这项工作得到了国立老龄研究所 (R01AG013308 SYB、F31AG047041 至屯门) 的资助。作者使用了与 Hoffman2 共享集群相关的计算和存储服务, 由加州大学洛杉矶分校数字研究和教育研究技术小组提供。

本研究是遵照加州大学洛杉矶分校机构审查委员会 (IRB) 的协议进行的, 并得到加州大学洛杉矶分校人类保护委员会的批准。所有参加者都给予书面知情同意, 以便在本研究中报名. 
 1. 参与者选择 <ol> <li> 获得 IRB 批准以执行该研究。
	<li> 使用标准化的神经心理学电池对55岁及以上的人进行认知衰退的筛选。包括一般智力测试 (子的 WAIS III) 13 , 流利 (水果和蔬菜) 14 , 注意 (数字向前和向后) 13 , 语言 (波士顿命名测试) 15 , 语言内存 (Buschke-富尔德选择性提醒任务) 16 , WMS III 逻辑内存和语言配对学习 13 , 和视觉记忆 (雷伊-Osterrieth 图测试) 17 . <ol> <li> 让参与者完成情绪调查表, 如汉密尔顿凹陷和焦虑清单 18 19 以及微型心理状态检查 () 20 . </li>
	</ol> </li> <li> 包括26或更高分数的参与者, 并在认知测试的年龄低于正常水平的两个标准偏差。排除临床焦虑、抑郁症或其他神经精神或神经系统疾病的参与者。排除不符合 MRI 安全标准或不同意抽取血液的参与者. 
	注: 在本研究中, 93 参与者符合这些标准 (平均年龄 = 67.4 岁, 31 米/49 f). </li>
</ol> 2。基因分型 <ol> <li> 有训练有素的血或其他医疗专业人员从每个参与者那里抽取血液. </li>
	<li> 分离200和 #181; g 基因组 DNA 从10毫升的样本, 如所述 21 . </li>
	<li> 用 real-time PCR 在两个位点, rs429358 和 rs7412 进行单核苷酸多态性 (SNP) 基因分型, 以区分 载脂蛋白 基因 22 。
	<ol> <li> 将 rs429358 和 rs7412 的报告染料合并为 SNP 基因分型检测。在每个 PCR 扩增周期完成后, 在图上绘制荧光信号, 显示报告者和止渴染料的分布情况。重复执行实验以确认结果. </li>
	</ol> </li> <li> 使用为 real-time PCR 过程输出而开发的软件包分析 SNP 基因型数据 23 。
	注: 本研究中使用的程序计算了样品与两个报告染料之一的亲和度, 反过来, 它代表一个 载脂蛋白 SNP。在本研究中, AD 风险等位基因的34载体, 载脂蛋白 和 #949; 4 (杂和 #949; 3/和 #949; 4) 和46比较 (纯合和 #949; 3/和 #949; 3) 报名参加了总共80名研究参与者。排除载脂蛋白和 #949 的载体; 2 等位基因, 因为有证据表明, 此等位基因可能具有与 AD 相关的保护作用. </li>
</ol> 3。功能和结构成像数据收集 <ol> <li> 使用3特斯拉 (3T) MRI 系统获取全脑成像数据。
	<ol> <li> 用于功能成像, 使用回声平面成像 (EPI) 序列收集轴向切片。为了便于功能图像的配准, 获取 T2-weighted 的轴向切片, co-planar 结构图像。对于高分辨率结构成像, 使用 3D T1-weighted 序列收集轴向切片. 
		注: 在目前的研究中, 一个3T 的磁铁使用12通道头线圈。下面的参数是为特定的扫描仪和线圈设计的。有关更多信息, 请参见 材料表 。
		<ol> <li> 使用以下序列参数获取功能成像数据: 重复时间 (TR) = 2500 毫秒, 回波时间 (TE) = 21 毫秒, 视场 (视) = 200 mm x 200 mm, 翻转角度 = 75 和 #176;, 矩阵 = 64 x 64, 33 切片, 切片粗细 = 3 mm, 条间隙 = 0. 75 毫米, 体素大小 = 3.125 x 3.125 x 3.75 mm. </li>
			<li> 触发不相关的单词联想记忆任务开始与功能成像序列的第三卷。要考虑稳态平衡, 将每个功能扫描的前两个卷排除在分析之外. 
			注意: 在其他地方 12 , 24 中描述了不相关的单词关联内存任务。简单来说, 它是一个具有编码和检索块的块设计功能任务。参与者被指示学习对不相关的单词. </li>
			<li> 使用以下序列参数获取 T2-weighted、co-planar 结构成像数据: TR = 5000 毫秒, TE = 34 毫秒, 视 = 200 mm x 200 mm, 翻转角度 = 90 和 #176;, 矩阵 = 128 x 128, 28 切片, 切片粗细 = 3 mm, 条间隙 = 1 mm 和体素大小 = 1.56 x 1. 56 x 4 mm. </li>
			<li> 使用下列磁化制备的快速梯度回波 (MPRAGE) 序列参数获取高分辨率结构 (解剖学) 成像: TR = 1900 毫秒, TE = 2.26 毫秒, TI = 900 毫秒, 视 = 250 mm x 218 mm, 翻转角度 = 9 和 #176;, 矩阵 = 256 x 215, 176 切片, 切片厚度 = 1 毫米, 零填充到 256 x 224 的矩阵, 从而产生一个体素大小 = 1 x 0.976 x 0.976 mm. </li>
		</ol> </li> </ol> </li> </ol> 4. fMRI BOLD 数据预处理 <ol> <li> 使用功能性数据进行预处理 (FMRIB) 软件库 (FSL) 版本 6.0 (http://fsl.fmrib.ox.ac.ul), 如下所示: <ol> <li>对于每个参与者和 #39 的数据集, 使用运动校正 FMRIB 和 #39; s 线性图像配准工具 (MCFLIRT) 25 删除磁头运动工件. </li>
		<li> 使用可选-f 标记的脑提取工具 (BET) 从图像中删除脑组织 26 . </li>
		<li> 使用 FSL 运动离群器工具来标识功能数据中的任何卷, 其中基于卷之间的帧移位的动作过多。将运动测量为异常值的标记卷 (高于 75 th 百分比 + 间距范围的1.5 倍) 与扫描的其余部分相比, 使用此程序的输出在分析中 downweight 这些卷. 
		注意: 在运行组比较之前, 请检查在两个组之间的平均运动 (由 FSL 运动异常值测量) 不存在差异。这将有助于确保调查结果不受与组相关的运动差异的驱动. </li>
	</ol> </li> <li> 为第一个参与者使用 FSL fMRI 专家分析工具 (专长) 的图形用户界面 (GUI) 设置预处理和一级通用线性模型 (GLM)。 注意: 对每个学习参与者重复此步骤。为了节省时间, 在为一个参与者设置了一个运行后, 编写一个脚本, 为其余的研究参与者和 #39 运行预处理; 通过更改 #34;d 设计和 #34 的数据; 文件 (FSL 壮举输出) 为每个参与者引用该参与者和 #39; s特定数据。
	<ol> <li> 在 "数据" 选项卡中, 单击 "#34"; 添加4D 数据和 #34; 并导航到运动矫正和脑提取文件。将 tr 设置为 2.5 s (对应于所获得的函数序列的 tr)。使用默认的高通滤波器 (设置为 100s). 
		注: 高通滤波将去除不感兴趣的低频信号. </li>
		<li> 在 "pre-stats" 选项卡中, 单击 "#34; 无和 #34; #34; 运动矫正和 #34; (正如在步骤4.1 中已经执行的那样)。取消选中和 #34; 投注大脑提取和 #34; (因为它已经在步骤4.1 中完成)。类型和 #34; 5 和 #34; 在框中设置 5 mm 全角 half-maximum (FWHM) 高斯内核, 用于空间平滑. 
		注意: 平滑内核的 FWHM 通常应设置为功能扫描体素大小的两倍. </li>
		<li> 使用 MCFLIRT 的输出 (6 列, 行 = 在扫描中的 "客票") 创建6单列文本文件, 用于描述在数据集中的每个卷上执行的运动更正。这些将在下一步中作为变量添加到模型中。
		<ol> <li> 在 "#34" 下的 "统计" 选项卡中; 完整的模型设置和 #34, 将6运动参数及其时态导数作为变量或解释变量 (EVs) 添加到 GLM 中。为每一个议案 EV 选择和 #34; 自定义和 #34; (每卷1条目) 基本形状、#34; 无 #34; 卷积和检查 #34; 应用时态筛选. 注意: 运动参数不需要积任何函数, 因为它们引用在运动矫正期间, 在每个功能量上进行重新调整, 因此不需要调整. </li>
		</ol> </li> <li> 在 "统计" 选项卡中, 选择 "与 #34 下步骤4.1 中的 FSL 运动异常值的输出; 添加额外的混淆 EVs 和 #34;. 
		注意: 这个输出是一个矩阵, 表示每个卷被标记为过度运动, 并通过添加混淆文件, 将在 GLM 中 deweighted. </li>
		<li> 在 "统计" 选项卡中, 单击和 #34; 完整的模型设置和 #34;。创建任务定时文本文件, 表示不同任务阶段的开始和偏移, 并在 GLM 中添加这些为 EVs, 方法是选择1列格式并导航到相关的文本文件 (包括一个用于任务的编码阶段, 一个用于检索阶段)。#34; 卷积和 #34; 从下拉列表中选择 #34;d 双伽玛 HRF 和 #34; 选项。不要在 GLM 中对任务的基线或非主动部分建模. 
		注: HRF 代表血流动力学反应功能。卷的任务 ev 的 HRF 转移任务 ev 的时间, 以更符合预期任务引起的大胆信号变化的大脑. </li>
		<li> 在 "注册" 选项卡中, 检查和 #34; 扩展功能图像和 #34; 和 #34; 主要结构图像和 #34; two-step 注册。
		<ol> <li> 选择参与者和 #39; co-planar T2-weighted 结构扫描进行第一步, 其中功能数据注册到 co-planar 结构数据。选择6自由度 (自由度) 的这一步, 点击第二个下拉框在这个步骤和选择和 #34; 6 自由度和 #34;. </li>
			<li> 对于下一步, 将 T2-weighted 映像注册到高分辨率 T1-weighted MPRAGE, 请从下拉框中选择 "基于边界的注册 (BBR)" 27 . 
			注: BBR 使用白色物质和灰质之间的强度差异来注册结构和功能扫描, 并显示出比调情和其他替代方法更好的表现. </li>
			<li> 在最后一步中, 将高分辨率结构数据注册到标准 MNI152 模板中, 选择12自由度, 并通过选择和 #34 进行线性转换; 12 #34;. 
			注意: 当4节中的所有步骤都完成后, 功能数据将被预处理并准备好进行进一步的分析. </li>
		</ol> </li> </ol> </li> </ol> 5。海马种子 <ol> <li> 在每个参与者和 #39 中生成左海马的掩码, 使用 FSL 和 #39 的高分辨率结构空间; FMRIB 集成的注册和分割工具 (第一个) 分割算法 28. 
	注意: 其他地区, 包括右海马, 将是有趣和有效的种子, 进一步分析. </li>
	<li> 使用统计软件平台, 编写代码以计算结构的前、后三分之二的长度 29 。具体来说, 使用前后平面内的海马体面具的长度来找到这架飞机前后 demarking 的坐标. 
	注意: 最近发布的沿纵轴细分海马体的方法可能是一种替代的种子创建方法 30 . </li>
	<li> 基于这些坐标, 创建前后的海马面具图像。使用 #34; example_func2highres 和 #34, 将前后的海马面具登记到本机功能空间中; 矩阵在注册目录中的壮举输出. 
	注: 在登记到功能空间后, 使用前、后三分之二防止了两种海马种子的信号模糊。在海马的纵轴上有功能专门化的证据 31 , 32 , 33 , 34 。前区是输入区域, 与编码相关, 而后海马是与内存检索和合并相关的输出区域 35 , 36 , 37 . 因此, 使用这些区域可以评估前与后海马在编码与记忆任务的检索阶段的功能参与. </li>
	<li> 使用 FSL 平均序列 (fslmeants) 从前和后海马种子中提取噪平均值序列 ( 图 1 )。按照程序说明, 使用前或后海马种子作为面具和噪, 预处理功能数据为主要图像. </li>
</ol> <img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55394/55394fig1.jpg"/> 图 1 : 海马种子。在原生空间中, 单个参与者和 #39 的海马前种子呈黄色。同一参与者的后海马种子以粉红色显示。种子是定义在每个参与者和 #39 的独特结构图像, 然后注册到他们的功能扫描。种子从来没有在一个标准化的空间, 这提高了准确性的海马分割。此图已被转载, 并具有 12 权限。<a href="http://ecsource.jove.com/files/ftp_upload/55394/55394fig1large.jpg" target="_blank"> 请单击此处查看此图的较大版本. </a> 
 6. PPI 模型 <ol> <li> 使用 GUI 进行 FSL 壮举来加载预处理的功能数据。
	<ol> <li> 在 "数据" 选项卡中, 选择 #34; filtered_func_data 和 #34; 噪映像 (从4节中完成的步骤输出) 作为输入文件。在 pre-stats 选项卡中, 设置运动矫正和脑提取 #34; 没有. #34; 取消盒执行时间滤波和空间平滑. </li>
	</ol> </li> <li> PPI Model 设置 ( 表 1 )。
	<ol> <li> 在 "统计" 选项卡中, 选择和 #34; 完整的模型设置和 #34;。在 evs 选项卡中, 从第一级模型中添加所有 evs: 6 运动修正 evs, 从 FSL 运动异常点和任务定时 evs 中混淆 EV 矩阵。单击向上箭头以添加 EVs。包括在这个模型中的生理 timecourse 从种子 (文本文件输出的 fslmeants 在步骤 5.4) 作为一个协没有兴趣通过单击向上箭头. </li>
		<li> 创建 PPI 术语。
		<ol> <li> 选择和 #34; 交互和 #34; 在 "基本形状" 菜单中选择种子 timecourse ev 和一个任务 ev。为和 #34; 使零和 #34; 选项; 选择; #34; 吝啬; #34; 为种子 timecourse ev 和 #34; 中心和 #34; 为任务 EV。对其他任务阶段重复此过程。为每个种子区域运行一个单独的模型. 
			注: 这些新的 EVs 是 PPI 术语的阶段的任务选择 (心理) 和种子 (生理)。在本研究中, 每个 ppi 模型中都包含了一个用于编码阶段的 ppi 术语和一个用于检索阶段的第二 ppi 术语。#34; 中心和 #34; 选项确保 #34; #34; 和 #34; 关闭和 #34; 块设计任务的阶段被同等对待。#34; 平均值; #34; 选项始终应用于种子 timecourse, 并导致从该回归中减去的平均值. </li>
		</ol> </li> <li> 在对比度和 F 测试选项卡中, 通过输入和 #34 来对以下特定效果进行建模; 1 和 #34; 在相应的 EV 单元格中: psych_enc (编码任务阶段)、psych_ret (检索任务阶段)、物理 (种子 timecourse)、PPI_enc (种子 PPI 和编码), PPI_ret (种子和检索 PPI)。最后, 输入一个和 #34;-1 和 #34; 为每个任务阶段建模负质子. </li>
	</ol> </li> </ol> <img alt="Table 1" src="/files/ftp_upload/55394/55394table1.jpg"/> 表 1: gPPI 型号设置 。
 7. 组比较 <ol> <li> 选择和 #34; higher-level 分析和 #34; 在 FSL 壮举中, 运行一个简单的组模型比较载脂蛋白和 #949; 4 承运人到比较为每个任务-种子组合. 
	注意: 这些比较运行, 以生成相关的组4D 残差图像 (和 #34; res4d 和 #34;) 需要估计数据集的平滑度。从统计学意义上说, 这组比较的结果是有效的, 但在下面的步骤中介绍了使用 AFNI 和 SPM8 在蒙特卡罗模拟的基础上设置一个重要的簇最小值的方法. </li>
	<li> 使用功能神经影像分析 (AFNI) <ol> <li> 使用 AFNI 和 #39 3dFWHMx (2015年12月以后的任何版本) 在命令行上, 以估计使用 FSL 生成的组4D 残差图像的平滑度. 
		注意: 在 AFNI 和 #39 中发现了一个 bug, 并在2015年5月更正了3dClustSim。在 2015年12月, AFNI 和 #39 的3dFWHMx 被更新了到更加准确地模型 auto-correlations。因此, 应使用2015年12月或更高版本中发布的这些工具. </li>
		<li> 使用 AFNI 和 #39 3dClustSim (2015年12月以后的任何版本) 来确定在不同的体素级阈值下, 簇的最小到达意义。在命令行调用3dClustSim 中包括上一步的平滑度估计。从3dClustSim 生成的表中, 基于对预期效果和 #39 的研究假设; 高度和程度, 选择一个素级阈值和相应的簇最小大小. 
		注: 通常, 较大的簇会最小化误报. </li>
	</ol> </li> <li> 使用统计参数映射 (SPM8) <ol> <li> 使用 SPM8 GUI, 选择和 #34; 指定 2 nd 级别和 #34;。将打开批编辑器。选择和 #34; 设计中的两个样本 t 检验和 #34;导航到具有1组 (载脂蛋白和 #949; 4 承运人) 的参数估计图像的目录, 然后通过单击它们进行选择。接下来, 添加组 2 (载脂蛋白和 #949; 4 比较) 图像。通过单击 "绿色播放" 按钮来运行此比较. </li>
		<li> 返回到 spm GUI, 选择和 #34; 估计和 #34;, 然后导航到在上一步中创建的管理模型的文件. </li>
		<li> 选择和 #34; 结果和 #34; 运行组比较对比: 载脂蛋白和 #949; 4 承运人和 #62; 载脂蛋白和 #949; 4 比较, 载脂蛋白和 #949; 4 比较和 #62; 载脂蛋白和 #949; 4 载体。
		<ol> <li> 单击 #34;d efine 新的对比度和 #34; 选择、#34、#34、#34、类型、#34、输入、#34、1-1、#34、#34、#34、4载波和 #949; 载脂蛋白和 #62; 4 比较。单击并 #34;d 一个和 #34;。选择和 #34; 无 #34; 对于应用掩蔽, 根据步骤7.2.2.Enter 和 #34 中所做的确定, 手动设置像素级阈值和簇大小;-1 1 和 #34; 用于载脂蛋白和 #949; 4 比较和 #62; 和 #949; 4 载体. 
			注: 在本研究中, voxelwise 阈值的 p 和 #60; 0.005 是使用和集群值在阿尔法和 #60; 0.05. </li>
		</ol>
		</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Sporns, O., Tononi, G., K&#246;tter, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Human+Connectome:+A+Structural+Description+of+the+Human+Brain.">The Human Connectome: A Structural Description of the Human Brain.</a> PLoS Comput Biol. 1 (4), 42 (2005).</li><li>Friston, K. J., Buechel, C., Fink, G. R., Morris, J., Rolls, E., Dolan, 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=Psychophysiological+and+modulatory+interactions+in+neuroimaging.">Psychophysiological and modulatory interactions in neuroimaging.</a> NeuroImage. 6 (3), 218-229 (1997).</li><li>McLaren, D. G., Ries, M. L., Xu, G., Johnson, S. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+generalized+form+of+context-dependent+psychophysiological+interactions+(gPPI):+a+comparison+to+standard+approaches.">A generalized form of context-dependent psychophysiological interactions (gPPI): a comparison to standard approaches.</a> NeuroImage. 61 (4), 1277-1286 (2012).</li><li>O'Reilly, J. X., Woolrich, M. W., Behrens, T. E. J., Smith, S. M., Johansen-Berg, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tools+of+the+trade:+psychophysiological+interactions+and+functional+connectivity.">Tools of the trade: psychophysiological interactions and functional connectivity.</a> Soc Cogn Affect Neurosci. 7 (5), 604-609 (2012).</li><li>Moody, T. D., Sasaki, M. 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=Functional+connectivity+for+face+processing+in+individuals+with+body+dysmorphic+disorder+and+anorexia+nervosa.">Functional connectivity for face processing in individuals with body dysmorphic disorder and anorexia nervosa.</a> Psychol Med. 45 (16), 3491-3503 (2015).</li><li>Simard, I., Luck, D., Mottron, L., Zeffiro, T. A., Souli&#232;res, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Autistic+fluid+intelligence:+Increased+reliance+on+visual+functional+connectivity+with+diminished+modulation+of+coupling+by+task+difficulty.">Autistic fluid intelligence: Increased reliance on visual functional connectivity with diminished modulation of coupling by task difficulty.</a> NeuroImage Clin. 9, 467-478 (2015).</li><li>Yan, L. -. R., Wu, Y. -. B., Zeng, X. -. H., Gao, L. -. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dysfunctional+putamen+modulation+during+bimanual+finger-to-thumb+movement+in+patients+with+Parkinson's+disease.">Dysfunctional putamen modulation during bimanual finger-to-thumb movement in patients with Parkinson's disease.</a> Front Hum Neurosci. 9, 516 (2015).</li><li>Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., Braver, T. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multi-task+connectivity+reveals+flexible+hubs+for+adaptive+task+control.">Multi-task connectivity reveals flexible hubs for adaptive task control.</a> Nat Neurosci. 16 (9), 1348-1355 (2013).</li><li>McLaren, D. G., Sperling, R. A., Atri, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flexible+modulation+of+network+connectivity+related+to+cognition+in+Alzheimer's+disease.">Flexible modulation of network connectivity related to cognition in Alzheimer's disease.</a> NeuroImage. 100, 544-557 (2014).</li><li>Morawetz, C., Bode, S., Baudewig, J., Heekeren, H. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effective+amygdala-prefrontal+connectivity+predicts+individual+differences+in+successful+emotion+regulation.">Effective amygdala-prefrontal connectivity predicts individual differences in successful emotion regulation.</a> Soc Cogn Affect Neurosci. , 169 (2016).</li><li>Takashima, A., Bakker, I., van Hell, J. G., Janzen, G., McQueen, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Richness+of+information+about+novel+words+influences+how+episodic+and+semantic+memory+networks+interact+during+lexicalization.">Richness of information about novel words influences how episodic and semantic memory networks interact during lexicalization.</a> NeuroImage. 84, 265-278 (2014).</li><li>Harrison, T. M., Burggren, A. C., Small, G. W., Bookheimer, S. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Altered+memory-related+functional+connectivity+of+the+anterior+and+posterior+hippocampus+in+older+adults+at+increased+genetic+risk+for+Alzheimer's+disease.">Altered memory-related functional connectivity of the anterior and posterior hippocampus in older adults at increased genetic risk for Alzheimer's disease.</a> Hum Brain Mapp. 37 (1), 366-380 (2016).</li><li>Wechsler, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Wecshler Adult Intelligence Scale, 3rd Edition. , (1997).</li><li>Cauthen, N. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Verbal+fluency:+normative+data.">Verbal fluency: normative data.</a> J Clin Psychol. 34 (1), 126-129 (1978).</li><li>Goodglass, H. P., Kaplan, E. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Boston Naming Test, 3rd Edition. , (2001).</li><li>Buschke, H., Fuld, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluating+storage,+retention,+and+retrieval+in+disordered+memory+and+learning.">Evaluating storage, retention, and retrieval in disordered memory and learning.</a> Neurol. 24 (11), 1019-1025 (1974).</li><li>Osterrieth, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Le+test+de+copie+d'une+figure+complex:+Contribution+&#224;+l'&#233;tude+de+la+perception+et+de+la+memoir.">Le test de copie d'une figure complex: Contribution &#224; l'&#233;tude de la perception et de la memoir.</a> Archives de Psychologie. 30, 286-356 (1944).</li><li>Hamilton, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+assessment+of+anxiey+states+by+rating.">The assessment of anxiey states by rating.</a> Br J Med Psychol. 32, 50-55 (1959).</li><li>Hamilton, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+rating+scale+for+depression.">A rating scale for depression.</a> J Neurol Neurosurg Psychiatry. 23, 56-62 (1960).</li><li>Folstein, M. F., Robins, L. N., Helzer, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Mini-Mental+State+Examination.">The Mini-Mental State Examination.</a> Arch Gen Psychiatry. 40 (7), 812 (1983).</li><li>O'Brien, D., Campbell, K. A., Morken, N. W., Bair, R. J., Heath, E. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+Nucleic+Acid+Purification+for+Large+Samples.">Automated Nucleic Acid Purification for Large Samples.</a> J Lab Autom. 6 (2), 67-70 (2001).</li><li>Lehmann, M., Ghosh, P. 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=Greater+medial+temporal+hypometabolism+and+lower+cortical+amyloid+burden+in+ApoE4-positive+AD+patients.">Greater medial temporal hypometabolism and lower cortical amyloid burden in ApoE4-positive AD patients.</a> J Neurol Neurosurg Psychiatry. 85 (3), 266-273 (2014).</li><li>. TaqMan&#174; SNP Genotyping Assays User Guide Available from: <a target="_blank" href="https://tools.thermofisher.com/content/sfs/manuals/TaqMan_SNP_Genotyping_Assays_man.pdf">https://tools.thermofisher.com/content/sfs/manuals/TaqMan_SNP_Genotyping_Assays_man.pdf</a> (2014)</li><li>Bookheimer, S. Y., Strojwas, M. H., 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=Patterns+of+brain+activation+in+people+at+risk+for+Alzheimer's+disease.">Patterns of brain activation in people at risk for Alzheimer's disease.</a> N Engl J Med. 343 (7), 450-456 (2000).</li><li>Jenkinson, M., Bannister, P., Brady, M., Smith, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+optimization+for+the+robust+and+accurate+linear+registration+and+motion+correction+of+brain+images.">Improved optimization for the robust and accurate linear registration and motion correction of brain images.</a> NeuroImage. 17 (2), 825-841 (2002).</li><li>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=Fast+robust+automated+brain+extraction.">Fast robust automated brain extraction.</a> Hum Brain Mapp. 17 (3), 143-155 (2002).</li><li>Greve, D. N., Fischl, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accurate+and+robust+brain+image+alignment+using+boundary-based+registration.">Accurate and robust brain image alignment using boundary-based registration.</a> NeuroImage. 48 (1), 63-72 (2009).</li><li>Patenaude, B., Smith, S. M., Kennedy, D. N., Jenkinson, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Bayesian+model+of+shape+and+appearance+for+subcortical+brain+segmentation.">A Bayesian model of shape and appearance for subcortical brain segmentation.</a> NeuroImage. 56 (3), 907-922 (2011).</li><li>. Learn MATLAB Basics Available from: <a target="_blank" href="https://www.mathworks.com/support/learn-with-matlab-tutorials.html?s_tid=hp_ff_I_tutorials">https://www.mathworks.com/support/learn-with-matlab-tutorials.html?s_tid=hp_ff_I_tutorials</a> (2017)</li><li>Lerma-Usabiaga, G., Iglesias, J. E., Insausti, R., Greve, D. N., Paz-Alonso, P. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+segmentation+of+the+human+hippocampus+along+its+longitudinal+axis.">Automated segmentation of the human hippocampus along its longitudinal axis.</a> Hum Brain Mapp. 37 (9), 3353-3367 (2016).</li><li>Salami, A., Eriksson, J., Nyberg, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Opposing+effects+of+aging+on+large-scale+brain+systems+for+memory+encoding+and+cognitive+control.">Opposing effects of aging on large-scale brain systems for memory encoding and cognitive control.</a> J Neurosci. 32 (31), 10749-10757 (2012).</li><li>Schacter, D. L., Wagner, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Medial+temporal+lobe+activations+in+fMRI+and+PET+studies+of+episodic+encoding+and+retrieval.">Medial temporal lobe activations in fMRI and PET studies of episodic encoding and retrieval.</a> Hippocampus. 9 (1), 7-24 (1999).</li><li>Strange, B., Dolan, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+segregation+within+the+human+hippocampus.">Functional segregation within the human hippocampus.</a> Mol Psychiatry. 4 (6), 508-511 (1999).</li><li>Strange, B. A., Fletcher, P. C., Henson, R. N., Friston, K. J., Dolan, 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=Segregating+the+functions+of+human+hippocampus.">Segregating the functions of human hippocampus.</a> Proc Natl Acad Sci U S A. 96 (7), 4034-4039 (1999).</li><li>Eldridge, L. L., Engel, S. A., Zeineh, M. M., Bookheimer, S. Y., Knowlton, B. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+dissociation+of+encoding+and+retrieval+processes+in+the+human+hippocampus.">A dissociation of encoding and retrieval processes in the human hippocampus.</a> J Neurosci. 25 (13), 3280-3286 (2005).</li><li>Strange, B. A., Witter, M. P., Lein, E. S., Moser, E. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+organization+of+the+hippocampal+longitudinal+axis.">Functional organization of the hippocampal longitudinal axis.</a> Nat Rev Neurosci. 15 (10), 655-669 (2014).</li><li>Zeineh, M. M., Engel, S. A., Thompson, P. M., Bookheimer, S. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamics+of+the+hippocampus+during+encoding+and+retrieval+of+face-name+pairs.">Dynamics of the hippocampus during encoding and retrieval of face-name pairs.</a> Science. 299 (5606), 577-580 (2003).</li><li>Nieuwenhuis, S., Forstmann, B. U., Wagenmakers, E. -. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Erroneous+analyses+of+interactions+in+neuroscience:+a+problem+of+significance.">Erroneous analyses of interactions in neuroscience: a problem of significance.</a> Nat Neurosci. 14 (9), 1105-1107 (2011).</li><li>Cisler, J. M., Bush, K., Steele, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparison+of+statistical+methods+for+detecting+context-modulated+functional+connectivity+in+fMRI.">A comparison of statistical methods for detecting context-modulated functional connectivity in fMRI.</a> NeuroImage. 84, 1042-1052 (2014).</li><li>Friston, K. J., Harrison, L., Penny, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+causal+modelling.">Dynamic causal modelling.</a> NeuroImage. 19 (4), 1273-1302 (2003).</li></ol>

副总经理是 Biospective、Inc. Biospective 的雇员, Inc. 没有处理任何所提供的数据。

早期的任务型 fMRI 研究旨在揭示特定认知过程或需求之间的统计关系, 以及与基线测量相关的大胆信号的变化。这种传统的方法是有用的, 以确定大脑中的特定区域的活动是由实验任务调制。相比之下, PPI 分析主要关注的是功能连接的调制, 或活动的同步性, 这是由任务引起的认知过程造成的。PPI 度量在定义的感兴趣区域 (种子) 和其他大脑区域之间的上下文相关的功能连接, 而不仅仅是活动在局部?...

"连接" 这个词是在2005年被创造出来的, 它标志着神经科学的范式转变, 并延续到今天的1。在功能网络、连通性和区域间的相互作用方面, 大脑的描述越来越广泛。然而, 区域功能专门化的划分和 fMRI 测量的活动与任务需求之间的联系仍然是有效的和有用的方法。鉴于对 connectomics 的兴趣越来越浓厚, 功能性的 fMRI 分析方法越来越受欢迎。一种测量依赖于任务需求的功能连通性变化的方法是利用 PPI 的概念。PPI 是一个活跃的任务阶段或特定的任务需求 ("心理") 与一个感兴趣的区域或 "种子" 在大脑中的功能连接 ("生理") 的相互作用。PPI 不同于双变量、关联的功能连通性分析, 一般衡量两个区域的活动之间的相关性, 而不受任务需求的约束。
PPI 分析的概念和框架最初是由 Friston 和同事在 1997年2中描述的。作者断言, 他们的方法是重要的, 因为它将允许对连通性的调查更具体功能, 并允许推断, 在远端种子的活动可能是调节活动的结果, 由任务的需求。在 2012年, 迈凯轮和同事增加了这个原始框架, 并描述了一种 gPPI 的方法, 其中所有的任务阶段及其交互都包含在一个模型3中。此方法导致对任务阶段和交互进行调查的结果更加敏感和具体。这是我们在本研究中使用的更新的 gPPI 方法 (请参见协议中的步骤 6.2.2)。gPPI 方法现已在200多项研究中引用。为了清楚起见, 我们以后使用 PPI 来描述标准和通用版本的共同特征。"gPPI" 将用于讨论与新框架相关的特定进展。
PPI 分析的总体目标是了解认知任务的需求是如何影响或调节种子区域的功能连通性的。PPI 分析需要一个强的先验假设。种子区域中的活动必须由任务调整, 以使 PPI 方法有效地工作4。例如, 在本研究中, 我们基于我们的种子选择的有力证据表明, 海马活动是由记忆任务的认知需求调制的。使用 PPI, 可以确定在特定任务阶段, 在功能上与海马体有显著或多或少的连接的区域。简而言之, 我们提出的问题是: 与基线相比, 在上下文 A 中, 哪个区域的活动与种子更相关？我们还可以要求逻辑上的相反 (因为理解差异是很重要的): "与基线相比, 在哪些区域中活动与种子的相关性更小？在解释 PPI 效应中的群体差异时, 重要的是要检查数据, 以及在功能连通性方面的正负变化, 或者两者都是驱动群体差异。
PPI 方法已被用于研究健康控制中的动态任务控制中枢, 如何调节功能连通性与阿尔茨海默氏病 (AD) 的认知表现、自闭症患者的智力、运动网络连通性有关在个人与帕金森病, 面部处理在个体与身体畸形紊乱并且厌食, 情感章程, 记忆和许多其他具体问题与连接性相关5,6,7 ,8,9,10,11。在本研究中, 我们比较了海马区各次区域在记忆编码和检索过程中的功能连通性的变化, 这两组个体在没有风险因子12的情况下, 对一组 AD 的遗传风险增加。下面介绍了我们使用 gPPI 方法的协议, 它允许我们测试任务引起的功能连接的变化是否与 AD 的遗传风险因素 APOEε4存在差异。

<table border="0" cellpadding="0" cellspacing="0" width="1091">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:307px;">3T manetic resonance imaging scanner</td>
			<td style="width:280px;">Siemens Medical Solutions</td>
			<td style="width:237px;">MAGNETOM Trio, A Tim System</td>
			<td style="width:267px;">3T MRI Scanner</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:307px;">FSL (FMRIB Software Library)</td>
			<td style="width:280px;">Oxford University</td>
			<td style="width:237px;">Version 6.0</td>
			<td>Functional Imaging Processing Software</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:307px;">AFNI (Analysis of Functional Neuroimaging)</td>
			<td style="width:280px;">National Institute of Mental Health, National Institutes of Health</td>
			<td style="width:237px;">Any version after May 2015</td>
			<td>Functional Imaging Processing Software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:307px;">SPM8 (Statistical Parametric Mapping)</td>
			<td style="width:280px;">University College of London</td>
			<td style="width:237px;">SPM8</td>
			<td>Functional Imaging Processing Software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:307px;">Matlab Software</td>
			<td style="width:280px;">The Mathworks, Inc</td>
			<td style="width:237px;">Version R2012a</td>
			<td>Computing Software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:307px;">SDS Software</td>
			<td style="width:280px;">Applied Biosystems, Inc</td>
			<td>7900HT Fast Real-Time PCR System</td>
			<td>Real Time PCR</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:307px;">Taqman Assays</td>
			<td style="width:280px;">ThermoFisher Scientific</td>
			<td style="width:237px;">Specific to SNP</td>
			<td>SNP Genotyping</td>
		</tr>
	</tbody>
</table>

generalized psychophysiological interaction (ppi) analysis of memory related connectivity in individuals at genetic risk for alzheimer's disease

在神经影像学中, 功能性磁共振成像 (fMRI) 测量大脑中的血氧水平依赖性 (黑体) 信号。在大脑的空间独立区域中, 黑体信号的相关度定义了这些区域的功能连通性。在认知功能磁共振成像任务中, 可以使用心理交互 (PPI) 分析来检查在认知任务定义的特定上下文中, 函数连通性的变化。此类任务的一个示例是使用内存系统, 要求参与者学习对不相关的单词 (编码), 并在第一个单词 (检索) 时撤回对的第二个单词。在本研究中, 我们使用了这种类型的联想记忆任务和广义 PPI (gPPI) 分析来比较老年人的海马连接性的变化, 谁是阿尔茨海默病 (AD) 遗传危险因素载脂蛋白 E epsilon-4 (APOEε4)。具体来说, 我们表明, 在编码和检索过程中, 海马区的功能连通性会发生变化, 这是联想记忆任务的两个活动阶段。与比较相比, APOEε4携带者海马体功能连通性的上下文相关变化明显不同。PPI 分析可以检查功能连通性的变化, 不同于单变量的主要效果, 并且可以跨组比较这些变化。因此, PPI 分析可能揭示了传统单变量方法不捕获的特定群体中复杂的任务效果。然而, PPI 分析不能确定功能连通区域之间的方向性或因果关系。然而, PPI 分析提供了强大的手段, 产生特定的假设有关的功能关系, 可以测试使用因果模型。随着大脑在连通性和网络方面越来越被描述, PPI 是分析 fMRI 任务数据的重要方法, 它与人脑的当前概念是一致的。

有两个不同的主动任务阶段 (编码和检索) 和两个种子区域 (前和后海马) 有四条件来报告每个组的结果。组内任务激活映射 (此处未显示, 请参见哈里森et al., 201612) 显示枕叶、听觉皮层、顶叶大区、额叶区、颞上回和尾状突起 (更明显在检索过程中) 在两个实验组的编码和检索过程中有显著的加粗信号。在组内 PPI 分析显示, 没有明显增加的功能连接?...

Watch this Scientific Journal Video about Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease at JoVE.com

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

这篇手稿描述了如何实现心理的交互分析, 揭示了任务相关的变化, 在功能连通性之间的选择种子区和体在其他区域的大脑。心理交互分析是一种常用的方法来检查任务对大脑连通性的影响, 有别于传统的单变量激活效应。

广义心理相互作用 (PPI) 分析阿尔茨海默病遗传危险个体的记忆相关连通性

In neuroimaging, functional magnetic resonance imaging (fMRI) measures the blood-oxygenation-level dependent (BOLD) signal in the brain. The degree of ...

generalized-psychophysiological-interaction-ppi-analysis-memory

Research

JoVE Journal

Neuroscience

Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

14.8K 次观看.  University of California, Los Angeles. 这篇手稿描述了如何实现心理的交互分析, 揭示了任务相关的变化, 在功能连通性之间的选择种子区和体在其他区域的大脑。心理交互分析是一种常用的方法来检查任务对大脑连通性的影响, 有别于传统的单变量激活效应。

视频: Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Preparation of Oligomeric &beta;-amyloid1-42 and Induction of Synaptic Plasticity Impairment on Hippocampal Slices

Impairment of synaptic connections is likely to underlie the subtle amnesic changes occurring at the early stages of Alzheimer s Disease (AD). &beta;-amyloid (A&beta;), a peptide produced in high amounts in AD, is known to reduce Long-Term Potentiation (LTP), a cellular correlate of learning and memory. Indeed, LTP impairment caused by A&beta; is a useful experimental paradigm for studying synaptic dysfunctions in AD models and for screening drugs capable of mitigating or reverting such synaptic impairments. Studies have shown that A&beta; produces the LTP disruption preferentially via its oligomeric form. Here we provide a detailed protocol for impairing LTP by perfusion of oligomerized synthetic A&beta;1-42 peptide onto acute hippocampal slices. In this video, we outline a step-by-step procedure for the preparation of oligomeric A&beta;1-42. Then, we follow an individual experiment in which LTP is reduced in hippocampal slices exposed to oligomerized A&beta;1-42 compared to slices in a control experiment where no A&beta;1-42 exposure had occurred.

Preparation of Oligomeric &#946;-amyloid1-42 and Induction of Synaptic Plasticity Impairment on Hippocampal Slices

One feature of Alzheimer's Disease is the elevation of A&beta;1-42 peptide. Here we provide a protocol for preparing synthetic A&beta;1-42 oligomers, which impairs hippocampal Long-Term Potentiation, a cellular correlate of memory. This procedure is useful for investigating mechanisms of A&beta;-induced pathology and drug screening.

低聚β-淀粉样蛋白的制备 1-42和突触可塑性损伤诱导海马脑片

Impairment of synaptic connections is likely to underlie the subtle amnesic changes occurring at the early stages of Alzheimer s Disease (AD). ...

科研

神经科学

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Neuronal morphology plays a significant role in determining how neurons function and communicate1-3. Specifically, it affects the ability of neurons to receive inputs from other cells2 and contributes to the propagation of action potentials4,5. The morphology of the neurites also affects how information is processed. The diversity of dendrite morphologies facilitate local and long range signaling and allow individual neurons or groups of neurons to carry out specialized functions within the neuronal network6,7. Alterations in dendrite morphology, including fragmentation of dendrites and changes in branching patterns, have been observed in a number of disease states, including Alzheimer's disease8, schizophrenia9,10, and mental retardation11. The ability to both understand the factors that shape dendrite morphologies and to identify changes in dendrite morphologies is essential in the understanding of nervous system function and dysfunction.
Neurite morphology is often analyzed by Sholl analysis and by counting the number of neurites and the number of branch tips. This analysis is generally applied to dendrites, but it can also be applied to axons. Performing this analysis by hand is both time consuming and inevitably introduces variability due to experimenter bias and inconsistency. The Bonfire program is a semi-automated approach to the analysis of dendrite and axon morphology that builds upon available open-source morphological analysis tools. Our program enables the detection of local changes in dendrite and axon branching behaviors by performing Sholl analysis on subregions of the neuritic arbor. For example, Sholl analysis is performed on both the neuron as a whole as well as on each subset of processes (primary, secondary, terminal, root, etc.) Dendrite and axon patterning is influenced by a number of intracellular and extracellular factors, many acting locally. Thus, the resulting arbor morphology is a result of specific processes acting on specific neurites, making it necessary to perform morphological analysis on a smaller scale in order to observe these local variations12.
The Bonfire program requires the use of two open-source analysis tools, the NeuronJ plugin to ImageJ and NeuronStudio. Neurons are traced in ImageJ, and NeuronStudio is used to define the connectivity between neurites. Bonfire contains a number of custom scripts written in MATLAB (MathWorks) that are used to convert the data into the appropriate format for further analysis, check for user errors, and ultimately perform Sholl analysis. Finally, data are exported into Excel for statistical analysis. A flow chart of the Bonfire program is shown in Figure 1.

We have developed a computer program to analyze neuronal morphology. In combination with two existing open source analysis tools, our program performs Sholl analysis and determines the number of neurites, branch points, and neurite tips. The analyses are performed so that local changes in neurite morphology can be observed.

肖尔自动数字化的神经元形态分析，多尺度

Neuronal morphology plays a significant role in determining how neurons function and communicate1-3.  Specifically, it affects the ability of neurons to ...

Detection of Neuritic Plaques in Alzheimer's Disease Mouse Model

Alzheimer's disease (AD) is the most common neurodegenerative disorder leading to dementia. Neuritic plaque formation is one of the pathological hallmarks of Alzheimer's disease. The central component of neuritic plaques is a small filamentous protein called amyloid &beta; protein (A&beta;)1, which is derived from sequential proteolytic cleavage of the beta-amyloid precursor protein (APP) by &beta;-secretase and &gamma;-secretase. The amyloid hypothesis entails that A&gamma;-containing plaques as the underlying toxic mechanism in AD pathology2. The postmortem analysis of the presence of neuritic plaque confirms the diagnosis of AD. To further our understanding of A&gamma; neurobiology in AD pathogenesis, various mouse strains expressing AD-related mutations in the human APP genes were generated. Depending on the severity of the disease, these mice will develop neuritic plaques at different ages. These mice serve as invaluable tools for studying the pathogenesis and drug development that could affect the APP processing pathway and neuritic plaque formation. In this protocol, we employ an immunohistochemical method for specific detection of neuritic plaques in AD model mice. We will specifically discuss the preparation from extracting the half brain, paraformaldehyde fixation, cryosectioning, and two methods to detect neurotic plaques in AD transgenic mice: immunohistochemical detection using the ABC and DAB method and fluorescent detection using thiofalvin S staining method.

One of the pathological characteristics of AD is the formation of Amyloid &#x03B2; protein positive neuritic plaques. In this protocol we describe two methods to detect neuritic plaques in transgenic AD model mice: immunohistochemical detection using the ABC and DAB method and fluorescent detection using thioflavin S staining method.

在阿尔茨海默氏症小鼠模型神经炎斑检测

Alzheimer's disease (AD) is the most common neurodegenerative disorder leading to dementia. Neuritic plaque formation is one of the pathological hallmarks ...

Morris Water Maze Test for Learning and Memory Deficits in Alzheimer&#39;s Disease Model Mice

The Morris Water Maze (MWM) was first established by neuroscientist Richard G. Morris in 1981 in order to test hippocampal-dependent learning, including acquisition of spatial memoryand long-term spatial memory 1. The MWM is a relatively simple procedure typically consisting of six day trials, the main advantage being the differentiation between the spatial (hidden-platform) and non-spatial (visible platform) conditions 2-4. In addition, the MWM testing environment reduces odor trail interference 5. This has led the task to be used extensively in the study of the neurobiology and neuropharmacology of spatial learning and memory. The MWM plays an important role in the validation of rodent models for neurocognitive disorders such as Alzheimer&#x2019;s Disease 6, 7. In this protocol we discussed the typical procedure of MWM for testing learning and memory and data analysis commonly used in Alzheimer&#x2019;s disease transgenic model mice.

Morris Water Maze Test for Learning and Memory Deficits in Alzheimer's Disease Model Mice

The Morris Water Maze is a behavioral task to test hippocampal-dependent learning and memory. It has been widely used in the study of neurobiology, neuropharmacology and neurocognitive disorders in rodent models.

在阿尔茨海默氏病模型小鼠学习和记忆障碍的Morris水迷宫测试

The Morris Water Maze (MWM) was first established by neuroscientist Richard G. Morris in 1981 in order to test hippocampal-dependent learning, including ...

Morphological Analysis of Drosophila Larval Peripheral Sensory Neuron Dendrites and Axons Using Genetic Mosaics

Nervous system development requires the correct specification of neuron position and identity, followed by accurate neuron class-specific dendritic development and axonal wiring. Recently the dendritic arborization (DA) sensory neurons of the Drosophila larval peripheral nervous system (PNS) have become powerful genetic models in which to elucidate both general and class-specific mechanisms of neuron differentiation.
There are four main DA neuron classes (I-IV)1. They are named in order of increasing dendrite arbor complexity, and have class-specific differences in the genetic control of their differentiation2-10. The DA sensory system is a practical model to investigate the molecular mechanisms behind the control of dendritic morphology11-13 because: 1) it can take advantage of the powerful genetic tools available in the fruit fly, 2) the DA neuron dendrite arbor spreads out in only 2 dimensions beneath an optically clear larval cuticle making it easy to visualize with high resolution in vivo, 3) the class-specific diversity in dendritic morphology facilitates a comparative analysis to find key elements controlling the formation of simple vs. highly branched dendritic trees, and 4) dendritic arbor stereotypical shapes of different DA neurons facilitate morphometric statistical analyses.
DA neuron activity modifies the output of a larval locomotion central pattern generator14-16. The different DA neuron classes have distinct sensory modalities, and their activation elicits different behavioral responses14,16-20. Furthermore different classes send axonal projections stereotypically into the Drosophila larval central nervous system in the ventral nerve cord (VNC)21. These projections terminate with topographic representations of both DA neuron sensory modality and the position in the body wall of the dendritic field7,22,23. Hence examination of DA axonal projections can be used to elucidate mechanisms underlying topographic mapping7,22,23, as well as the wiring of a simple circuit modulating larval locomotion14-17.
We present here a practical guide to generate and analyze genetic mosaics24 marking DA neurons via MARCM (Mosaic Analysis with a Repressible Cell Marker)1,10,25 and Flp-out22,26,27 techniques (summarized in Fig. 1).

Morphological Analysis of Drosophila Larval Peripheral Sensory Neuron Dendrites and Axons Using Genetic Mosaics

The dendritic arborization sensory neurons of the Drosophila larval peripheral nervous system are useful models to elucidate both general and neuron class-specific mechanisms of neuron differentiation. We present a practical guide to generate and analyze dendritic arborization neuron genetic mosaics.

形态分析果蝇幼虫外周感觉神经元树突和轴突使用遗传马赛克

Nervous system development requires the correct specification of neuron position and identity, followed by accurate neuron class-specific dendritic ...

T-maze Forced Alternation and Left-right Discrimination Tasks for Assessing Working and Reference Memory in Mice

Forced alternation and left-right discrimination tasks using the T-maze have been widely used to assess working and reference memory, respectively, in rodents. In our laboratory, we evaluated the two types of memory in more than 30 strains of genetically engineered mice using the automated version of this apparatus. Here, we present the modified T-maze apparatus operated by a computer with a video-tracking system and our protocols in a movie format. The T-maze apparatus consists of runways partitioned off by sliding doors that can automatically open downward, each with a start box, a T-shaped alley, two boxes with automatic pellet dispensers at one side of the box, and two L-shaped alleys. Each L-shaped alley is connected to the start box so that mice can return to the start box, which excludes the effects of experimenter handling on mouse behavior. This apparatus also has an advantage that in vivo microdialysis, in vivo electrophysiology, and optogenetics techniques can be performed during T-maze performance because the doors are designed to go down into the floor. In this movie article, we describe T-maze tasks using the automated apparatus and the T-maze performance of &alpha;-CaMKII+/- mice, which are reported to show working memory deficits in the eight-arm radial maze task. Our data indicated that &alpha;-CaMKII+/- mice showed a working memory deficit, but no impairment of reference memory, and are consistent with previous findings using the eight-arm radial maze task, which supports the validity of our protocol. In addition, our data indicate that mutants tended to exhibit reversal learning deficits, suggesting that &alpha;-CaMKII deficiency causes reduced behavioral flexibility. Thus, the T-maze test using the modified automatic apparatus is useful for assessing working and reference memory and behavioral flexibility in mice.

This article presents the protocol of T-maze tests using a modified automated apparatus for assessing the learning and memory functions in mice.

T-迷宫强迫交替和评估工作，并参考在小鼠记忆的左，右的歧视任务

Forced alternation and left-right discrimination tasks using the T-maze have been widely used to assess working and reference memory, respectively, in ...

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Signaling of information in the vertebrate central nervous system is often carried by populations of neurons rather than individual neurons. Also propagation of suprathreshold spiking activity involves populations of neurons. Empirical studies addressing cortical function directly thus require recordings from populations of neurons with high resolution. Here we describe an optical method and a deconvolution algorithm to record neural activity from up to 100 neurons with single-cell and single-spike resolution. This method relies on detection of the transient increases in intracellular somatic calcium concentration associated with suprathreshold electrical spikes (action potentials) in cortical neurons. High temporal resolution of the optical recordings is achieved by a fast random-access scanning technique using acousto-optical deflectors (AODs)1. Two-photon excitation of the calcium-sensitive dye results in high spatial resolution in opaque brain tissue2. Reconstruction of spikes from the fluorescence calcium recordings is achieved by a maximum-likelihood method. Simultaneous electrophysiological and optical recordings indicate that our method reliably detects spikes (&gt;97% spike detection efficiency), has a low rate of false positive spike detection (&lt; 0.003 spikes/sec), and a high temporal precision (about 3 msec) 3. This optical method of spike detection can be used to record neural activity in vitro and in anesthetized animals in vivo3,4.

Understanding the function of the vertebrate central nervous system requires recordings from many neurons because cortical function arises on the level of populations of neurons. Here we describe an optical method to record suprathreshold neural activity with single-cell and single-spike resolution, dithered random-access scanning. This method records somatic fluorescence calcium signals from up to 100 neurons with high temporal resolution. A maximum-likelihood algorithm deconvolves the underlying suprathreshold neural activity from the somatic fluorescence calcium signals. This method reliably detects spikes with high detection efficiency and a low rate of false positives and can be used to study neural populations in vitro and in vivo.

阈上的神经活动的单细胞和单穗的分辨率的光学的方法记录

Signaling of information in the vertebrate central nervous system is often carried by populations of neurons rather than individual neurons. Also ...

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees (Apis mellifera L.)

Honey bees (Apis mellifera L.) are eusocial insects and well known for their complex division of labor and associative learning capability1, 2. The worker bees spend the first half of their life inside the dark hive, where they are nursing the larvae or building the regular hexagonal combs for food (e.g. pollen or nectar) and brood3. The antennae are extraordinary multisensory feelers and play a pivotal role in various tactile mediated tasks4, including hive building5 and pattern recognition6. Later in life, each single bee leaves the hive to forage for food. Then a bee has to learn to discriminate profitable food sources, memorize their location, and communicate it to its nest mates7. Bees use different floral signals like colors or odors7, 8, but also tactile cues from the petal surface9 to form multisensory memories of the food source. Under laboratory conditions, bees can be trained in an appetitive learning paradigm to discriminate tactile object features, such as edges or grooves with their antennae10, 11, 12, 13. This learning paradigm is closely related to the classical olfactory conditioning of the proboscis extension response (PER) in harnessed bees14. The advantage of the tactile learning paradigm in the laboratory is the possibility of combining behavioral experiments on learning with various physiological measurements, including the analysis of the antennal movement pattern.

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.

In this protocol we show how to condition harnessed honey bees to tactile stimuli and introduce a 2D motion capture technique for analyzing the kinematics of fine-scale antennal sampling pattern.

触觉空调和取样策略在蜜蜂触角的运动分析（<em&gt;蜜蜂</em&gt; L.）

Honey bees (Apis mellifera L.) are eusocial insects and well known for their complex division of labor and associative learning capability1, 2. The worker ...

Whole Mount Immunolabeling of Olfactory Receptor Neurons in the Drosophila Antenna

Odorant molecules bind to their target receptors in a precise and coordinated manner. Each receptor recognizes a specific signal and relays this information to the brain. As such, determining how olfactory information is transferred to the brain, modifying both perception and behavior, merits investigation. Interestingly, there is emerging evidence that cellular transduction and transcriptional factors are involved in the diversification of olfactory receptor neuron. Here we provide a robust whole mount immunological labeling method to assay in vivo olfactory receptor neuron organization. Using this method, we identified all olfactory receptor neurons with anti-ELAV antibody, a known pan-neural marker and Or49a-mCD8::GFP, an olfactory receptor neuron specifically expressed in Nba neuron using anti-GFP antibody.

Whole Mount Immunolabeling of Olfactory Receptor Neurons in the Drosophila Antenna

Herein we describe the process of whole mount immunostaining of Drosophila antennae, which enables us to better understand the molecular mechanisms involved in the diversification of olfactory receptor neurons (ORN)s.

嗅觉受体神经元在全山免疫标记<em&gt;果蝇</em&gt;天线

Odorant molecules bind to their target receptors in a precise and coordinated manner. Each receptor recognizes a specific signal and relays this ...

Stereotaxic Infusion of Oligomeric Amyloid-beta into the Mouse Hippocampus

Alzheimer&#8217;s disease is a neurodegenerative disease affecting the aging population. A key neuropathological feature of the disease is the over-production of amyloid-beta and the deposition of amyloid-beta plaques in brain regions of the afflicted individuals. Throughout the years scientists have generated numerous Alzheimer&#8217;s disease mouse models that attempt to replicate the amyloid-beta pathology. Unfortunately, the mouse models only selectively mimic the disease features. Neuronal death, a prominent effect in the brains of Alzheimer&#8217;s disease patients, is noticeably lacking in these mice. Hence, we and others have employed a method of directly infusing soluble oligomeric species of amyloid-beta - forms of amyloid-beta that have been proven to be most toxic to neurons - stereotaxically into the brain. In this report we utilize male C57BL/6J mice to document this surgical technique of increasing amyloid-beta levels in a select brain region. The infusion target is the dentate gyrus of the hippocampus because this brain structure, along with the basal forebrain that is connected by the cholinergic circuit, represents one of the areas of degeneration in the disease. The results of elevating amyloid-beta in the dentate gyrus via stereotaxic infusion reveal increases in neuron loss in the dentate gyrus within 1 week, while there is a concomitant increase in cell death and cholinergic neuron loss in the vertical limb of the diagonal band of Broca of the basal forebrain. These effects are observed up to 2 weeks. Our data suggests that the current amyloid-beta infusion model provides an alternative mouse model to address region specific neuron death in a short-term basis. The advantage of this model is that amyloid-beta can be elevated in a spatial and temporal manner.

Here, we present a protocol for direct stereotaxic brain infusion of amyloid-beta. This methodology provides an alternative in vivo mouse model to address the short-term effects of amyloid-beta on brain neurons.