Name: concurrent eeg and functional mri recording and integration analysis for dynamic cortical activity imaging
Uploaded: 2018-06-30T12:30:00.000+00:00
Duration: 11 min 28 s
Description: Watch this Scientific Journal Video about Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging at JoVE.com

这项工作得到了 NIH DK082644 和休斯顿大学的部分支持。

这里提出的议定书是根据休斯顿大学和休斯顿卫理公会研究所各机构审查委员会所规定的道德人类研究准则设计和执行的。
1. 同步脑电图/fMRI 记录
<ol><li>获得参加者的知情同意。向参加者解释研究的目的和程序, 以及同步脑电图/fMRI 数据记录过程的重要安全措施。</li>
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在 MRI 扫描室外准备脑电图帽和检查阻抗。
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<li>放置一个适当大小的, 被动的, MRI 兼容的脑电图帽上的主题的头部。根据10–20国际标签系统10定位电极。</li>
		<li>在 EEG 记录软件上, 检查接地阻抗和参考电极。为此, 请单击 "阻抗" 选项卡, 然后在软件用户界面上选择电极类型 (见图 1)。 注意: 这里的确切说明是特定于此处使用的软件 (见材料表), 可能需要适应其他系统。</li>
		<li>对于每一个电极, 使用注射器注入电解质凝胶进入电极, 然后使用棉签来传播凝胶, 以确保皮肤电极接触。<ol>
<li>随着阻抗的降低, 继续使用适当的软件 (根据设置调整阻抗刻度) 来监视阻抗电平 (见图 1)。继续, 直到所有电极达到阻抗水平低于 10 kΩ, 以确保一个高质量的信号。 注: 根据此处列出和使用的材料, 在 MR-环境11中, 具有阻抗级别超过 50 kΩ的任何电极被认为是不安全的。这可能会根据选择的 cap 和 mri 设置的设计而改变, 因此请与设备制造商和 mri 技术专家协商, 以确保实验装置的安全性。</li>
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同时脑电图/fMRI 硬件设置。
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<li>一旦脑电图盖准备完成, 让主题移动到 MR 扫描仪与硬件设置描述在图 2。 注意: 图形的某些细节可能会发生变化, 具体取决于所使用的系统。</li>
		<li>建立实验范式显示。使用位于观察室中的监视器, 在面对 MR 扫描仪前面的玻璃窗口后面 (参见图 2)。使用头部线圈观察镜, 使受试者可以查看显示器屏幕, 而不移动他们的头或眼睛, 而躺下。</li>
		<li>在计算机屏幕上显示一个示例图像, 以确保主题可以轻松地查看屏幕, 并且该范例将正确显示。进行必要的硬件或软件调整。</li>
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实验范式 (见图 3).
	<ol>
<li>指导主体保持静止, 并进行初步 T1-weighted 解剖 MRI 扫描。如果可能的话, 使用从小脑底部到头部顶端的视野, 包括头骨和皮肤。</li>
		<li>开始记录脑电图数据 (见图 4)。</li>
		<li>同时单击相应的按钮开始 MRI 记录, 并启动演示软件感兴趣的范例。检查脑电图数据记录以确保信号质量, 并在需要时记录适当的标记。<ol>
<li>使用此处描述的设置时, 请先单击演示软件中的 "运行", 然后输入主题编号和试用编号。该范式将在确认这些设置时启动。 注: 这里使用的范式包括10项试验, 在这些实验中, 通过视觉刺激诱发情绪驱动的运动反应。每次试验中, 受试者被要求先休息五十年代观看绿色屏幕, 在之后一个不愉快的图片 (对应于惊奇、愤怒或反感的图片) 或不愉快 (对应于幸福或中立的图片) 面对12是十年代提出的. 五张图片从每个类别被提出随机顺序, 并且被要求的对象在辨认面孔是不愉快的时挤压球, 并且保持挤压直到它消失。</li>
			<li>使用梯度召回的回声平面成像 (GR) 序列的 fMRI 记录 (推荐);自定义以适应设备和范例。 注: 所用序列包括: 回波时间 (TE) = 35 毫秒;重复时间 (TR) = 1500 毫秒;切片厚度 = 5 毫米;翻转角度 = 90 °;像素间距: 2.75 毫米 x 2.75 毫米。可能需要使用比范式本身的显示稍长的 MRI 序列, 以确保记录完整的范式而不进行剪辑。</li>
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</ol>2. 结构磁共振数据分析和前向模型生成
<ol><li>应用 Freesurfer 图像分析套件13,14, 对 T1-weighted 解剖 MRI 体积的各种曲面进行完整的分割和重建。 注意: 包含所有分割输出的文件夹将由 Freesurfer 生成。</li>
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根据 (https://martinos.org/mne/dev/manual/appendix/bem_model.html)15提供的说明生成特定于主题的3层边界元方法 (边界元) 几何模型使用可用的图形用户界面 (GUI) 来确保层中没有重叠。
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<li>打开 Freeview 应用程序。单击 "文件" > > "加载面"。导航到 "Freesurfer" 文件夹中的 "主题" 目录。打开 "边界元" 文件夹。打开 "分水岭" 文件夹。加载此处找到的四文件 ("outer_skin_surface"、"outer_skull_surface"、"brain_surface" 和 "inner_skull_surface")。</li>
		<li>移动切片选择滑块, 并在黄色曲面图层中查找重叠。如果发生重叠, 请检查 MRI 数据中的解剖缺陷或错误, 并使用 GUI 绘图工具来澄清图层。<ol>
<li>通过单击 "文件" > > "加载卷", 加载 Freeview 应用程序中的原始 MRI 数据。导航到 "主题" 文件夹并打开 "mri" 文件夹。然后点击 "orig" 目录, 打开在那里找到的结构核磁共振数据 (应该在. mgz 或. nii 格式)。单击 "确定"。</li>
			<li>查看头部的灰度图像。看看大脑周围的灰色和黑色的不同层。确保这些层没有任何缺口或不规则。使用 "拾色器" 工具, 从要改正的图层中选取一个体素。</li>
			<li>切换到 "手绘体素编辑", 并单击以绘制图像。使用此填充 MRI 图像中的任何缺陷。对出现缺陷的所有层和 MRI 切片执行校正。 注意: "Polywire" 和 "Livewire" 体素编辑工具也可以用来代替 "手绘"。</li>
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	<li>基于脑膜曲面的几何生成源空间。</li>
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使用 Freesurfer 头模型叠加 (图 5) 对 MRI 空间执行特定于主题的脑电图传感器对齐 (例如, 平移和旋转)。保存转换。
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<li>打开 MNE_analyze 应用程序。点击 "文件" > > "加载面"。导航到包含主题数据的文件夹并加载脑膜曲面。</li>
		<li>单击 "文件" > ">" 加载数字化仪数据并选择感兴趣的 EEG 文件 (应包含数字化仪数据)。单击 "查看" > > "显示查看器"。出现查看器 GUI 后, 单击 "选项", 确保选择 "头皮" 和 "数字化仪数据" 选项。这里的电极以红色显示, 基准点为黄色。</li>
		<li>在主窗口 (而不是查看器) 上, 选择 "调整" > > "坐标对齐"。使用 "坐标对齐 GUI", 使用箭头和 L/R 按钮在查看器中移位和旋转脑电图电极。调整尽可能多的必要。完成对齐后, 单击 "坐标对齐 GUI" 底部的 "保存..." 以保存对齐方式。 注意: 通常, 在整个头皮上均匀分布的电极需要良好的基准对准。</li>
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	<li>通过提供特定于主题的边界元模型、源空间和基于15的脑电信号传感器转换, 生成前向模型。</li>
</ol>3. 功能磁共振成像数据分析
<ol><li>使用一般线性模型 (GLM) 方法执行一级 (个别主题) fMRI 统计分析, 获取感兴趣任务的粗体激活图。使用 Freesurfer 组分析管道中内置的基于群集的方法, 为必要的多项比较更正16。</li>
	<li>对所有主题进行组级分析 (如果需要), 以获取标准空间中所有主题 (MNI 或 Talairach) 的粗体激活映射。 注: 牛津大学大脑功能磁共振成像中心 (FMRIB) 软件库 (FSL)17和功能 Neuroimages (AFNI)18软件包分析两种方法都允许分析在同一表面产生的 fMRI 数据。Freesurfer, 使其便于后续分析。</li>
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使用 tksurfer 可视化工具通过加载 fMRI 激活映射 (个人级别和组级别) 来执行感兴趣区域 (ROI) 标识, 并设置所需的罗斯福更正阈值19 (p < 0.05 在这里使用). 注意: 从单个级别的激活映射中识别出来的 ROIs 将作为后续源定位的特定于 fMRI 的空间先验。<ol>
<li>利用 fMRI 激活图在灰物质层, 提取表面补丁使用连接标记算法。 注: 本例中使用了 Dulmage-门德尔松分解。</li>
		<li>根据预定义的脑地图集的标签进一步分叉补丁, 以便覆盖多个区域的任何活动补丁被拆分。 注: 这里使用的地图集是 DKT40 地图集20 (可从 Freesurfer)21。地图集可以根据实验偏好进行专门或选择。</li>
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	<li>将获得的组级 ROIs (当前在标准空间中) 的项目返回到每个主题的各个源空间。在完成单个主体的结构 MRI 分割 (步骤 2.1) 后, 在主题的 "冲浪" 文件夹中, lh.sphere.reg 和 rh.sphere.reg 文件中提供了主题和标准空间之间的坐标转换。Freesurfer 输出文件夹。 注: 所有科目将因此共享同一组 ROIs, 但在他们自己的具体模式。如图6所示的 fMRI 结果和结果 ROIs 的例子。</li>
</ol>4. 脑电图数据分析
注: 本节中的详细信息可能特定于所使用的软件 (有关详细信息, 请参阅材料表)。如果使用不同的软件包, 请参阅相应的文档。
<ol><li>通过模板减法执行扫描仪渐变工件更正。为此, 单击 "特殊信号处理" 菜单中的 "MR 更正" 按钮, 然后在 EEG 分析软件 GUI 中选择适当的参数 (见图 7)。输入适当的参数到选定的扫描仪序列和实验设计。 注意: 主要参数包括: mri 扫描的重复时间 (TR)、扫描类型 (交错或连续)、MRI 体积标记 (或渐变检测方法和渐变触发器)、校正通道和工件模板。</li>
	<li>通过模板减法删除 cardioballistic 工件。为此, 单击 "特殊信号处理" 菜单中的 "CB 更正" 按钮, 并在分析软件 GUI 中选择适当的参数。 注: 此处所需参数包括最小和最大心率、工件模板、心电图通道、模板相关性以及校正通道。</li>
	<li>应用过滤。在 "数据过滤" 下, 选择分析 GUI 顶部的 IIR 过滤按钮。例如, 应用高通在 0.05 hz, 低通在40赫兹和一个凹槽过滤器在电源线频率 (60 Hz), 与一滚出48分贝/赫兹。 注: 在40赫兹的截止频率下应用低通滤波器时, 60 赫兹凹槽过滤器不是严格必要的, 但它被用作防止任何残余的电力线频率, 因为在过滤器边缘上的滚出可能会幸存下来。</li>
	<li>执行眼部工件校正, 在分析 GUI 的顶部: 选择 "转换" > > "工件拒绝/减少" > > "眼部矫正 ICA"。</li>
	<li>根据指定的前、后刺激时间, 将 EEG 数据分割成世纪, 并对事件计时标记进行细分。为此, 选择 "转换" > > "段分析函数" > > "分割", 然后选择感兴趣的标记和感兴趣的时间段。 注意: 分割长度应选择适合的范式和预期的大脑活动的兴趣。</li>
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执行手动或半自动工件拒绝: 选择 "转换" > > "工件拒绝/还原" > > "工件拒绝"。出现提示时, 在 gui 的三选项卡中定义工件的条件, 并按照 gui 的指示进行操作。
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<li>在 "检查方法" 选项卡中, 选择 "自动"、"半自动" 或 "手动选择工件" (建议使用半自动模式)。然后选择 "标记" 或 "删除工件", 并指定更正是否用于单个通道。</li>
		<li>在 "通道选择" 选项卡中, 选择将为工件更正的通道。</li>
		<li>在 "条件" 选项卡中, 选择将识别工件的依据。在这里做选择, 以适应实验需要。选择条件后单击 "确定", 工件将根据选择进行识别和/或拒绝。</li>
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执行基线校正和试平均 (如果适用)。
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<li>要执行基线更正: 选择 "转换" > > "细分分析函数" > > "基线校正"。平均分段数据: 选择 "转换" > > "细分分析函数" > > "平均值"。</li>
	</ol>
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</ol>5. 时空 fMRI 约束-如源成像
<ol><li>定义窗口大小和窗口重叠大小 (默认设置要求40毫秒窗口大小与 50% (20 毫秒) 重叠)。</li>
	<li>选择特定于主题的 ROIs 集 (在步骤3中获得) 作为空间先验集。对于每个 EEG 段, 算法将估计一组权重的空间先验子集最大化的模型证据, 并据此计算源协方差矩阵。</li>
	<li>利用产生的源协方差矩阵, 对所分析的脑电图段进行源定位, 产生源电流密度结果。</li>
	<li>对所有 EEG 段执行步骤5.2 和 5.3, 如有必要, 将所有时间段的当前密度结果汇总为一个完整的电流密度时间-路线, 方法是平均重叠部分。 注意: 这一步将导致在步骤2.3 中定义的每个源点上有一个电流密度时间--皮质活动的过程 (这个数字通常是以成千上万的顺序排列) (图 8)。</li>
	<li>
提取代表电流密度时间-路线在每个 ROIs。
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<li>从 ROI 中的多个源点到单个信号时间段中, 选择用于汇总时间路线的首选方法: 平均值、第一 eigenvariate等.
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	</ol>
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	<li>对所有主题重复步骤5.1 到5.5。</li>
</ol>

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II: Inflation, flattening, and a surface-based coordinate system.</a> Neuroimage. 9 (2), 195-207 (1999).</li><li>Gramfort, A., 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=MNE+software+for+processing+MEG+and+EEG+data.">MNE software for processing MEG and EEG data.</a> Neuroimage. 86, 446-460 (2014).</li><li>Hagler, D. J., Saygin, A. P., Sereno, M. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Smoothing+and+cluster+thresholding+for+cortical+surface-based+group+analysis+of+fMRI+data.">Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data.</a> Neuroimage. 33 (4), 1093-1103 (2006).</li><li>Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., Smith, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fsl.">Fsl.</a> Neuroimage. 62 (2), 782-790 (2012).</li><li>Cox, R. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AFNI:+software+for+analysis+and+visualization+of+functional+magnetic+resonance+neuroimages.">AFNI: software for analysis and visualization of functional magnetic resonance neuroimages.</a> Comput Biomed Res. 29 (3), 162-173 (1996).</li><li>Genovese, C. R., Lazar, N. A., Nichols, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Thresholding+of+statistical+maps+in+functional+neuroimaging+using+the+false+discovery+rate.">Thresholding of statistical maps in functional neuroimaging using the false discovery rate.</a> Neuroimage. 15 (4), 870-878 (2002).</li><li>Klein, A., Tourville, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=101+labeled+brain+images+and+a+consistent+human+cortical+labeling+protocol.">101 labeled brain images and a consistent human cortical labeling protocol.</a> Front Neurosci. 6, 171 (2012).</li><li>Fischl, B., 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=Automatically+parcellating+the+human+cerebral+cortex.">Automatically parcellating the human cerebral cortex.</a> Cereb Cortex. 14 (1), 11-22 (2004).</li><li>Delorme, A., Makeig, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEGLAB:+an+open+source+toolbox+for+analysis+of+single-trial+EEG+dynamics+including+independent+component+analysis.">EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.</a> J Neurosci Methods. 134 (1), 9-21 (2004).</li><li>Iannetti, G. 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=Simultaneous+recording+of+laser-evoked+brain+potentials+and+continuous,+high-field+functional+magnetic+resonance+imaging+in+humans.">Simultaneous recording of laser-evoked brain potentials and continuous, high-field functional magnetic resonance imaging in humans.</a> Neuroimage. 28 (3), 708-719 (2005).</li><li>Niazy, R. K., Beckmann, C. F., Iannetti, G. D., Brady, J. M., Smith, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Removal+of+FMRI+environment+artifacts+from+EEG+data+using+optimal+basis+sets.">Removal of FMRI environment artifacts from EEG data using optimal basis sets.</a> Neuroimage. 28 (3), 720-737 (2005).</li></ol>

作者没有什么可透露的。

我们在这里展示了使用时空 fmri 约束源分析方法进行脑电图/fMRI 综合分析的必要步骤。脑电图和 fMRI 已经成为无创性影像脑活动的基本方法, 虽然他们在各自的空间和时间的分辨率面临困难。虽然已开发方法来充分利用各方面的有利特性, 但目前的 fmri 约束脑电图源定位方法往往依赖于简单的 fmri 约束, 这可能受到偏见和串扰, 限制空间的准确性 (例如, 如果真实的活动发生在 mri 图之外, 静态...

脑电图 (EEG) 和功能性磁共振成像 (fMRI) 可以被视为具有互补功能的神经影像学模式。FMRI 捕捉大的时间尺度的大脑活动, 因为血流动力学信号间接测量的基础神经元活动与一个糟糕的时间分辨率 (在秒的顺序)1,2。与此相反, EEG 直接测量大脑的动态电生理活动, 具有很高的时态分辨率 (毫秒级), 但空间分辨率为3,4。这些特性导致了多模式方法, 旨在优化每个方法5的有利方面。同时使用脑电图和 fmri, 使脑电图的最佳时间分辨率与高功能磁共振成像的空间准确性相结合, 以克服与单一功能磁共振成像或脑电图相关的局限性。
脑电图和 fmri 集成的方法始于 fmri 消息灵通的脑电图源定位6,7。该技术利用 fmri 衍生的空间信息来改善 EEG 源定位, 但其中一个缺点是功能磁共振成像作为 "硬约束"-fmri 源空间信息所引起的潜在空间偏差, 被认为是绝对真理。这构成了两个必须调和6-8的大问题。首先, 必须考虑的是, 使用静态的血氧水平依赖性 (大胆) 对比图可能会无意中加强任何属于它的错误活动, 而在它之外的真正活动的阻尼。第二, 在大胆的激活图之外发生的来自源的串扰可能会影响结果中真实活动的呈现或导致错误的活动。尽管如此, 利用高空间分辨率的 fMRI 提供先验空间知识仍然是一个有利的解决方案5, 因为脑电图反问题的建模可以被限制在解剖和功能感官。
在本文中, 我们演示了一种时空 fmri 约束脑电图源成像方法, 解决了脑电图与 fmri 的时间不匹配问题, 通过计算基于层次贝叶斯模型9的 fMRI 先验的最佳子集。fmri-先验是以数据驱动的方式计算的, 从特定的窗口感兴趣的脑电图数据, 导致时变的 fMRI 限制。该方法利用 EEG 的高时间分辨率来计算皮层活动的电流密度图, 并通过 fMRI 的高空间分辨率在时变、空间选择的情况下准确地预测动态神经活动。

<table><tbody><tr><td>BrainAmp MR Plus</td><td>Brain Products</td><td></td><td>Amplifiers for EEG recording, MR-compatible</td></tr><tr><td>BrainAmp ExG MR&amp;nbsp;</td><td>Brain Products</td><td></td><td>Amplifier for auxilary sensor (EMG), MR-compatible</td></tr><tr><td>BrainAmp Power Pack</td><td>Brain Products</td><td></td><td>Provide power to amplifiers in the MR environment</td></tr><tr><td>Ribbon Cables</td><td>Brain Products</td><td></td><td>Connects the Power Pack to Amplifiers</td></tr><tr><td>SyncBox</td><td>Brain Products</td><td></td><td>Synchronize MR scanner clock with EEG amplifier clock</td></tr><tr><td>BrainCap MR</td><td>Brain Products</td><td></td><td>Passive-electrode 64-channel EEG cap, MR-compatible</td></tr><tr><td>BrainVision Recorder</td><td>Brain Products</td><td></td><td>EEG data recording software (steps 1.2-1.4.2)</td></tr><tr><td>BrainVision Analyzer 2.0</td><td>Brain Products</td><td></td><td>EEG analysis software (steps 4.1-4.6)</td></tr><tr><td>USB 2 Adapter (also known as BUA)</td><td>Brain Products</td><td></td><td>Interface between the amplifiers and data acquisition computer</td></tr><tr><td>Fiber Optic Cables</td><td>Brain Products</td><td></td><td>Connects the EEG cap in the MR scanner to the Recording Computer</td></tr><tr><td>SyncBox Scanner Interface</td><td>Brain Products</td><td></td><td>Synchronize MR scanner clock with EEG amplifier clock</td></tr><tr><td>Trigger Cable</td><td>Brain Products</td><td></td><td>Used to send scanner/paradigm triggers to the recording computer</td></tr><tr><td>ABRALYT HiCl EEG Electrode Gel</td><td>EasyCap</td><td></td><td>Abrasive EEG gel for passive electrode in MR environment</td></tr><tr><td>Ingenia 3.0T MR system</td><td>Philips</td><td></td><td>3.0 T MRI system</td></tr><tr><td>Patriot Digitizer</td><td>Polhemus</td><td></td><td>EEG channel location digitization&amp;nbsp;</td></tr><tr><td>MATLAB r2014a</td><td>MathWorks</td><td></td><td>Programming base for the DBTN algorithm (steps 3.3-3.4 and 5.1-5.7)</td></tr><tr><td>Pictures of Facial Affect</td><td>Paul Eckman Group</td><td></td><td>A series of emotionally valent faces used as stimuli</td></tr><tr><td>E-Prime 2.0</td><td>Psychology Software Tools, Inc</td><td></td><td>Presentation Software (step 1.4.3)</td></tr><tr><td>Bipolar skin EMG electrode</td><td>Brain Products</td><td></td><td>Used to detect muscle activity.</td></tr><tr><td>POLGUI</td><td></td><td></td><td>MATLAB software for digitization</td></tr><tr><td>Freesurfer</td><td></td><td></td><td>Software used in steps 2.1-2.4, and steps 3.1-3.2</td></tr><tr><td>MNE</td><td></td><td></td><td>Software used in step 2.5</td></tr></tbody></table>

concurrent eeg and functional mri recording and integration analysis for dynamic cortical activity imaging

脑电图 (EEG) 和功能磁共振成像 (fMRI) 是两种基本的无创方法, 以识别大脑活动。多模方法试图将 EEG 的高时间分辨率与 fMRI 的空间精度结合起来, 但这种方法的复杂性目前需要改进。本协议介绍了最近开发的时空 fmri 约束脑电图源成像方法, 旨在纠正源偏差, 通过动态招聘功能磁共振成像的亚区域来改善脑电图-fMRI 源定位。该过程从并发脑电图和 fmri 扫描的多式数据收集、3D 皮层模型的生成以及独立的脑电图和 fmri 处理开始。根据它们的位置和周围区域, 经过处理的 fMRI 激活图将被分成多个先验。这些都被作为先验的两级递归贝叶斯算法的脑电图源定位。对于每个感兴趣的窗口 (由操作者定义), fMRI 激活图的特定部分将被确定为活动, 以优化称为模型证据的参数。这些将被用于对已确定的皮质活动的软约束, 通过减少交叉交谈和避免其他有条件活跃的 fMRI 区域的错误活动, 增加多式成像方法的特异性。该方法生成活动和时间课程的皮质图, 可作为最终结果, 或用作进一步分析的基础 (相关性、因果关系等), 而方法受其方式的限制 (它不会发现EEG-隐形源), 它广泛兼容于大多数主要的处理软件, 适合大多数神经影像学研究。

EEG 源定位的基本层次涉及求解正反问题。生成和解决正向问题所需的组件如图 5C所示。使用主题特定的 T1 图像, 三层-大脑, 头骨和皮肤-被分割和网状。这些层充当了生成边界元模型的输入。同样, 主体的灰物质层从结构 MRI 中分割出来, 用于构造源空间。脑电图传感器的位置是共同注册到头部模型使用了一系列刚性几何变换。在构造时, ?...

Watch this Scientific Journal Video about Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging at JoVE.com

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

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

本文介绍了一种脑电图-fmri 多模态成像方法, 称为时空 fmri 约束脑电图源成像方法。所提出的方法采用有条件的功能磁共振成像子图, 或先验, 以提高空间特异性和限制错误结果的方式引导脑电图源定位。

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain ...

concurrent-eeg-functional-mri-recording-integration-analysis-for

Research

JoVE Journal

Biology

11.6K 次观看.  University of Houston. 本文介绍了一种脑电图-fmri 多模态成像方法, 称为时空 fmri 约束脑电图源成像方法。所提出的方法采用有条件的功能磁共振成像子图, 或先验, 以提高空间特异性和限制错误结果的方式引导脑电图源定位。

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

Functional Mapping with Simultaneous MEG and EEG

We use magnetoencephalography (MEG) and electroencephalography (EEG) to locate and determine the temporal evolution in brain areas involved in the processing of simple sensory stimuli. We will use somatosensory stimuli to locate the hand somatosensory areas, auditory stimuli to locate the auditory cortices, visual stimuli in four quadrants of the visual field to locate the early visual areas. These type of experiments are used for functional mapping in epileptic and brain tumor patients to locate eloquent cortices. In basic neuroscience similar experimental protocols are used to study the orchestration of cortical activity. The acquisition protocol includes quality assurance procedures, subject preparation for the combined MEG/EEG study, and acquisition of evoked-response data with somatosensory, auditory, and visual stimuli. We also demonstrate analysis of the data using the equivalent current dipole model and cortically-constrained minimum-norm estimates. Anatomical MRI data are employed in the analysis for visualization and for deriving boundaries of tissue boundaries for forward modeling and cortical location and orientation constraints for the minimum-norm estimates.

We use magnetoencephalography (MEG) and electroencephalography (EEG) to map brain areas involved in the processing of simple sensory stimuli.

同时脑磁图和脑电图的功能映射

We use magnetoencephalography (MEG) and electroencephalography (EEG) to locate and determine the temporal evolution in brain areas involved in the ...

科研

神经科学

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Simultaneous EEG-fMRI allows the excellent temporal resolution of EEG to be combined with the high spatial accuracy of fMRI. The data from these two modalities can be combined in a number of ways, but all rely on the acquisition of high quality EEG and fMRI data. EEG data acquired during simultaneous fMRI are affected by several artifacts, including the gradient artefact (due to the changing magnetic field gradients required for fMRI), the pulse artefact (linked to the cardiac cycle) and movement artifacts (resulting from movements in the strong magnetic field of the scanner, and muscle activity). Post-processing methods for successfully correcting the gradient and pulse artifacts require a number of criteria to be satisfied during data acquisition. Minimizing head motion during EEG-fMRI is also imperative for limiting the generation of artifacts.
Interactions between the radio frequency (RF) pulses required for MRI and the EEG hardware may occur and can cause heating. This is only a significant risk if safety guidelines are not satisfied. Hardware design and set-up, as well as careful selection of which MR sequences are run with the EEG hardware present must therefore be considered.
The above issues highlight the importance of the choice of the experimental protocol employed when performing a simultaneous EEG-fMRI experiment. Based on previous research we describe an optimal experimental set-up. This provides high quality EEG data during simultaneous fMRI when using commercial EEG and fMRI systems, with safety risks to the subject minimized. We demonstrate this set-up in an EEG-fMRI experiment using a simple visual stimulus. However, much more complex stimuli can be used. Here we show the EEG-fMRI set-up using a Brain Products GmbH (Gilching, Germany) MRplus, 32 channel EEG system in conjunction with a Philips Achieva (Best, Netherlands) 3T MR scanner, although many of the techniques are transferable to other systems.

Simultaneous electroencephalography (EEG) and functional Magnetic Resonance imaging (fMRI) is a powerful neuroimaging tool. However, the inside of an MRI scanner forms a difficult environment for EEG data recording and safety must be considered whenever operating EEG equipment inside a scanner. Here, we present an optimised EEG-fMRI data acquisition protocol.

获得高质量的脑电数据在同步功能磁共振成像的当前最佳实践

Simultaneous  EEG-fMRI allows the excellent temporal resolution of EEG to be combined with the  high spatial accuracy of fMRI. The data  from these two ...

行为学

Simultaneous fMRI and Electrophysiology in the Rodent Brain

To examine the neural basis of the blood oxygenation level dependent (BOLD) magnetic resonance imaging (MRI) signal, we have developed a rodent model in which functional MRI data and in vivo intracortical recording can be performed simultaneously. The combination of MRI and electrical recording is technically challenging because the electrodes used for recording distort the MRI images and the MRI acquisition induces noise in the electrical recording. To minimize the mutual interference of the two modalities, glass microelectrodes were used rather than metal and a noise removal algorithm was implemented for the electrophysiology data. In our studies, two microelectrodes were separately implanted in bilateral primary somatosensory cortices (SI) of the rat and fixed in place. One coronal slice covering the electrode tips was selected for functional MRI. Electrode shafts and fixation positions were not included in the image slice to avoid imaging artifacts. The removed scalp was replaced with toothpaste to reduce susceptibility mismatch and prevent Gibbs ringing artifacts in the images. The artifact structure induced in the electrical recordings by the rapidly-switching magnetic fields during image acquisition was characterized by averaging all cycles of scans for each run. The noise structure during imaging was then subtracted from original recordings. The denoised time courses were then used for further analysis in combination with the fMRI data. As an example, the simultaneous acquisition was used to determine the relationship between spontaneous fMRI BOLD signals and band-limited intracortical electrical activity. Simultaneous fMRI and electrophysiological recording in the rodent will provide a platform for many exciting applications in neuroscience in addition to elucidating the relationship between the fMRI BOLD signal and neuronal activity.

We have developed a method for simultaneous functional magnetic resonance imaging and electrophysiological recording in the rodent brain, providing a platform for the investigation of the relationship between neural activity and the blood oxygenation level dependent (BOLD) MRI signal.

同时在啮齿动物脑功能磁共振成像和电

To examine the neural basis of the blood oxygenation level dependent (BOLD) magnetic resonance imaging (MRI) signal, we have developed a rodent model in ...

Simultaneous Transcranial Alternating Current Stimulation and Functional Magnetic Resonance Imaging

Transcranial alternating current stimulation (tACS) is a promising tool for noninvasive investigation of brain oscillations. TACS employs frequency-specific stimulation of the human brain through current applied to the scalp with surface electrodes. Most current knowledge of the technique is based on behavioral studies; thus, combining the method with brain imaging holds potential to better understand the mechanisms of tACS. Because of electrical and susceptibility artifacts, combining tACS with brain imaging can be challenging, however, one brain imaging technique that is well suited to be applied simultaneously with tACS is functional magnetic resonance imaging (fMRI). In our lab, we have successfully combined tACS with simultaneous fMRI measurements to show that tACS effects are state, current, and frequency dependent, and that modulation of brain activity is not limited to the area directly below the electrodes. This article describes a safe and reliable setup for applying tACS simultaneously with visual task fMRI studies, which can lend to understanding oscillatory brain function as well as the effects of tACS on the brain.

Transcranial alternating current stimulation (tACS) is a promising tool for noninvasive investigation of brain oscillations, though its effects are not completely understood. This article describes a safe and reliable setup for applying tACS simultaneously with functional magnetic resonance imaging, which can increase understanding oscillatory brain function and effects of tACS.

同时经颅交替电流刺激和功能磁共振成像

Transcranial alternating current stimulation (tACS) is a promising tool for noninvasive investigation of brain oscillations. TACS employs ...

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the cortical networks involved in dynamical perceptual and cognitive tasks, such as attending to different sounds in a cocktail party. Many past studies have employed data recorded at the sensor level only, i.e., the magnetic fields or the electric potentials recorded outside and on the scalp, and have usually focused on activity that is time-locked to the stimulus presentation. This type of event-related field / potential analysis is particularly useful when there are only a small number of distinct dipolar patterns that can be isolated and identified in space and time. Alternatively, by utilizing anatomical information, these distinct field patterns can be localized as current sources on the cortex. However, for a more sustained response that may not be time-locked to a specific stimulus (e.g., in preparation for listening to one of the two simultaneously presented spoken digits based on the cued auditory feature) or may be distributed across multiple spatial locations unknown a priori, the recruitment of a distributed cortical network may not be adequately captured by using a limited number of focal sources.
Here, we describe a procedure that employs individual anatomical MRI data to establish a relationship between the sensor information and the dipole activation on the cortex through the use of minimum-norm estimates (MNE). This inverse imaging approach provides us a tool for distributed source analysis. For illustrative purposes, we will describe all procedures using FreeSurfer and MNE software, both freely available. We will summarize the MRI sequences and analysis steps required to produce a forward model that enables us to relate the expected field pattern caused by the dipoles distributed on the cortex onto the M/EEG sensors. Next, we will step through the necessary processes that facilitate us in denoising the sensor data from environmental and physiological contaminants. We will then outline the procedure for combining and mapping MEG/EEG sensor data onto the cortical space, thereby producing a family of time-series of cortical dipole activation on the brain surface (or "brain movies") related to each experimental condition. Finally, we will highlight a few statistical techniques that enable us to make scientific inference across a subject population (i.e., perform group-level analysis) based on a common cortical coordinate space.

We use magneto- and electroencephalography (MEG/EEG), combined with anatomical information captured by magnetic resonance imaging (MRI), to map the dynamics of the cortical network associated with auditory attention.

同时MEG / EEG和解剖约束的最小模估计皮质动态映射：听觉注意示例

Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the ...

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological effects. Specifically, by creating a transient interference with the normal activity of a target region and measuring changes in an electrophysiological signal, we can establish a causal link between the stimulated brain area or network and the electrophysiological signal that we record. If target brain areas are functionally defined with prior fMRI scan, TMS could be used to link the fMRI activations with evoked potentials recorded. However, conducting such experiments presents significant technical challenges given the high amplitude artifacts introduced into the EEG signal by the magnetic pulse, and the difficulty to successfully target areas that were functionally defined by fMRI. Here we describe a methodology for combining these three common tools: TMS, EEG, and fMRI. We explain how to guide the stimulator&#39;s coil to the desired target area using anatomical or functional MRI data, how to record EEG during concurrent TMS, how to design an ERP study suitable for EEG-TMS combination and how to extract reliable ERP from the recorded data. We will provide representative results from a previously published study, in which fMRI-guided TMS was used concurrently with EEG to show that the face-selective N1 and the body-selective N1 component of the ERP are associated with distinct neural networks in extrastriate cortex. This method allows us to combine the high spatial resolution of fMRI with the high temporal resolution of TMS and EEG and therefore obtain a comprehensive understanding of the neural basis of various cognitive processes.

This paper describes a method for collecting and analyzing electroencephalography (EEG) data during concurrent transcranial magnetic stimulation (TMS) guided by activations revealed with functional magnetic resonance imaging (fMRI). A method for TMS artifact removal and extraction of event related potentials is described as well as considerations in paradigm design and experimental setup.

提取脑电数据视觉诱发电位记录在功能磁共振成像引导下经颅磁刺激

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological ...

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Resting-state functional connectivity MRI (rs-fcMRI) is a technique that identifies connectivity between different brain regions based on correlations over time in the blood-oxygenation level dependent signal. rs-fcMRI has been applied extensively to identify abnormalities in brain connectivity in different neurologic and psychiatric diseases. However, the relationship among rs-fcMRI connectivity abnormalities, brain electrophysiology and disease state is unknown, in part because the causal significance of alterations in functional connectivity in disease pathophysiology has not been established. Transcranial Magnetic Stimulation (TMS) is a technique that uses electromagnetic induction to noninvasively produce focal changes in cortical activity. When combined with electroencephalography (EEG), TMS can be used to assess the brain's response to external perturbations. Here we provide a protocol for combining rs-fcMRI, TMS and EEG to assess the physiologic significance of alterations in functional connectivity in patients with neuropsychiatric disease. We provide representative results from a previously published study in which rs-fcMRI was used to identify regions with abnormal connectivity in patients with epilepsy due to a malformation of cortical development, periventricular nodular heterotopia (PNH). Stimulation in patients with epilepsy resulted in abnormal TMS-evoked EEG activity relative to stimulation of the same sites in matched healthy control patients, with an abnormal increase in the late component of the TMS-evoked potential, consistent with cortical hyperexcitability. This abnormality was specific to regions with abnormal resting-state functional connectivity. Electrical source analysis in a subject with previously recorded seizures demonstrated that the origin of the abnormal TMS-evoked activity co-localized with the seizure-onset zone, suggesting the presence of an epileptogenic circuit. These results demonstrate how rs-fcMRI, TMS and EEG can be utilized together to identify and understand the physiological significance of abnormal brain connectivity in human diseases.

Resting-state functional-connectivity MRI has identified abnormalities in patients with a wide range of neuropsychiatric disorders, including epilepsy due to malformations of cortical development. Transcranial Magnetic Stimulation in combination with EEG can demonstrate that patients with epilepsy have cortical hyperexcitability in regions with abnormal connectivity.

评价中的连接有关的大脑兴奋患者癫痫多式联运Imaging-和激励为基础的方法

Resting-state functional connectivity MRI (rs-fcMRI) is a technique that identifies connectivity between different brain regions based on correlations ...

医学

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 ...

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Simultaneous electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) is a unique combined technique that provides synergy in the understanding and localization of seizure onset in epilepsy. However, reported experimental protocols for EEG-fMRI recordings fail to address details about conducting such procedures on epilepsy patients. In addition, these protocols are limited solely to research settings. To fill the gap between patient monitoring in an epilepsy monitoring unit (EMU) and conducting research with an epilepsy patient, we introduce a unique EEG-fMRI recording protocol of epilepsy during the interictal period. The use of an MR conditional electrode set, which can also be used in the EMU for a simultaneous scalp EEG and video recording, allows an easy transition of EEG recordings from the EMU to the scanning room for concurrent EEG-fMRI recordings. Details on the recording procedures using this specific MR conditional electrode set are provided. In addition, the study explains step-by-step EEG processing procedures to remove the imaging artifacts, which can then be used for clinical review. This experimental protocol promotes an amendment to the conventional EEG-fMRI recording for enhanced applicability in both clinical (i.e., EMU) and research settings. Furthermore, this protocol provides the potential to expand this modality to postictal EEG-fMRI recordings in the clinical setting.

This article details simultaneous electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) recording procedures that can be used in both clinical and research settings. EEG processing procedures to remove imaging artifacts for clinical review are also included. This study focuses on the example of epilepsy during the interictal period.

同步脑电图和功能性磁共振成像的设备设置和伪影去除，用于癫痫临床审查

Simultaneous electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) is a unique combined technique that provides synergy in the ...

生物工程

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, ...