Name: cortical source analysis of high-density eeg recordings in children
Uploaded: 2014-06-30T15:00:00.000+00:00
Duration: 9 min 32 s
Description: Watch this Scientific Journal Video about Cortical Source Analysis of High-Density EEG Recordings in Children at JoVE.com

我们要感谢教授约翰·理查兹，南卡罗来纳大学，授予我们访问发育的MRI数据库和有益的讨论。我们还要感谢我们的资助者大奥蒙德街儿童慈善，伦敦大学学院的影响及大挑战。

儿童1。设计脑电图与事件相关电位实验注:一个简单的实验是专为这篇文章可用于研究人脸处理幼儿的目的。下面的章节将描述实验，并解释如何利用MATLAB R2012b和Psychtoolbox V3.0.11 23,24来实现它。被用于这个例子从NimStim组情绪面部表情25拍摄的照片。根据要求这种刺激是一套可用于研究目的（ <a href="http://www.macbrain.org/resources.htm">http://www.macbrain.org/resources.htm</a> ）。 <ol><li>传输将RGB图像灰度，以减少刺激之间的差异。 见表1注:这些命令需要的图像处理工具箱（ <a href="http://www.mathworks.co.uk/products/image/">http://www.mathworks.co.uk/products/image/</a> ）。替代品可能会发现吨hrough的文件交换（ <a href="https://www.mathworks.co.uk/matlabcentral/fileexchange">https://www.mathworks.co.uk/matlabcentral/fileexchange</a> ）。 </li><li>用实验控制软件来实现的实验使用的一系列命令（ 见表1中的示例）刺激呈现触发器精确定时。 </li></ol> 2，数据采集<ol><li>确保儿童是舒服的测试环境。让年幼的孩子坐在他们的照顾者的膝盖上或在一个舒适的儿童座椅。让孩子看得见，摸得着的传感器网络应用到孩子的头之前。如果有多余的网，有​​家长也试一上，或放置一对玩偶或毛绒玩具。 </li><li>测量最大头围来选择对孩子正确的净尺寸。用尺量，并将其抱到鼻根。然后头部周围的测量周围的最大周长（〜1厘米INION以上）。注意:保持测量头围和记录用于日后分析26传感器网。它帮助，如果父母头上还测得让孩子更舒适的局面。 </li><li>识别头部的顶点在鼻根和INION和左和右耳部点之间的中点的距离的交叉点。标志着这点与中国的笔，以确保顶点通道应用时净值正确定位。 </li><li>适用于传感器网络，并确保关键通道与解剖标志（鼻根，INION，顶点和左/右乳突）对齐。注:为了获得最准确的结果，在头部通道的位置可以被数字使用特殊的数位化设备获得的。希望获得传感器的位置研究者应参阅相应的硬件和软件手册。另外，该承担的电极安置标准解剖沿兰电极地图dmarks都可以使用。这些地图可以被扭曲为在分析部分所述的年龄适当的头部模型。 </li><li>确保渠道有通过分别定位传感器与头皮接触良好;轻轻扭动每个传感器从一侧到另一侧移动的头发出的方式。 </li><li>测量信道增益和通道阻抗。点击"开始"，开始记录在NetStation的脑电记录软件，并开始增益和阻抗测量。如果测量不自动启动，使用"校准放大器"和"测量阻抗净"按钮。 </li><li>检查录音软件渠道阻抗大于50KΩ将出现红色的高。用移液管，以降低通道阻抗应用额外的电解质溶液。检查脑电图显示屏，显示频率高的活性，尽管比周边通道（扁线通道）低阻抗或明显少活动的渠道。这些信道可以甲肝e为头皮和需要调整接触不良。 </li><li>为了脑电图制备过程中让孩子舒适，让孩子听音乐，看年龄适当的卡通或使用其它实验者转移他们的注意力， 例如 ，吹肥皂泡的幼儿。 </li></ol> 3。分析<ol><li>预处理<ol><li>数字滤波器，一个高通滤波器的数据用切断在0.1赫兹，除去信道漂移27（表1）。 </li><li>对ERP的分析，应用低通滤波器，截止于30赫兹27（ 表1）。 </li><li>根据录制过程中设置的触发代码划时代的连续数据。对于大多数实验中，使用前200毫秒的基线刺激呈现和600毫秒后刺激间隔，以支付利息（ 表1）的时间间隔。 </li><li>删除包含移动或闪烁文物时代:标记C具有峰值hannels峰值幅度大于150毫伏 - 调整这个阈值取决于参与者组和数据质量。为了保持一致性，使用相同的阈值在一项研究中所有参与者。如果一个信道是高于此阈值的时期的30％以上，取出通道（通道活性可以从周围的通道内插，如果这些含有可接受的数据）。如果通道的20％以上都被标记为坏的时代，取出时代。如果信道的20％以上是由算法或拒绝历元的低于50％被保留，考虑从进一步的分析（ 表1）取出数据集。 注:百分比阈值时代和信道抑制是大概的数字是消除我们的经验噪音足量。神器在记录的金额有可能使用其他参与者组，实验范式或脑电图系统是不同的。实验者可能要调整的百分比阈值，并检查它们是否满意的神器拒绝。另外，实验者可以拒绝包含神器通过目视检查试验。 </li><li>再参照平均参考减去平均活动横跨从每个通道（ 表1）信道。注意:该顶点电极通常用作在NetStation的记录参考。 </li></ol></li><li>神器修正的独立成分分析<ol><li>将数据导入到更快的工具箱28和运行上的数据自动神器抑制算法（ 表1）。 </li><li>使用图形用户界面（GUI），用于速度更快;打开GUI，类型FA​​STER_GUI进入命令行模式。 </li><li>取消选择的过滤菜单中的过滤选项作为数据已经在悬置前过滤。 </li><li>指定的信道数:126脑电图频道与2 electrooculagram（EOG）频道。 </li><li>输入用于悬置的数据作为一个单元阵列的串标记。对于所提出的情况下，输入:{&#39;面子&#39;，&#39;涉农供应链&#39;}为面和炒脸上的条件。 </li><li>选择频道的独立成分分析（ICA）。通常情况下选择所有记录通道，包括。外部非脑电图渠道。 </li><li>指定在GUI的右窗格中的输入和输出文件夹。 </li><li>选择录音相应的频道文件。注:通道的文件的大部分EEG系统可以是从制造商处下载，或者可以从EEGLAB网站下载。 </li><li>点击RUN启动更快的处理速度。取决于记录和文件的数目的长度，这样的处理可能需要几个小时。 </li><li>目视检查记录，独立成分地图和ERP的处理后。 </li></ol></li><li>事件相关电位数据的通道电平分析<ol><li>结合几个渠道，形成一个虚拟的CHANNEL具有更好的信噪比（ 表1）。注:渠道的选择应根据文献或先验假设以前的报告。这表明在给定的时间窗口中的最高振幅选择信道是不建议29。 </li><li>获得类似的峰值幅度的措施，是指振幅和峰潜伏期表征的波形和进行统计检验（ 表1）。 </li></ol></li><li>建立边界元模型（BEM） <ol><li>段解剖MRI扫描与FreeSurfer。注:为了获得最准确的结果，基部边界元模型对单个MRI扫描每个参与者。如果这是不可行的，参与者的年龄匹配尽可能平均MRI模板应该被使用。请注意，BEMS不能被用于24个月以下的儿童。边界元模型假定每个壳（脑，头骨，皮肤）由一个封闭的外壳。然而，沿y翁荣南孩子的囟门在头骨不是封闭的，这违反了封闭外壳假设。 <ol><li>为了安装FreeSurfer软件，首先从FreeSurfer网站（下载<a href="http://freesurfer.net/fswiki/DownloadAndInstall">http://freesurfer.net/fswiki/DownloadAndInstall</a> ）。接下来，设置shell环境包括FreeSurfer;对于bashrc中，包括在bashrc文件下面的命令: <ol><li>导出FREESURFER_HOME = /应用/ freesurfer / </li><li>来源$ FREESURFER_HOME / FreeSurferEnv.sh 注意:这些命令假定FreeSurfer文件夹是在Unix系统上的应用程序文件夹。有关于如何设置FreeSurfer更多的细节与替代shell环境， 如 CSH / tcsh中，或FreeSurfer网站上的操作系统（ <a href="http://freesurfer.net/fswiki/DownloadAndInstall">http://freesurfer.net/fswiki/DownloadAndInstall</a> ）。 </li></ol></li><li>接下来，定义主题方向托里，即输出将被写入使用以下命令的文件夹: <ol><li>出口SUBJECTS_DIR = / ... / BEMS / 注意:结果可能会被写入到系统上的任何文件夹。 </li></ol></li><li>接下来，工作目录更改为包含使用以下命令为边界元模型的MRI检查文件的文件夹: <ol><li> CD /用户/ joebathelt / Neurodevelopmental_MRI_database /儿童/脑/ 注:在系统上的任何文件夹可以使用cd命令的语法来指定。这是主要的研究人员的文件结构的一个例子。 </li></ol></li><li>最后，使用以下命令启动重建: <ol><li>重建全我&lt;mri_file&gt;-subjid &lt;subject_id&gt; </li><li>重建全全subjid &lt;subject_id&gt; 注意:需要更换与所需的MRI扫描的在当前目录中的文件名&lt;mri_file&gt;。 &lt;subject_id&gt;可以代表股价与任何名称。 FreeSurfer将在主题目录下创建一个文件夹，此名称。根据所使用的系统上，最后一个命令可能需要一些时间来执行。 </li></ol></li></ol></li><li>检查FreeSurfer分割不正确的分割， 例如 。重叠的领域，解剖学上不可能车厢等 。通过导入段到头脑风暴，并使用显示工具在GUI中: <ol><li>在头脑风暴中，选择解剖窗格。通过右键单击对象并选择"导入解剖文件夹"导入分割MRI检查。确保该文件夹与FreeSurfer输出被选中。通过右击并选择"显示"检查分割视觉。注意:可替换地，可以使用FreeSurfer命令。详细说明可在FreeSurfer网站上找到: <a href="http://surfer.nmr.mgh.harvard.edu/fswiki/RecommendedReconstruction">http://surfer.nmr.mgh.harvard.edu/fswiki/RecommendedR</a>econstruction。如果感兴趣分析的基础上解剖土地分割区域是理想，FreeSurfer功能<a href="https://surfer.nmr.mgh.harvard.edu/fswiki/mris_ca_label?action=fullsearch&amp;context=180&amp;value=linkto%3A%22mris_ca_label%22">mris_ca_label</a> ， <a href="https://surfer.nmr.mgh.harvard.edu/fswiki/mri_annotation2label?action=fullsearch&amp;context=180&amp;value=linkto%3A%22mri_annotation2label%22">mri_annotation2label</a>和<a href="https://surfer.nmr.mgh.harvard.edu/fswiki/mri_mergelabels?action=fullsearch&amp;context=180&amp;value=linkto%3A%22mri_mergelabels%22">mri_mergelabels</a>都可以使用。请参阅FreeSurfer出版物和在线帮助页以获取更多详细信息。 </li></ol></li></ol></li><li>估计在头脑风暴的源活动<ol><li>通过在命令窗口中键入"头脑风暴"开始头脑风暴。 </li><li>通过从文件菜单中选择新建协议创建一个新的协议。 </li><li>添加一个新的主旨ECT的协议通过从文件菜单中选择新建主题。 </li><li>进口脑电图数据，关于这一问题的参与者通过右键单击并选择"导入MEG / EEG"。 </li><li>通过右键单击并选择"导入通道文件"导入声道文件。注:通道文件需要对齐MRI对源重建。头脑风暴使用的用户需要在MRI，以纪念4解剖参考点的系统。请参阅教程脑力激荡更多信息（ <a href="http://neuroimage.usc.edu/brainstorm/CoordinateSystems">http://neuroimage.usc.edu/brainstorm/CoordinateSystems</a> ）。作为一个特定的脑电图系统或理想情况下，可以使用进行了数字化的EEG记录之前，该头的位置所定义的信道文件的标准位置。 </li><li>检查边界元法和渠道调整预期:右键单击通道文件为主体，并导航到"MRI注册"和";检查"。注意:如果模型中的领域重叠，或者如果通道边界元法中，源重建会产生不正确的结果。通过使用"编辑"选项中的"MRI分割"菜单中调整对齐。 </li><li>选择"噪声协方差矩阵"和"从拍摄计算"通过右键单击参与者来自各个时代的基线计算噪声协方差矩阵和。注:头脑风暴工具箱的作者建议使用短录音对角线噪声协方差矩阵（〜比渠道较少的时间点）和一个完整的一个用于较长的录音。请参阅头脑风暴源估计教程的详细资料: <a href="http://neuroimage.usc.edu/brainstorm/Tutorials/TutSourceEstimation">http://neuroimage.usc.edu/brainstorm/Tutorials/TutSourceEstimation</a> 。 </li><li>通过右击计算源模型上的主题，然后选择"COMPUT辰源模式"。 </li><li>使用深度加权最小模估计通过右击对象并选择"计算源"和"最小模估计"计算的反解。注:其他选项（DSPM，sLORETA）可供选择。每个选项都有不同的优点和缺点。该算法应该基于先验的考虑和以前的报告在文献中进行选择。此外，一些算法是在某些领域解决焦活化较好，而另一些更适合于广泛的活化。跨国公司用于此研究的基础上文献16以前的报告。为了保持一致性，应采用在一项研究中所有参与者的相同算法进行逆溶液。研究人员还可能需要比较​​强劲的结果如何是不同的逆解算法的应用。 </li><li>重复第3节研究中的所有参与者。注意:无论是使用梯度PHICAL混油界面或脚本重复处理步骤的参与者。看到头脑风暴文档说明（ <a href="http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript">http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript</a> ）。 </li><li>每多参加试验的平均源活动通过拖动录音过程菜单并选择"一般"和"按条件（须平均）"。 </li><li>通过选择"过程2"和拖动每个条件在一个窗口中对比的条件。然后，选择"测试"和"学生t-检验"或"学生t检验（配对）"根据设计。要在生成的统计图中的"统计"菜单中的显示进行多重比较，设置幅度和面积阈值。注:另外，在激活图可以导出到SPM（ <a href="http://www.fil.ion.ucl.ac.uk/spm/">http://www.fil。</a>ion.ucl.ac.uk / SPM /）进行更深入的统计分析（ <a href="http://neuroimage.usc.edu/brainstorm/ExportSpm12%20HYPERLINK%20%22http:/neuroimage.usc.edu/brainstorm/ExportSpm12%22)">http://neuroimage.usc.edu/brainstorm/ExportSpm12 HYPERLINK"http://neuroimage.usc.edu/brainstorm/ExportSpm12"） 。</a> </li><li>计算的感兴趣的区域中的与事件相关的响应。对于基于土地分割的ROI，加载FreeSurfer土地分割右击皮质表面解剖菜单，选择"导入标签"。浏览相应的文件并加载它。现在，选择在功能数据菜单中的"侦察兵"窗格中的投资回报率。 </li><li>获得通过拖动文件到程序1窗口中的投资回报率事件相关的活动，然后从源菜单中的"童军提取时间序列"。注:有几个投资回报可同时选择和投资回报率的时间序列可以导出为进一步绘制和分析右击侦察员​​时间系列数据并选择"导出到Matlab的"。 </li></ol></li></ol>

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本文章介绍了高密度的脑电图为根据年龄适当的平均MRI模板和深度加权最小模估计在一个标准的ERP模式适合儿童使用的重建边界元模型皮质发电机的记录和分析。在这个范例中，面孔和炒面孔的照片呈现。不同的作者用这种模式来探讨人脸处理机制在开发35的发展。在渠道层面，在右侧枕颞渠道更负偏差的描述为面条件的炒脸上的条件。地形，延迟和响应特性都与N170组件34一致。?...

美国总统奥巴马所描述的人类大脑的科学发现与高度重视对健康和经济3（http://www.whitehouse.gov/share/brain-initiative）的下一个前沿领域。然而，像在自然科学的其他领域，神经科学依赖于方法和分析技术的进步进步。在大约在人类脑功能研究两种常用的非侵入性工具是磁共振成像（MRI）和脑电图（EEG）。这些工具利用不同的物理属性，并提供不同的见解与独特的优点和缺点大脑功能。 MRI使用的磁场内的水分子的磁特性，得到生物体组织的图像。受试者需要被放置在具有高磁场强度的磁体。在这个过程中参与者的运动受到限制，参与者必须容忍造成的磁快速变化的噪声场。除了结构图像，核磁共振也提供了衡量改变血液氧合研究脑功能成像（fMRI）的可能性。综上所述，MRI可提供高达0.5 立方毫米与现代高科技领域的扫描仪和优化参数4相对较高的空间分辨率。与此相反，功能磁共振成像的时间分辨率是有限的BOLD响应，其中仅间接反映神经活动5,6的高时间动态的慢动力学。 另一方面，脑电图测量通过放置在头皮上的电极所造成的神经元活性的改变的电活动。在脑电图技术的最新进展允许快速和方便的应用传感器的短期或长期的，固定的，以及动态的录音。因为脑电图的限制较少，它也是首选的某些参与者群体不容忍MRI环境中以及类似的方法儿科和某些老人科及精神科的人群。脑电图的特性显示出逆图案的那些核磁共振:时间分辨率是非常高的精确到毫秒，但其空间分辨率是有限的。电电流通过不同组织中的发电机和EEG电极之间的头皮的表面上。这导致混合并称为容积传导效应源活动的空间拖尾现象。因此，通过在电极上的头皮的表面上测得的活性反映了来自这可能是遥远的电极上的头1,7的位置多个源活性。 近年来大量的工作一直致力于MRI和脑电图的合并，以发挥各自的长处。工作的一号线是专门为同时采集脑电图和MRI的功能研究。另一种方法是使用由结构的MRI提供的空间信息来采取的卷C的账户onduction效果通过生物物理建模。对于脑电图记录的源重建利用结构信息是涉及儿童人群的研究特别有用。脑功能的发展的调查是了解如何复杂的认知技能是建立在简单的前体8顶。 这些调查有助于突出变化的神经基质和反应特性与变化，行为表现相关。但是，在开发过程中脑功能和认知的调查也带来了特殊的挑战。尤其是，这个机会对于功能性磁共振成像研究有限，因为让小孩和婴儿要么是睡着了还是镇静获得的MRI数据，而无需移动文物和参与者身心健康的负面影响。此外，脑电图是由父母，这让研究参与者的招聘更容易被认为是风险小，微创。 Ŧherefore，脑电图是首选幼儿大脑功能的许多调查的方法。在脑电图系统方法论的进步使高密度电极阵列具有128个或更多的渠道分钟内的应用。易于应用及穿着的舒适性是足够的，甚至允许脑电记录中最年轻的婴儿。然而，很多研究人员也不能只关心响应特定刺激的时空动态，也想比较介导的反应的神经基础。 在比较不同年龄组通道电平的ERP分析的普遍假设是相同的神经反应底物，但该时间或响应幅度的跨时代9有所不同。类似头皮地形经常被用来作为类似相关的神经活动的指标。然而，许多不同的源的配置可能会导致类似的头皮地形10。通过应用源估计，这uncertainty可以减小和量化。观测值的独立性是对脑功能的网络帐户的关键:如果源混合时，相关性会偏向更高的本地连接。源重建可应用于减少这种偏差11。可替换地，可以用于连接分析在定时和相位的差异，但这些数学模型要求是很难在非模拟的数据12，以评估假设。综上所述，源估计提供了额外的信息，以建立在有关解剖学和组织的生物物理特性通道水平脑电图和ERP分析。 不同的算法已经制订找到解决逆问题。这些算法大致可分为两类:参数和非参数13。参数模型假定一个或多个偶极子，可能在位置，方向和强度有所不同。与此相反，非参数模型含有多种呐大量偶极子与固定的位置和方向。在这些模型中，头皮电活动被解释为激活的在固定偶极子10,13,14的组合。非参数，分布源模型可以建立在有关解剖学和电导率在不同介质中。边界元模型结合电导率值的头部与不同的炮弹对大脑，脑脊髓液，和颅骨的主要组织。这是基于这样的假设电导率为每个隔间内大多不变，但显着的变化发生在不同的隔室的边界。有限元模型基础上，进一步细分MR扫描成灰质和白质，使电导率值可以分配给每个像素15。 在实际应用中，非参数化模型是用于重建源特别有用的复杂认知任务，其中所涉及的区域的数量可不知道10。边界元模型是最广泛使用于目前的文献，可能是因为更准确的有限元模型构成比较高的计算需求。此外，还有在灰质和白质相当大的个体间变异，使前端模块，应根据个人的MRI扫描。 非参数化模型需要匹配的头皮测定活性的正向模型的预测的第二步骤。再次，不同的方法具有不同的优点和缺点已经在文献中讨论的（参见Michel 等人，2004年为一个概观）。最广泛使用的算法是基于最小范数估计（MNE），其中头皮测量活动相匹配，以最低的总强度16的正向模型中的电流分布。跨国公司偏向疲弱和肤浅的来源。深度加权跨国公司算法试图通过引入权重降低表面偏置基于数学假设10矩阵。被广泛使用的LORETA方法也是基于加权跨国公司，但另外减少源的拉普拉斯算子，从而导致更顺畅的解决方案17,18。 LORETA已被发现表现最佳的单一来源的模拟研究19,20。然而，LORETA可能会导致过度的解决方案的平滑。深度加权MNE优选当来源是未知的或多个源可能是存在13，16。比较不同算法的结果来评估不同的模型假设的影响，建议。 综上所述，通过建模方法来源重建一直局限于儿童，直到最近。这是因为大多数脑电信号分析软件基于成人解剖，大大限制了孩子2,8源码解决方案的准确性依赖于头部模型。廉价获得的计算能力和提供源重建用户友好的软件使其能够克服这些限制。应用源估计到脑电提供基于单独通道水平的观察分析以上两个重要的优点:提高了空间分辨率和观测独立性。 源估计可能不翔实在某些情况下:头的良好覆盖，需要区分来源。具有128或更多个电极的高密度系统被推荐10,15;一个稀疏的覆盖面将作为一个空间滤波器导致更广泛的传播源的激活或假阴性结果10。此外，根据本文所述的方法源重建已被发现的皮质发电机。因此，不太适合约皮层下基底或皮质皮质下的相互作用假设检验。最后，源代码分析，应根据有关皮质基底详细的事先假设，同时现有的文献从其他成像方式考虑。空间滤波技术也可用于通过降低头皮上的电平的空间混合，以改善脑电图信号的空间分辨率。替代方法来减少无头造型容积传导效应的影响被使用， 例如 ，拉普拉斯滤波21或电流源密度分析22。但是，这些方法不提供有关神经发生源的详细信息，如体积传导效应不仅局限于传感器紧密的空间接近1。 在下面的章节中，本文介绍了如何为实验和脑在2岁以下的儿童认知功能的调查是在伦敦婴儿实验室设计的。接着，脑电图数据采集与孩子高密度低阻抗系统进行了讨论。然后，脑电图预处理和分析上的声道电平提出。 LastlY，本文侧重于结构MRI数据皮质来源的重建和源电平信号的分析处理。

<table><tbody><tr><td>High-density EEG sensor net (128 or 256 channels)</td><td>HydroCel Geodesic Sensor Net 128</td><td>Electrical Geodesic Inc., Oregon, US</td><td></td></tr><tr><td>EEG high impendance amplifier</td><td>NetAmps 200</td><td>Electrical Geodesic Inc., Oregon, US</td><td></td></tr><tr><td>Data Acquisition Computer&amp;nbsp;</td><td>PowerMac G4</td><td>Apple Inc, California, US</td><td></td></tr><tr><td>Stimulus Presentation Computer</td><td>Optiplex 745</td><td>Dell Computers Inc., Texas, US</td><td></td></tr><tr><td>Stimulus Presentation Software</td><td>MATLAB R2012b with PsychToolBox</td><td>Brainard &lt;em&gt;et al.&lt;/em&gt; 1997</td><td></td></tr><tr><td>EEG recording software</td><td>NetStation 4.5.1</td><td>Electrical Geodesic Inc., Oregon, US</td><td></td></tr><tr><td>EEG analysis software</td><td>MATLAB R2012b</td><td>The Mathworks Inc.</td><td></td></tr><tr><td></td><td>EEGLAB</td><td>Delorme et &lt;em&gt;al.&lt;/em&gt; 2004</td><td></td></tr><tr><td></td><td>BrainStorm</td><td>Sylvain &lt;em&gt;et al.&lt;/em&gt; 2001</td><td></td></tr><tr><td>MRI processing software</td><td>FreeSurfer</td><td>Fischl &lt;em&gt;et al.&lt;/em&gt; 2004</td><td></td></tr><tr><td></td><td>OpenMEEG</td><td>Gramfort &lt;em&gt;et. al.&lt;/em&gt; 2010</td><td></td></tr><tr><td colspan="4">Delorme, A., &amp;amp; Makeig, S. EEGLAB: an open source toolbox for analysis of single trial EEG dynamics including independent component analysis. &lt;em&gt;Journal of Neuroscience Methods&lt;/em&gt;, &lt;strong&gt;134&lt;/strong&gt;(1), 9&amp;ndash;21, (2004).</td></tr><tr><td colspan="4">Sylvain, B., John, C., Dimitrios, P., &amp;amp; Richard, M. Brainstorm: A User Friendly Application for MEG/EEG Analysis. &lt;em&gt;Computational Intelligence and Neuroscience&lt;/em&gt;, 1&amp;ndash;13, (2011).</td></tr><tr><td colspan="4">Fischl, B. &lt;em&gt;et al.&lt;/em&gt; Automatically parcellating the human cerebral cortex. &lt;em&gt;Cerebral Cortex&lt;/em&gt;, &lt;strong&gt;14&lt;/strong&gt;(1), 11&amp;ndash;22, (2004).</td></tr><tr><td colspan="4">Gramfort, A., Papadopoulo, T., Olivi, E., &amp;amp; Clerc, M. OpenMEEG: opensource software for quasistatic bioelectromagnetics. &lt;em&gt;BioMedical Engineering OnLine&lt;/em&gt;, &lt;strong&gt;9&lt;/strong&gt;(1), 45, (2010).</td></tr><tr><td colspan="4">Brainard, D. H. The psychophysics toolbox. &lt;em&gt;Spatial vision&lt;/em&gt;, (1997).</td></tr></tbody></table>

cortical source analysis of high-density eeg recordings in children

脑电图是传统描述为具有高时间和低空间分辨率的影像学技术。在生物物理建模和信号处理的最新进展使人们有可能利用从类似结构的MRI能够提供高空间分辨率来克服这个限制1其他成像方式的信息。这对于那些需要高解析度的时间和空间域的研究特别有用。此外，由于应用方便和脑电图记录的成本低，脑电图往往是首选的方法，与人群，如幼儿，不容忍功能性磁共振成像扫描工作顺利。然而，为了调查哪些神经基础是从结构的MRI介入，解剖信息还是需要的。大多数脑电图分析软件包与基于成人解剖学标准头型号。这些模型用于儿童时的精确度是有限的2，因为合作mposition和头部的空间构型组织中的变化较显着的发展。3。 在本文中，我们提供基于单个结构MRI扫描或特定年龄头部模型重建高密度的脑电图皮质发电机在利用头部模型我们近期工作的概述。本文介绍如何脑电图记录被收购，加工，并与儿童人群在伦敦婴儿实验室，包括实验室的设置，任务设计，脑电图预处理，MRI检查处理，和EEG通道的水平和来源分析分析。

设计的ERP实验婴儿和儿童往往是具有挑战性的，因为他们的能力有限忍受长期反复的实验30。当实验者计划申请源的重建，因为准确的来源重建将需要高信噪比1， 图1显示了一个实验性协议，可以用非常年幼的儿童使用面处理机制，调查这个问题进一步恶化。该模式适用于a）在刺激呈现减少眨眼和眼球运动，因为孩子会少一些能够控制眼球运动比成年志?...

Watch this Scientific Journal Video about Cortical Source Analysis of High-Density EEG Recordings in Children at JoVE.com

Cortical Source Analysis of High-Density EEG Recordings in Children

近年来，出现了在估计头皮测量电活动的认知神经科学实验的皮质来源的兴趣日渐浓厚。本文介绍了如何高密度脑电图是从2岁在伦敦婴儿实验室采集和录音如何处理孩子的皮质来源的估计。

高密度的脑电图记录在儿童的皮质来源分析

EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and ...

cortical-source-analysis-of-high-density-eeg-recordings-in-children

Universidad San Sebastian

Research

JoVE Journal

Behavior

21.2K 次观看.  UCL Institute of Child Health. 近年来，出现了在估计头皮测量电活动的认知神经科学实验的皮质来源的兴趣日渐浓厚。本文介绍了如何高密度脑电图是从2岁在伦敦婴儿实验室采集和录音如何处理孩子的皮质来源的估计。 

视频: Cortical Source Analysis of High-Density EEG Recordings in Children

Infant Auditory Processing and Event-related Brain Oscillations

Rapid auditory processing and acoustic change detection abilities play a critical role in allowing human infants to efficiently process the fine spectral and temporal changes that are characteristic of human language. These abilities lay the foundation for effective language acquisition; allowing infants to hone in on the sounds of their native language. Invasive procedures in animals and scalp-recorded potentials from human adults suggest that simultaneous, rhythmic activity (oscillations) between and within brain regions are fundamental to sensory development; determining the resolution with which incoming stimuli are parsed. At this time, little is known about oscillatory dynamics in human infant development. However, animal neurophysiology and adult EEG data provide the basis for a strong hypothesis that rapid auditory processing in infants is mediated by oscillatory synchrony in discrete frequency bands. In order to investigate this, 128-channel, high-density EEG responses of 4-month old infants to frequency change in tone pairs, presented in two rate conditions (Rapid: 70 msec ISI and Control: 300 msec ISI) were examined. To determine the frequency band and magnitude of activity, auditory evoked response averages were first co-registered with age-appropriate brain templates. Next, the principal components of the response were identified and localized using a two-dipole model of brain activity. Single-trial analysis of oscillatory power showed a robust index of frequency change processing in bursts of Theta band (3 - 8 Hz) activity in both right and left auditory cortices, with left activation more prominent in the Rapid condition. These methods have produced data that are not only some of the first reported evoked oscillations analyses in infants, but are also, importantly, the product of a well-established method of recording and analyzing clean, meticulously collected, infant EEG and ERPs. In this article, we describe our method for infant EEG net application, recording, dynamic brain response analysis, and representative results.

High-density electroencephalography (dEEG) is being used increasingly to study brain development and plasticity in the early years of life. Here we present an application of sophisticated analysis techniques that builds on traditional EEG recording to understand the oscillatory dynamics of rapid auditory processing in the infant brain.

婴儿听觉处理和事件相关的脑振荡

Rapid auditory processing and acoustic change detection abilities play a critical role in allowing human infants to efficiently process the fine spectral ...

科研

行为学

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

Dense array electroencephalography (dEEG), which provides a non-invasive window for measuring brain activity and a temporal resolution unsurpassed by any other current brain imaging technology1,2, is being used increasingly in the study of social cognitive functioning in infants and adults. While dEEG is enabling researchers to examine brain activity patterns with unprecedented levels of sensitivity, conventional EEG recording systems continue to face certain limitations, including 1) poor spatial resolution and source localization3,4,2) the physical discomfort for test subjects of enduring the individual application of numerous electrodes to the surface of the scalp, and 3) the complexity for researchers of learning to use multiple software packages to collect and process data. Here we present an overview of an established methodology that represents a significant improvement on conventional methodologies for studying EEG in infants and adults. Although several analytical software techniques can be used to establish indirect indices of source localization to improve the spatial resolution of dEEG, the HydroCel Geodesic Sensor Net (HCGSN) by Electrical Geodesics, Inc. (EGI), a dense sensory array that maintains equal distances among adjacent recording electrodes on all surfaces of the scalp, further enhances spatial resolution4,5,6 compared to standard dEEG systems. The sponge-based HCGSN can be applied rapidly and without scalp abrasion, making it ideal for use with adults7,8, children9,10,11, and infants12, in both research and clinical4,5,6,13,14,15 settings. This feature allows for considerable cost and time savings by decreasing the average net application time compared to other dEEG systems. Moreover, the HCGSN includes unified, seamless software applications for all phases of data, greatly simplifying the collection, processing, and analysis of dEEG data.
The HCGSN features a low-profile electrode pedestal, which, when filled with electrolyte solution, creates a sealed microenvironment and an electrode-scalp interface. In all Geodesic dEEG systems, EEG sensors detect changes in voltage originating from the participant's scalp, along with a small amount of electrical noise originating from the room environment. Electrical signals from all sensors of the Geodesic sensor net are received simultaneously by the amplifier, where they are automatically processed, packaged, and sent to the data-acquisition computer (DAC). Once received by the DAC, scalp electrical activity can be isolated from artifacts for analysis using the filtering and artifact detection tools included in the EGI software. Typically, the HCGSN can be used continuously for only up to two hours because the electrolyte solution dries out over time, gradually decreasing the quality of the scalp-electrode interface.
In the Parent-Infant Research Lab at the University of Toronto, we are using dEEG to study social cognitive processes including memory, emotion, goals, intentionality, anticipation, and executive functioning in both adult and infant participants.

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

Dense array electroencephalography is being used increasingly to study social cognitive functions in infants and adults. Here we present an established methodology that represents a significant improvement on conventional methodologies for studying EEG in infants and adults.

调查婴儿和成人使用密集阵脑电图的社会认知（ D脑电图）

Dense array electroencephalography (dEEG), which provides a non-invasive window for measuring brain activity and a temporal resolution unsurpassed by any ...

神经科学

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

Stereotactic Electroencephalography (SEEG) is a technique used to localize seizure foci in patients with medically intractable epilepsy. This procedure involves the chronic placement of multiple depth electrodes into regions of the brain typically inaccessible via subdural grid electrode placement. SEEG thus provides a unique opportunity to investigate brain function. In this paper we demonstrate how SEEG can be used to investigate the role of the dorsal anterior cingulate cortex (dACC) in cognitive control. We include a description of the SEEG procedure, demonstrating the surgical placement of the electrodes. We describe the components and process required to record local field potential (LFP) data from consenting subjects while they are engaged in a behavioral task. In the example provided, subjects play a cognitive interference task, and we demonstrate how signals are recorded and analyzed from electrodes in the dorsal anterior cingulate cortex, an area intimately involved in decision-making. We conclude with further suggestions of ways in which this method can be used for investigating human cognitive processes.

Stereotactic Electroencephalography (SEEG) is an operative technique used in epilepsy surgery to help localize seizure foci. It also affords a unique opportunity to investigate brain function. Here we describe how SEEG can be used to investigate cognitive processes in human subjects.

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 ...

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Electroencephalogram (EEG) has been traditionally used to determine which brain regions are the most likely candidates for resection in patients with focal epilepsy. This methodology relies on the assumption that seizures originate from the same regions of the brain from which interictal epileptiform discharges (IEDs) emerge. Preclinical models are very useful to find correlates between IED locations and the actual regions underlying seizure initiation in focal epilepsy. Rats have been commonly used in preclinical studies of epilepsy1; hence, there exist a large variety of models for focal epilepsy in this particular species. However, it is challenging to record multichannel EEG and to perform brain source imaging in such a small animal. To overcome this issue, we combine a patented-technology to obtain 32-channel EEG recordings from rodents2 and an MRI probabilistic atlas for brain anatomical structures in Wistar rats to perform brain source imaging. In this video, we introduce the procedures to acquire multichannel EEG from Wistar rats with focal cortical dysplasia, and describe the steps both to define the volume conductor model from the MRI atlas and to uniquely determine the IEDs. Finally, we validate the whole methodology by obtaining brain source images of IEDs and compare them with those obtained at different time frames during the seizure onset.

This video introduces the preparation, recording, and source analysis procedures of high-resolution EEG on sedated rats with a particular preclinical model of focal epilepsy under noninvasive conditions.

采用高分辨率脑电图记录脑源成像局灶性癫痫的临床前模型大鼠

Electroencephalogram (EEG) has been traditionally used to determine which brain regions are the most likely candidates for resection in patients with ...

医学

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 ...

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 ...

Application of an Amplitude-integrated EEG Monitor (Cerebral Function Monitor) to Neonates

Amplitude-integrated EEG (aEEG) is an easily accessible technique to monitor the electrocortical activity in preterm and term infants in neonatal intensive care units (NICUs). This method was first used to monitor newborns after asphyxia, providing information about future neurological outcomes. The aEEG is also helpful to select newborns who benefit from cooling. The aEEG monitoring of preterm infants is becoming more widespread, as various studies have shown that neurodevelopmental outcome is related to early aEEG tracings. Here, we demonstrate the application of the aEEG monitoring system and present typical patterns that depend upon gestational age and pathophysiological conditions. Furthermore, we mention pitfalls in the interpretation of the aEEG, as this method requires accurate fixation and localization of the electrodes. Additionally, the raw EEG can be used to detect neonatal seizures or to identify aEEG application problems. In conclusion, aEEG is a safe and generally well-tolerated method for the bedside monitoring of neonatal cerebral function; it can even provide information about long-term outcome.

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

Here, we show how to apply amplitude-integrated electroencephalography to monitor cerebral function in neonates.

振幅综合脑电图监测仪 (脑功能监护仪) 在新生儿中的应用

Amplitude-integrated EEG (aEEG) is an easily accessible technique to monitor the electrocortical activity in preterm and term infants in neonatal ...

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

This paper presents a methodology for the recording and analysis of cortical visual evoked potentials (CVEPs) in response to various visual stimuli using 128-channel high-density electroencephalography (EEG). The specific aim of the described stimuli and analyses is to examine whether it is feasible to replicate previously reported CVEP morphological patterns elicited by an apparent motion stimulus, designed to simultaneously stimulate both ventral and dorsal central visual networks, using object and motion stimuli designed to separately stimulate ventral and dorsal visual cortical networks.&#160; Four visual paradigms are presented: 1. Randomized visual objects with consistent temporal presentation. 2. Randomized visual objects with inconsistent temporal presentation (or jitter).&#160; 3. Visual motion via a radial field of coherent central dot motion without jitter.&#160; 4. Visual motion via a radial field of coherent central dot motion with jitter.&#160; These four paradigms are presented in a pseudo-randomized order for each participant.&#160; Jitter is introduced in order to view how possible anticipatory-related effects may affect the morphology of the object-onset and motion-onset CVEP response.&#160; EEG data analyses are described in detail, including steps of data exportation from and importation to signal processing platforms, bad channel identification&#160;and removal, artifact rejection, averaging, and categorization of average CVEP morphological pattern type based upon latency ranges of component peaks. Representative data show that the methodological approach is indeed sensitive in eliciting differential object-onset and motion-onset CVEP morphological patterns and may, therefore, be useful in addressing the larger research aim. Given the high temporal resolution of EEG and the possible application of high-density EEG in source localization analyses, this protocol is ideal for the investigation of distinct CVEP morphological patterns and the underlying neural mechanisms which generate these differential responses.&#160; &#160; &#160;&#160;

In this paper, we present a protocol to investigate differential cortical visual evoked potential morphological patterns through stimulation of ventral and dorsal networks using high-density EEG. Visual object and motion stimulus paradigms, with and without temporal jitter, are described. Visual evoked potential morphological analyses are also outlined.&#160;&#160;

刺激特定的皮质视觉引发潜在形态模式

This paper presents a methodology for the recording and analysis of cortical visual evoked potentials (CVEPs) in response to various visual stimuli using ...

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Non-invasive electrophysiological recordings are useful for the evaluation of nervous system function. These techniques are inexpensive, fast, replicable, and less resource-intensive than imaging. Further, the functional data produced have excellent temporal resolution, which is not achievable with structural imaging.
Current applications of electroencephalograms (EEG) are limited by data processing methods. Standard analysis techniques using raw time series data at individual channels are very limited methods of interrogating nervous system activity. More detailed information about cortical function can be achieved by examining relationships between channels and deriving statistical models of how areas are interacting, allowing visualization of connectivity between networks.
This manuscript describes a method for deriving statistical models of cortical network activity by recording EEG in a standard manner, then examining the interelectrode coherence measures to assess relationships between the recorded areas. Higher order interactions can be further examined by assessing the covariance between the coherence pairs, producing high-dimensional &#34;maps&#34; of network interactions. These data constructs can be examined to assess cortical network function and its relationship to pathology in ways not achievable with traditional techniques.
This approach offers greater sensitivity to network level interactions than is achievable with raw time series analysis. It is, however, limited by the complexity of drawing specific mechanistic conclusions about the underlying neural populations and the high volumes of data generated, requiring more advanced statistical techniques for evaluation, including dimensionality reduction and classifier-based approaches.

Standard EEG analysis techniques offer limited insight into nervous system function. Deriving statistical models of cortical connectivity offers far greater ability to investigate underlying network dynamics. Improved functional assessment opens new possibilities for diagnosis, prognostication, and outcome prediction in nervous system diseases.

使用非侵入性脑电图的皮质连通性统计建模

Non-invasive electrophysiological recordings are useful for the evaluation of nervous system function. These techniques are inexpensive, fast, replicable, ...

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain activity. Multimodal methods have sought to combine the high temporal resolution of EEG with the spatial precision of fMRI, but the complexity of this approach is currently in need of improvement. The protocol presented here describes the recently developed spatiotemporal fMRI-constrained EEG source imaging method, which seeks to rectify source biases and improve EEG-fMRI source localization through the dynamic recruitment of fMRI sub-regions. The process begins with the collection of multimodal data from concurrent EEG and fMRI scans, the generation of 3D cortical models, and independent EEG and fMRI processing. The processed fMRI activation maps are then split into multiple priors, according to their location and surrounding area. These are taken as priors in a two-level hierarchical Bayesian algorithm for EEG source localization. For each window of interest (defined by the operator), specific segments of the fMRI activation map will be identified as active to optimize a parameter known as model evidence. These will be used as soft constraints on the identified cortical activity, increasing the specificity of the multimodal imaging method by reducing cross-talk and avoiding erroneous activity in other conditionally active fMRI regions. The method generates cortical maps of activity and time-courses, which may be taken as final results, or used as a basis for further analyses (analyses of correlation, causation, etc.) While the method is somewhat limited by its modalities (it will not find EEG-invisible sources), it is broadly compatible with most major processing software, and is suitable for most neuroimaging studies.

An EEG-fMRI multimodal imaging method, known as the spatiotemporal fMRI-constrained EEG source imaging method, is described here. The presented method employs conditionally-active fMRI sub-maps, or priors, to guide EEG source localization in a manner that improves spatial specificity and limits erroneous results.

高密度的脑电图记录在儿童的皮质来源分析

摘要

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此视频中的章节

婴儿听觉处理和事件相关的脑振荡

调查婴儿和成人使用密集阵脑电图的社会认知（_D脑电图）

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

采用高分辨率脑电图记录脑源成像局灶性癫痫的临床前模型大鼠

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

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

振幅综合脑电图监测仪 (脑功能监护仪) 在新生儿中的应用

刺激特定的皮质视觉引发潜在形态模式

使用非侵入性脑电图的皮质连通性统计建模

动态皮层活动影像的并发脑电图与功能 MRI 记录及积分分析

高密度的脑电图记录在儿童的皮质来源分析

摘要

探索更多视频

此视频中的章节

婴儿听觉处理和事件相关的脑振荡

调查婴儿和成人使用密集阵脑电图的社会认知（ D脑电图）

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

采用高分辨率脑电图记录脑源成像局灶性癫痫的临床前模型大鼠

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

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

振幅综合脑电图监测仪 (脑功能监护仪) 在新生儿中的应用

刺激特定的皮质视觉引发潜在形态模式

使用非侵入性脑电图的皮质连通性统计建模

动态皮层活动影像的并发脑电图与功能 MRI 记录及积分分析

调查婴儿和成人使用密集阵脑电图的社会认知（_D脑电图）

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