalp HFOs from simultaneous EEG and MEG recordings comparing the results with invasive recordings. These findings suggest that HFOs can be reliably detected and localized noninvasively with scalp EEG and MEG. We conclude that the noninvasive localization of interictal HFOs could significantly improve the presurgical evaluation for pediatric patients with epilepsy.

High Frequency Oscillations (HFOs) have emerged as presurgical biomarkers for the identification of the epileptogenic zone in pediatric patients with medically refractory epilepsy. A methodology for the noninvasive recording, detection, and localization of HFOs with simultaneous scalp electroencephalography (EEG) and magnetoencephalography (MEG) is presented.

同时用脑磁图和脑电图为小儿癫痫的生物标志物检测发作间期高频振动

Crucial to the success of epilepsy surgery is the availability of a robust biomarker that identifies the Epileptogenic Zone (EZ). High Frequency ...

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive neuroscience. Intracranial electroencephalography (EEG) can directly record electrical neural activity and has the unique potential to accomplish this goal. Traditionally, averaging analysis has been applied to analyze intracranial EEG data; however, several new techniques are available for depicting neural activity and intra- and inter-regional connectivity. Here, we introduce two analytical protocols we recently applied to analyze intracranial EEG data using the Statistical Parametric Mapping (SPM) software: time-frequency SPM analysis for neural activity and dynamic causal modeling of induced responses for intra- and inter-regional connectivity. We report our analysis of intracranial EEG data during the observation of faces as representative results. The results revealed that the inferior occipital gyrus (IOG) showed gamma-band activity at very early stages (110 ms) in response to faces, and both the IOG and amygdala showed rapid intra- and inter-regional connectivity using various types of oscillations. These analytical protocols have the potential to identify the neural mechanisms underlying cognitive functions with high spatial and temporal profiles.

We present two analytical protocols that can be used to analyze intracranial electroencephalography data using the Statistical Parametric Mapping (SPM) software: time-frequency statistical parametric mapping analysis for neural activity, and dynamic causal modeling of induced responses for intra- and inter-regional connectivity.

利用 SPM 软件分析颅内脑电图数据的神经活动和连接性

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive ...

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Alteration of electroencephalography (EEG) signals during task-specific movement of the impaired limb has been reported as a potential biomarker for the severity of motor impairment and for the prediction of motor recovery in individuals with stroke. When implementing EEG experiments, detailed paradigms and well-organized experiment protocols are required to obtain robust and interpretable results. In this protocol, we illustrate a task-specific paradigm with upper limb movement and methods and techniques needed for the acquisition and analysis of EEG data. The paradigm consists of 1 min of rest followed by 10 trials comprising alternating 5 s and 3 s of resting and task (hand extension)-states, respectively, over 4 sessions. EEG signals were acquired using 32 Ag/AgCl scalp electrodes at a sampling rate of 1,000 Hz. Event-related spectral perturbation analysis associated with limb movement and functional network analyses at the global level in the low-beta (12-20 Hz) frequency band were performed. Representative results showed an alteration of the functional network of low-beta EEG frequency bands during movement of the impaired upper limb, and the altered functional network was associated with the degree of motor impairment in chronic stroke patients. The results demonstrate the feasibility of the experimental paradigm in EEG measurements during upper limb movement in individuals with stroke. Further research using this paradigm is needed to determine the potential value of EEG signals as biomarkers of motor impairment and recovery.

The experimental protocol demonstrates the paradigm for acquiring and analyzing electroencephalography (EEG) signals during upper limb movement in individuals with stroke. The alteration of the functional network of low-beta EEG frequency bands was observed during the movement of the impaired upper limb and was associated with the degree of motor impairment.

脑电图网络指数作为慢性中风上肢损伤的生物标志物

Alteration of electroencephalography (EEG) signals during task-specific movement of the impaired limb has been reported as a potential biomarker for the ...