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>
	<li>
在 MRI 扫描室外准备脑电图帽和检查阻抗。
	<ol>
<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>
		</ol>
</li>
	</ol>
</li>
	<li>
同时脑电图/fMRI 硬件设置。
	<ol>
<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>
	</ol>
</li>
	<li>
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>
		</ol>
</li>
	</ol>
</li>
</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>
	<li>
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。
	<ol>
<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>
		</ol>
</li>
	</ol>
</li>
	<li>基于脑膜曲面的几何生成源空间。</li>
	<li>
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。
	<ol>
<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>
	</ol>
</li>
	<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>
	<li>
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>
	</ol>
</li>
	<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>
	<li>
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。
	<ol>
<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>
	</ol>
</li>
	<li>
执行基线校正和试平均 (如果适用)。
	<ol>
<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>
</li>
</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。
	<ol>
<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.
</li>
	</ol>
</li>
	<li>对所有主题重复步骤5.1 到5.5。</li>
</ol>

<ol>
	<li>Belanger, M., Allaman, I., Magistretti, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain+energy+metabolism:+focus+on+astrocyte-neuron+metabolic+cooperation.">Brain energy metabolism: focus on astrocyte-neuron metabolic cooperation.</a> Cell Metab. 14 (6), 724-738 (2011).</li><li>Logothetis, N. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+underpinnings+of+the+BOLD+functional+magnetic+resonance+imaging+signal.">The underpinnings of the BOLD functional magnetic resonance imaging signal.</a> J Neurosci. 23 (10), 3963-3971 (2003).</li><li>Michel, C. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG+source+imaging.">EEG source imaging.</a> Clin Neurophysiol. 115 (10), 2195-2222 (2004).</li><li>Ramon, C., Schimpf, P. H., Haueisen, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+head+models+on+EEG+simulations+and+inverse+source+localizations.">Influence of head models on EEG simulations and inverse source localizations.</a> Biomed Eng Online. 5, 10 (2006).</li><li>Mosher, J. C., Spencer, M. E., Leahy, R. M., Lewis, P. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Error+bounds+for+EEG+and+MEG+dipole+source+localization.">Error bounds for EEG and MEG dipole source localization.</a> Electroencephalogr Clin Neurophysiol. 86 (5), 303-321 (1993).</li><li>Hamalainen, M. S., Ilmoniemi, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interpreting+magnetic+fields+of+the+brain:+minimum+norm+estimates.">Interpreting magnetic fields of the brain: minimum norm estimates.</a> Med Biol Eng Comput. 32 (1), 35-42 (1994).</li><li>Pascual-Marqui, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Review+of+methods+for+solving+the+EEG+inverse+problem.">Review of methods for solving the EEG inverse problem.</a> International journal of bioelectromagnetism. 1 (1), 75-86 (1999).</li><li>Rosa, M. J., Daunizeau, J., Friston, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG-fMRI+integration:+a+critical+review+of+biophysical+modeling+and+data+analysis+approaches.">EEG-fMRI integration: a critical review of biophysical modeling and data analysis approaches.</a> J Integr Neurosci. 9 (4), 453-476 (2010).</li><li>Nguyen, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG+Source+Imaging+Guided+by+Spatiotemporal+Specific+fMRI:+Toward+an+Understanding+of+Dynamic+Cognitive+Processes.">EEG Source Imaging Guided by Spatiotemporal Specific fMRI: Toward an Understanding of Dynamic Cognitive Processes.</a> Neural Plast. 2016, 10 (2016).</li><li>Klem, G. H., Luders, H. O., Jasper, H. H., Elger, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+ten-twenty+electrode+system+of+the+International+Federation.+The+International+Federation+of+Clinical+Neurophysiology.">The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology.</a> Electroencephalogr Clin Neurophysiol Suppl. 52, 3-6 (1999).</li><li>Mullinger, K. J., Castellone, P., Bowtell, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Best+current+practice+for+obtaining+high+quality+EEG+data+during+simultaneous+FMRI.">Best current practice for obtaining high quality EEG data during simultaneous FMRI.</a> J Vis Exp. (76), (2013).</li><li>Ekman, P., Friesen, W. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Pictures of Facial Affect. , (1976).</li><li>Dale, A. M., Fischl, B., 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=Cortical+surface-based+analysis.+I.+Segmentation+and+surface+reconstruction.">Cortical surface-based analysis. I. Segmentation and surface reconstruction.</a> Neuroimage. 9 (2), 179-194 (1999).</li><li>Fischl, B., Sereno, M. I., Dale, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortical+surface-based+analysis.+II:+Inflation,+flattening,+and+a+surface-based+coordinate+system.">Cortical surface-based analysis. 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, 静态限制将导致真正的来源被减少, 而假峰值将被观察在附近的 mri 活跃区域。同样, MRI 活动区域中的错误或基于噪声的活动将会增强, 就好像它是真的一样)。利用时空 fmri 约束方法, 在两层层次贝叶斯模型中使用可变的 fmri 约束进行改进。目前的源活动是从脑电图数据的滑动窗口的方式估计。fMRI 激活图首先被划分成多个子映射, 每一个作为一个可能的空间之前的皮质源。这些空间先验的一个子集被选择性地用作约束来解决脑电图反问题。因此, 脑电图和 fMRI 数据被集成在一个空间和世俗特定的方式。这有效地取代了传统的 fMRI 激活图与一组感兴趣的区域, 可以根据来自脑电图数据的证据进行可变应用, 从而导致数据驱动的方法, 限制偏差和错误。
这里提出的方法是基于可用的方法 (Freesurfer, FSL等), 并产生皮层模型和处理脑电图和 fMRI 数据。虽然这里提到的一些程序确实使用了特定的软件, 但大多数这些程序都是在 GNU 许可下免费提供的。对此的例外是 BrainVision 分析器, 虽然也可以使用不同的方法 (特别是 EEGLAB22与 EEGLAB 的 FMRIB 插件, 由 FMRIB23、24) 提供。同样, 时空 fmri 约束脑电图源成像方法利用一个相对简单的数据结构, 为其 fMRI 先验和地图集, 允许他们从许多来源, 包括其他成像套件, 或用户定义的来源导入.这方面的唯一限制是将所需的布局与适当指定顶点的主题模型相匹配。
上面描述的一般处理参数概述了这些实验中通常使用的方法。值得注意的是, 对于这些参数的选择没有严重的技术限制-数据过滤和调整方法可以添加或删除管道, 以适应任何实验。更重要的是窗口大小的选择, 因为这直接影响到模型证据的计算和随后应用的 fMRI 先验。虽然窗口大小的变化从近似 40–150 ms 导致仅轻微的变动在被形成的波形, 延伸在这之外不构成风险对稳定并且可能导致某些区域是共同活跃或被掩盖的不适当地。更具体地说, 更大的窗口大小可能会更有用时, 低频是感兴趣的, 而较小的窗口大小可能是最好的, 当集中在更高的频率振荡。滑动窗口的重叠和移位也应在这里考虑, 因为它对过程的计算复杂度有影响, 并且由于分析所需的资源而可能会变得不受抑制。无论选择的确切参数如何, 以下步骤在过程中被认为是关键的: 1) 获得解剖 MRI 数据和同时脑电图/fMRI 数据;2) 3D 模型生成;3) MRI 资料分析;4) 去除脑电图数据中的 MR 伪影;5) 正反计算;6) ROI 生成;7) 投资回报率先验和源本地化的滑动窗口选择。在此过程中, 我们开发并利用了整个管道和方法, 以取得良好的动态效果。应该指出的是, 许多细节-精确的定位方法, 证据计算, 统计方法, 脑电图和 fMRI 参数等-可以修改, 以适应用户的喜好。
时空 fmri 约束源分析法被认为是脑电图与 fMRI 集成中值得注意的一步, 但受到一定的限制。虽然我们看到重建后的深源的质量有所提高, 但这种方法仍然受到其个人方式的总体限制;如果一个源足够深, 可以有效地对 EEG 视而不见, 它将不会被这种方法捕获。其次, 分析的重点是3D 模型的脑膜表面, 并不会重建任何内部区域, 无论任何 fMRI 确定的血流动力学活动。
利用 EEG 结合分项和有条件地应用 fMRI 先验, 我们已经产生了一个先进的, spatiotemporally 特定的成像算法。实时结果表明, 该算法对深源重构具有较强的重建能力, 且比传统的时不变的 fMRI 约束源成像更容易受到交叉谈话的影响。此外, 该方法基本上是可自定义的, 可以适用于每个应用程序, 或者用作后续分析的基础。这些特性给出了时空 fMRI 约束源分析方法的潜力作为一种独立的能力分析方法, 为今后的研究打下了基础。

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 border="0" cellpadding="0" cellspacing="0" style="width:869px;" width="868">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="40">
			<td height="40" style="height:40px;width:219px;">BrainAmp MR Plus</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td style="width:415px;">Amplifiers for EEG recording, MR-compatible</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">BrainAmp ExG MR&#160;</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Amplifier for auxilary sensor (EMG), MR-compatible</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">BrainAmp Power Pack</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Provide power to amplifiers in the MR environment</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Ribbon Cables</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Connects the Power Pack to Amplifiers</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">SyncBox</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Synchronize MR scanner clock with EEG amplifier clock</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">BrainCap MR</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Passive-electrode 64-channel EEG cap, MR-compatible</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">BrainVision Recorder</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>EEG data recording software (steps 1.2-1.4.2)</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">BrainVision Analyzer 2.0</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>EEG analysis software (steps 4.1-4.6)</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">USB 2 Adapter (also known as BUA)</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Interface between the amplifiers and data acquisition computer</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Fiber Optic Cables</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Connects the EEG cap in the MR scanner to the Recording Computer</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">SyncBox Scanner Interface</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Synchronize MR scanner clock with EEG amplifier clock</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Trigger Cable</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Used to send scanner/paradigm triggers to the recording computer</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">ABRALYT HiCl EEG Electrode Gel</td>
			<td style="width:117px;">EasyCap</td>
			<td style="width:119px;"></td>
			<td>Abrasive EEG gel for passive electrode in MR environment</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Ingenia 3.0T MR system</td>
			<td style="width:117px;">Philips</td>
			<td style="width:119px;"></td>
			<td>3.0 T MRI system</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Patriot Digitizer</td>
			<td style="width:117px;">Polhemus</td>
			<td style="width:119px;"></td>
			<td>EEG channel location digitization&#160;</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">MATLAB r2014a</td>
			<td style="width:117px;">MathWorks</td>
			<td></td>
			<td>Programming base for the DBTN algorithm (steps 3.3-3.4 and 5.1-5.7)</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Pictures of Facial Affect</td>
			<td style="width:117px;">Paul Eckman Group</td>
			<td></td>
			<td>A series of emotionally valent faces used as stimuli</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">E-Prime 2.0</td>
			<td style="width:117px;">Psychology Software Tools, Inc</td>
			<td></td>
			<td>Presentation Software (step 1.4.3)</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Bipolar skin EMG electrode</td>
			<td style="width:117px;">Brain Products</td>
			<td></td>
			<td>Used to detect muscle activity.</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">POLGUI</td>
			<td style="width:117px;"></td>
			<td></td>
			<td>MATLAB software for digitization</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Freesurfer</td>
			<td style="width:117px;"></td>
			<td></td>
			<td>Software used in steps 2.1-2.4, and steps 3.1-3.2</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">MNE</td>
			<td style="width:117px;"></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, 正向模型表示从源空间的任何位置产生的电活动会在头皮上的每个 EEG 传感器位置引起潜在的测量。
fMRI 提供了3D 图像的大脑功能活动, 具有良好的空间分辨率和准确性。传统的 fMRI 分析遵循 GLM 方法来识别大脑素显著激活的某一任务。这一分析的典型结果是 fMRI 激活图: 一张突出活动素的单脑图, 可以投射到灰质表面, 如图 6a所示。我们进一步将获得的活化图划分为子映射, 每一个都充当潜在的空间, 然后在任何特定时间窗口中对脑电图测量的头皮电位进行定位 (图 6B)。图 8代表了上面描述的时空 fMRI 约束源分析的重点示意图。只有适当的部分集的 fMRI 激活图是用来产生脑电图源重建的相应的脑电图数据段, 在指定的窗口大小。随着所有脑电图时间窗的分析, 皮质活动的完全重建是以一种 spatiotemporally 的特定方式实现的, 它缓解了在所有 eeg 时间点应用相同的 fMRI 先验的空间偏差。
我们进一步证明了时空 fMRI 约束源分析方法在应用于视觉/运动激活任务研究9中的成功应用, 其中从视觉输入到马达输出的脑活动序列恢复的时空精度高 (图 9)。虽然对用户选择窗口大小有一定的依赖性, 但重建后的源成像结果通常对适度变化是稳健的, 如图 10所示。为此, 实验者应选择窗口大小, 以最适合其特定的研究 (例如,窗口大小太大可能被证明是错误的快速活动或振荡, 而窗口大小太短可能错过低频信号) (图 10)。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56417/56417fig1.jpg"> 图 1: 头皮脑电图阻抗检查.记录器软件用户界面的截图, 箭头指向协议步骤1.2 中的关键图标。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56417/56417fig1large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56417/56417fig2.jpg"> 图 2: 同步脑电图/fMRI 记录硬件设置示意图-不绘制到刻度.(1) 扫描仪;(2) 参加者佩戴胶体脑电图被动帽;(3) 脑电图放大器和电源包连接到脑电帽;(4) 将放大器连接到 USB 2 适配器 (也称为 BUA) 的光纤电缆;(5) BUA, 放大器与记录计算机之间的接口;(6) 数据采集计算机;(7) 范例演示计算机, 配有快速卡输出事件定时标记;(8) 晶体管晶体管逻辑 (TTL) 触发电缆, 将事件定时标记从表示计算机和 MR 扫描仪硬件传送到 BUA;(9) MR 扫描仪硬件在开始时提供定时标记 (10) 新的 fMRI 切片/体积采集和 (11) 时钟同步信号;(12) 时钟同步装置, 提供 EEG 放大器时钟和 MR 扫描仪时钟之间的同步;(13) 接口模块, 在 MR 扫描仪与时钟同步装置之间进行连接;(14) 监控实验范式的视觉显示;(15) 从控制室查看扫描仪室的玻璃窗口。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56417/56417fig2large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56417/56417fig3.jpg"> 图 3: 实验范式.这个主题被显示了一系列视觉刺激, 属于两类之一: 愉悦面孔和不愉快的面孔12。在每次试验中, 第一次显示五十年代绿屏基线, 紧接着是随机选择的十年代视觉刺激。主题是用他/她的右手在整个刺激过程中挤出一个橡皮球, 如果图像被认为是不愉快的面孔。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56417/56417fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/56417/56417fig4.jpg"> 图 4: 脑电图数据记录截图.记录过程中脑电图数据的一个代表部分。(A)脑电图数据的周期与 fMRI 脉冲序列的效果, MR 扫描仪工件发音。(B)没有 fMRI 脉冲序列的脑电图数据周期, 没有明显的 MR 扫描仪工件可见。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56417/56417fig4large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/56417/56417fig5.jpg"> 图 5: 正向模型生成。(A)将脑电图电极对准头部模型空间。红色和蓝色圆圈代表数字化脑电图传感器位置, 黄色圆圈代表数字化脑电图基准点: nasion, 左耳, 右耳。(B)传感器对准过程的选项, 包括手动转换, 如脑电图传感器空间的平移和旋转 (协议步骤 2.4)。(C)所产生的特定的边界元模型, 包括3个舱室: (3) 脑, (4) 颅骨, (5) 皮肤。(1) 灰物质层表面的分布源空间。(2) 脑电图传感器位置在模型上对齐。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56417/56417fig5large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/56417/56417fig6.jpg"> 图 6: fMRI 激活图和感兴趣区域的提取。(A)在充气表面显示的 fMRI 活化图便于检查。红色和黄色编码的区域显着激活 (p校正 < 0.05)。(B)从 fMRI 激活图中提取的感兴趣的8个代表性区域。注意到以阿特拉斯为基础的马达活动分离为3先验。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56417/56417fig6large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/56417/56417fig7.jpg"> 图 7: 分析器软件用户界面-删除 MR 扫描仪工件。(a)在扫描仪工件校正之前: (a) 脑电图数据段在磁共振成像脉冲序列开始之前;(b) 脑电图数据段在 fMRI 脉冲序列的作用下, 扫描仪工件清晰可见;(c) (b) 数据科的频率内容 (FFT);(d) 分析器软件的内置分析模块, 用于扫描仪渐变工件校正和 cardioballistic 工件校正。(B)扫描仪工件更正后: (a) 删除 MR 扫描仪工件后的脑电图数据段;(b) (a) 数据科的频率内容 (FFT)。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56417/56417fig7large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/56417/56417fig8.jpg"> 图 8: 分析过程的总体示意图.(A) 脑电图数据处理和窗口大小选择。(B) fMRI 数据分析, 然后抽取感兴趣的区域作为源分析的空间先验资料。(C)在每个脑电图段进行的源分析, 由窗口大小和重叠百分比指定。(D)在感兴趣的时间段内完成重建的皮质活动。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56417/56417fig8large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/56417/56417fig9.jpg"> 图 9: 重建的一个有代表性的主体的皮质活动经历了视觉/运动激活范式.源重建的结果从对比两种方法: 时空 fmri 约束 (上) 和时间不变的 fmri 约束源成像 (下)。图转载, 并获得参考文献9的许可。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56417/56417fig9large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/56417/56417fig10.jpg"> 图 10: 重建活动时间-路线在扣带皮层使用不同的窗口大小。(a)活动时间-用较小的窗口大小重建的课程显示了非常相似的结果 (相关 R > 0.95)。(b)使用较大的窗口尺寸导致高差距 (R < 0.7)。图转载, 并获得参考文献9的许可。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56417/56417fig10large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>

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

Nanyang Technological University

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

Application of Light-cured Dental Adhesive Resin for Mounting Electrodes or Microdialysis Probes in Chronic Experiments

In chronic recording experiments, self-curing dental acrylic resins have been used as a mounting base of electrodes or microdialysis-probes. Since these acrylics do not bond to the bone, screws have been used as anchors. However, in small experimental animals like finches or mouse, their craniums are very fragile and can not successfully hold the anchors. In this report, we propose a new application of light-curing dental resins for mounting base of electrodes or microdialysis probes in chronic experiments. This material allows direct bonding to the cranium. Therefore, anchor screws are not required and surgical field can be reduced considerably. Past experiences show that the bonding effect maintains more than 2 months. Conventional resin's window of time when the materials are pliable and workable is a few minutes. However, the window of working time for these dental adhesives is significantly wider and adjustable.

In this report, we propose a new application of light-curing dental resins for mounting base of electrodes or microdialysis probes in chronic experiments. This material allows direct bonding to the cranium.

光固化牙科粘合树脂安装电极或微透析探针在慢性实验中的应用

In chronic recording experiments, self-curing dental acrylic resins have been used as a mounting base of electrodes or microdialysis-probes. Since these ...

科研

生物学

Labeling hESCs and hMSCs with Iron Oxide Nanoparticles for Non-Invasive in vivo Tracking with MR Imaging

In recent years, stem cell research has led to a better understanding of developmental biology, various diseases and its potential impact on regenerative medicine. A non-invasive method to monitor the transplanted stem cells repeatedly in vivo would greatly enhance our ability to understand the mechanisms that control stem cell death and identify trophic factors and signaling pathways that improve stem cell engraftment. MR imaging has been proven to be an effective tool for the in vivo depiction of stem cells with near microscopic anatomical resolution. In order to detect stem cells with MR, the cells have to be labeled with cell specific MR contrast agents. For this purpose, iron oxide nanoparticles, such as superparamagnetic iron oxide particles (SPIO), are applied, because of their high sensitivity for cell detection and their excellent biocompatibility. SPIO particles are composed of an iron oxide core and a dextran, carboxydextran or starch coat, and function by creating local field inhomogeneities, that cause a decreased signal on T2-weighted MR images. This presentation will demonstrate techniques for labeling of stem cells with clinically applicable MR contrast agents for subsequent non-invasive in vivo tracking of the labeled cells with MR imaging.

For the evaluation of new stem cell therapies it is important to non-invasively track the injected cells in vivo. This video will show you how to label human mesenchymal and embryonic stem cells with iron oxide based contrast agents in vivo for subsequent MR imaging in vivo.

氧化铁标记的胚胎干细胞和的hMSCs纳米粒子在体内的跟踪与非侵入性磁共振成像

In recent years, stem cell research has led to a better understanding of developmental biology, various diseases and its potential impact on regenerative ...

Labeling Stem Cells with Fluorescent Dyes for non-invasive Detection with Optical Imaging

Optical imaging (OI) is an easy, fast and inexpensive tool for in vivo monitoring of new stem cell based therapies. The technique is based on ex vivo labeling of stem cells with a fluorescent dye, subsequent intravenous injection of the labeled cells and visualization of their accumulation in specific target organs or pathologies. The presented technique demonstrates how we label human mesenchymal stem cells (hMSC) by simple incubation with the lipophilic fluorescent dye DiD (C67H103CIN2O3S) and how we label human embryonic stem cells (hESC) with the FDA approved fluorescent dye Indocyanine Green (ICG). The uptake mechanism is via adherence and diffusion of the lypophilic dye across the phospholipid cell membrane bilayer. The labeling efficiency is usually improved if the cells are incubated with the dye in serum-free media as opposed to incubation in serum-containing media. Furthermore, the addition of the transfection agent Protamine Sulfate significantly improves contrast agent uptake.

This video shows techniques for labeling of human embryonic stem cells and mesenchymal stem cells with fluorescent dyes. This technique can be used for an in vivo tracking of transplanted stem cells with optical imaging and for histopathological correlations with fluorescence microscopy.

与非侵入性检测荧光染料标记的干细胞与光学成像

Optical imaging (OI) is an easy, fast and inexpensive tool for in vivo monitoring of new stem cell based therapies. The technique is based on ex vivo ...

Functional Imaging with Reinforcement, Eyetracking, and Physiological Monitoring

We use functional brain imaging (fMRI) to study neural circuits that underlie decision-making.  To understand how outcomes affect decision processes, simple perceptual tasks are combined with appetitive and aversive reinforcement.  However, the use of reinforcers such as juice and airpuffs can create challenges for fMRI.  Reinforcer delivery can cause head movement, which creates artifacts in the fMRI signal.  Reinforcement can also lead to changes in heart rate and respiration that are mediated by autonomic pathways.  Changes in heart rate and respiration can directly affect the fMRI (BOLD) signal in the brain and can be confounded with signal changes that are due to neural activity.  In this presentation, we demonstrate methods for administering reinforcers in a controlled manner, for stabilizing the head, and for measuring pulse and respiration.

This presentation demonstrates the use of fMRI to study neural circuits that underlie decision-making. Simple perceptual tasks are combined with appetitive and aversive reinforcements to investigate how outcomes affect decision processes.

加固，Eyetracking，生理监测的功能成像

We use functional brain imaging (fMRI) to study neural circuits that underlie decision-making.  To understand how outcomes affect decision processes, ...

Dissection of Hippocampal Dentate Gyrus from Adult Mouse

The hippocampus is one of the most widely studied areas in the brain because of its important functional role in memory processing and learning, its remarkable neuronal cell plasticity, and its involvement in epilepsy, neurodegenerative diseases, and psychiatric disorders. The hippocampus is composed of distinct regions; the dentate gyrus, which comprises mainly granule neurons, and Ammon's horn, which comprises mainly pyramidal neurons, and the two regions are connected by both anatomic and functional circuits. Many different mRNAs and proteins are selectively expressed in the dentate gyrus, and the dentate gyrus is a site of adult neurogenesis; that is, new neurons are continually generated in the adult dentate gyrus. To investigate mRNA and protein expression specific to the dentate gyrus, laser capture microdissection is often used. This method has some limitations, however, such as the need for special apparatuses and complicated handling procedures. In this video-recorded protocol, we demonstrate a dissection technique for removing the dentate gyrus from adult mouse under a stereomicroscope. Dentate gyrus samples prepared using this technique are suitable for any assay, including transcriptomic, proteomic, and cell biology analyses. We confirmed that the dissected tissue is dentate gyrus by conducting real-time PCR of dentate gyrus-specific genes, tryptophan 2,3-dioxygenase (TDO2) and desmoplakin (Dsp), and Ammon's horn enriched genes, Meis-related gene 1b (Mrg1b) and TYRO3 protein tyrosine kinase 3 (Tyro3). The mRNA expressions of TDO2 and Dsp in the dentate gyrus samples were detected at obviously higher levels, whereas Mrg1b and Tyro3 were lower levels, than those in the Ammon's horn samples. To demonstrate the advantage of this method, we performed DNA microarray analysis using samples of whole hippocampus and dentate gyrus. The mRNA expression of TDO2 and Dsp, which are expressed selectively in the dentate gyrus, in the whole hippocampus of alpha-CaMKII+/- mice, exhibited 0.037 and 0.10-fold changes compared to that of wild-type mice, respectively. In the isolated dentate gyrus, however, these expressions exhibited 0.011 and 0.021-fold changes compared to that of wild-type mice, demonstrating that gene expression changes in dentate gyrus can be detected with greater sensitivity. Taken together, this convenient and accurate dissection technique can be reliably used for studies focused on the dentate gyrus.

A dissection technique for removal of the dentate gyrus from adult mouse under a stereomicroscope was demonstrated in this video-recorded protocol.

从成年小鼠海马齿状回的剖析

The hippocampus is one of the most widely studied areas in the brain because of its important functional role in memory processing and learning, its ...

Robotics and Dynamic Image Analysis for Studies of Gene Expression in Plant Tissues

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods presented here utilize the green fluorescent protein (gfp) gene for continual monitoring of gene expression in the same pieces of tissues, over time. The gfp gene was placed under regulatory control of different promoters and introduced into lima bean cotyledonary tissues via particle bombardment. Cotyledons were then placed on a robotic image collection system, which consisted of a fluorescence dissecting microscope with a digital camera and a 2-dimensional robotics platform custom-designed to allow secure attachment of culture dishes. Images were collected from cotyledonary tissues every hour for 100 hours to generate expression profiles for each promoter. Each collected series of 100 images was first subjected to manual image alignment using ImageReady to make certain that GFP-expressing foci were consistently retained within selected fields of analysis. Specific regions of the series measuring 300 x 400 pixels, were then selected for further analysis to provide GFP Intensity measurements using ImageJ software. Batch images were separated into the red, green and blue channels and GFP-expressing areas were identified using the threshold feature of ImageJ. After subtracting the background fluorescence (subtraction of gray values of non-expressing pixels from every pixel) in the respective red and green channels, GFP intensity was calculated by multiplying the mean grayscale value per pixel by the total number of GFP-expressing pixels in each channel, and then adding those values for both the red and green channels. GFP Intensity values were collected for all 100 time points to yield expression profiles. Variations in GFP expression profiles resulted from differences in factors such as promoter strength, presence of a silencing suppressor, or nature of the promoter. In addition to quantification of GFP intensity, the image series were also used to generate time-lapse animations using ImageReady. Time-lapse animations revealed that the clear majority of cells displayed a relatively rapid increase in GFP expression, followed by a slow decline. Some cells occasionally displayed a sudden loss of fluorescence, which may be associated with rapid cell death. Apparent transport of GFP across the membrane and cell wall to adjacent cells was also observed. Time lapse animations provided additional information that could not otherwise be obtained using GFP Intensity profiles or single time point image collections.

We report a method for introduction, tracking and quantitative analysis of GFP expression in plant cells. This method utilizes a custom-designed robotics system for semi-continuous image collection from large numbers of samples, over time. We also demonstrate the use of ImageJ and ImageReady for analysis of image series.

机器人技术和植物组织的基因表达研究的动态图像分析

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods ...

Mutagenesis and Functional Analysis of Ion Channels Heterologously Expressed in Mammalian Cells

We will demonstrate how to study the functional effects of introducing a point mutation in an ion channel. We study G protein-gated inwardly rectifying potassium (referred to as GIRK) channels, which are important for regulating the excitability of neurons. There are four different mammalian GIRK channel subunits (GIRK1-GIRK4) - we focus on GIRK2 because it forms a homotetramer. Stimulation of different types of G protein-coupled receptors (GPCRs), such as the muscarinic receptor (M2R), leads to activation of GIRK channels. Alcohol also directly activates GIRK channels. We will show how to mutate one amino acid by specifically changing one or more nucleotides in the cDNA for the GIRK channel. This mutated cDNA sequence will be amplified in bacteria, purified, and the presence of the point mutation will be confirmed by DNA sequencing. The cDNAs for the mutated and wild-type GIRK channels will be transfected into human embryonic kidney HEK293T cells cultured in vitro. Lastly, whole-cell patch-clamp electrophysiology will be used to study the macroscopic potassium currents through the ectopically expressed wild-type or mutated GIRK channels. In this experiment, we will examine the effect of a L257W mutation in GIRK2 channels on M2R-dependent and alcohol-dependent activation.

We will demonstrate how to study the effect of a single point mutation on the function of an ion channel.

异源表达在哺乳动物细胞离子通道的突变和功能分析

We will demonstrate how to study the functional effects of introducing a point mutation in an ion channel. We study G protein-gated inwardly rectifying ...

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard.
Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.

Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.

快速分析和荧光显微镜图像的探索

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized ...

Functional Reconstitution and Channel Activity Measurements of Purified Wildtype and Mutant CFTR Protein

The Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a unique channel-forming member of the ATP Binding Cassette (ABC) superfamily of transporters. The phosphorylation and nucleotide dependent chloride channel activity of CFTR has been frequently studied in whole cell systems and as single channels in excised membrane patches. Many Cystic Fibrosis-causing mutations have been shown to alter this activity. While a small number of purification protocols have been published, a fast reconstitution method that retains channel activity and a suitable method for studying population channel activity in a purified system have been lacking. Here rapid methods are described for purification and functional reconstitution of the full-length CFTR protein into proteoliposomes of defined lipid composition that retains activity as a regulated halide channel. This reconstitution method together with a novel flux-based assay of channel activity is a suitable system for studying the population channel properties of wild type CFTR and the disease-causing mutants F508del- and G551D-CFTR. Specifically, the method has utility in studying the direct effects of phosphorylation, nucleotides and small molecules such as potentiators and inhibitors on CFTR channel activity. The methods are also amenable to the study of other membrane channels/transporters for anionic substrates.

Described here is a rapid and effective procedure for functional reconstitution of purified wild-type and mutant CFTR protein that preserves activity for this chloride channel, which is defective in Cystic Fibrosis. Iodide efflux from reconstituted proteoliposomes mediated by CFTR allows studies of channel activity and the effects of small molecules.

纯化野生型和突变CFTR蛋白的功能重建和通道活性测定

The Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a unique channel-forming member of the ATP Binding Cassette (ABC) superfamily of ...

Implementation of a Coherent Anti-Stokes Raman Scattering (CARS) System on a Ti:Sapphire and OPO Laser Based Standard Laser Scanning Microscope

Laser scanning microscopes combining a femtosecond Ti:sapphire laser and an optical parametric oscillator (OPO) to duplicate the laser line have become available for biologists. These systems are primarily designed for multi-channel two-photon fluorescence microscopy. However, without any modification, complementary non-linear optical microscopy such as second-harmonic generation (SHG) or third harmonic generation (THG) can also be performed with this set-up, allowing label-free imaging of structured molecules or aqueous medium-lipid interfaces. These techniques are well suited for in-vivo observation, but are limited in chemical specificity. Chemically selective imaging can be obtained from inherent vibration signals based on Raman scattering. Confocal Raman microscopy provides 3D spatial resolution, but it requires high average power and long acquisition time. To overcome these difficulties, recent advances in laser technology have permitted the development of nonlinear optical vibrational microscopy, in particular coherent anti-Stokes Raman scattering (CARS). CARS microscopy has therefore emerged as a powerful tool for biological and live cell imaging, by chemically mapping lipids (via C-H stretch vibration), water (via O-H stretch vibrations), proteins or DNA. In this work, we describe the implementation of the CARS technique on a standard OPO-coupled multiphoton laser scanning microscope. It is based on the in-time synchronization of the two laser lines by adjusting the length of one of the laser beam path. We present a step-by-step implementation of this technique on an existing multiphoton system. A basic background in experimental optics is helpful and the presented system does not require expensive supplementary equipment. We also illustrate CARS imaging obtained on myelin sheaths of sciatic nerve of rodent, and we show that this imaging can be performed simultaneously with other nonlinear optical imaging, such as standard two-photon fluorescence technique and second-harmonic generation.

Implementation of a Coherent Anti-Stokes Raman Scattering CARS System on a Ti:Sapphire and OPO Laser Based Standard Laser Scanning Microscope

Coherent anti-Stokes Raman scattering (CARS) microscopy based on inherent vibration of molecule bonds permits label-free chemically selective live cell imaging. This work presents the implementation of a complementary microscopy technique on a standard multiphoton laser scanning microscope based on a femtosecond Ti:sapphire laser and an OPO laser.

蓝宝石OPO基于激光的标准激光扫描显微镜:在TI相干反斯托克斯拉曼散射（CARS）系统的实现

Laser scanning microscopes combining a femtosecond Ti:sapphire laser and an optical parametric oscillator (OPO) to duplicate the laser line have become ...