Name: generalized psychophysiological interaction (ppi) analysis of memory related connectivity in individuals at genetic risk for alzheimer's disease
Uploaded: 2017-11-14T12:30:00.000+00:00
Duration: 9 min 38 s
Description: 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

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="https://ecsource-jove-com.bdigitaluss.remotexs.co/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>
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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区域的增加和减少。种子区域的选择必须是假设驱动的, 因为 PPI 分析将在种子区域的活动在一个单变量的框架内通过任务诱导的认知环境进行优化。然后, PPI 框架可用于探讨种子区域活动如何与其他区域在响应特定任务上下文 (如内存编码或检索) 时或多或少地同步。因此, 组之间的差异仅限于种子和其他由特定任务阶段调制的区域之间的功能连接更改。
对 GLM 的透彻了解对于实施 PPI 分析至关重要。一个完整的, 组比较 ppi 研究有三层次的线性建模: 第一级 (预处理, 任务, 和运动建模), 中级 ppi 模型 (添加种子 timecourse 和任务交互 EVs) 和更高层次的组比较模型 (组参数估计的对比度)。在每个步骤中, 输出图像将用作下一步的输入。2012年提出并采用本研究的 gPPI 方法利用了 GLM 的特性, 以确保对比度特定于与感兴趣的任务阶段的交互3。在经典 PPI 中, 有两种条件的模型, 假设两个条件在基线的另一侧 (如果有基线条件)。gPPI 允许一个精确地对所有条件进行建模, 并且不对条件与基线条件的关系做出任何假设。任何 PPI 分析的另一个重要组成部分是适当选择种子区域。种子区域可以根据文献中的先验证据进行选择, 例如在目前的研究中, 海马被用作记忆任务的种子区。种子选择的另一种方法是选择在特定任务阶段活动显著增加的区域。使用这种方法, 种子区域的定义不是解剖的, 而是在单变量激活映射中使用一组阈体。通过这种方法来选择种子, ppi 分析避免了循环, 因为任务的主要作用是核算的, 而 ppi 只揭示了不同于 (超过或高于) 任务主要效果的效果。
自 PPI 首次提出以来, 功能性连接的概念, 空间遥远的脑区已成为广泛接受。通过使用静止状态 fMRI, 它已经显示出大脑有内在的网络, 或者是在静止的功能连接的区域的集合。因此, 静止状态的 fMRI 研究经常调查功能连通性, 同样的术语用于 PPI 研究。然而, 对功能连通性的解释不同于静止状态的 fMRI 和 PPI 研究。根据定义, PPI 结果是任务和种子区域之间的相互作用的解释效果, 任务设计、种子 timecourse 或任何其他混淆变量4都无法解释。在静止状态 fMRI 中, 网络活动的差异可能是由特定区域之间的连接变化或网络活动的总体变化引起的。因此, 如果研究的目的是比较两组之间功能连通性的变化, PPI 方法就更好了。相比之下, 如果研究的目的是描述两组之间内在连通性的差异, 静止状态 fMRI 分析就更好了。
最初的 PPI 框架的一个主要限制是缺少方法4中固有的统计能力。由于 PPI 术语 (EV) 是使用两个也包括在模型中的 EVs 创建的, 因此很可能与这两者都相关。在 GLM 中, 可由多个预测器或 ev 解释的方差没有分配给单个 ev。因此, ppi 术语只具有检测无法由任务或种子 timecourse 解释的效果的能力, 它们都与 ppi 术语相关。因此, PPI 分析中可能会出现假阴性。gPPI, 但是, 已被证明, 以尽量减少假底片的数量和更敏感的小效果大小发现3,39。
PPI 可以发现两个区域之间的功能连接中的任务相关更改, 但它无法确定一个区域中的活动是否会导致另一地区的活动发生变化。换言之, PPI 分析不能用来探索功能性连接变化的因果关系。其他方法, 如动态因果模型, 更适合于分析功能数据中的因果关系40。PPI 分析可以通过这些技术来告知实验的设计。总之, PPI 是一个有用的方法来检查任务特定的变化的功能连通性的相关种子区域和比较这些变化组之间。PPI 研究的结果可使人们更好地了解健康、疾病和疾病风险中功能连通性的动态性质。

"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><tbody><tr><td>3T manetic resonance imaging scanner</td><td>Siemens Medical Solutions</td><td>MAGNETOM Trio, A Tim System</td><td>3T MRI Scanner</td></tr><tr><td>FSL (FMRIB Software Library)</td><td>Oxford University</td><td>Version 6.0</td><td>Functional Imaging Processing Software</td></tr><tr><td>AFNI (Analysis of Functional Neuroimaging)</td><td>National Institute of Mental Health, National Institutes of Health</td><td>Any version after May 2015</td><td>Functional Imaging Processing Software</td></tr><tr><td>SPM8 (Statistical Parametric Mapping)</td><td>University College of London</td><td>SPM8</td><td>Functional Imaging Processing Software</td></tr><tr><td>Matlab Software</td><td>The Mathworks, Inc</td><td>Version R2012a</td><td>Computing Software</td></tr><tr><td>SDS Software</td><td>Applied Biosystems, Inc</td><td>7900HT Fast Real-Time PCR System</td><td>Real Time PCR</td></tr><tr><td>Taqman Assays</td><td>ThermoFisher Scientific</td><td>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与前或后海马种子的任何 APOEε4载体或比较。内部 PPI 分析显示, APOEε4载流子在任务条件和海马次区域的功能连通性显著下降 (图 2)。在 APOEε4比较中, 在编码时只观察到后海马的功能连通性显著降低 (图 2)。正面和负面 PPI 图显示了 APOEε4载体和比较之间的差异在如何在记忆任务的海马功能连接变化。为了确定差异是否具有统计学意义, 有必要直接比较四结果38中的每一个组。
为了简洁起见, 小组比较结果显示载脂蛋白介导的差异只为一个地区和任务阶段, 前海马在检索期间, 这里提出 (比较和 #62; 载体,图 3)。在检索过程中, 组内观察到的海马前连接改变的差异 (图 2) 在双侧缘回、右角回和右前的组间差异上有显著性。
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/55394/55394fig2.jpg"> 
图 2: 海马种子与任务相关负功能连接更改映射.群平均任务依赖负功能连通性的冠状和轴向观 APOEε4比较和载体内的海马分次区域的变化。与任务相关的连通性减少与前海马种子显示在上部板。下部的面板显示任务相关的连通性随后海马的减少而减小。地图被值在 z = 2.3, 在 p 和 #60 校正了群; 0.05。体会议门限在 APOEε4比较 (在红色) 和载体 (在绿色) 被覆盖。此图已被转载, 并具有12权限。<a href="https://ecsource-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55394/55394fig2large.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/55394/55394fig3.jpg"> 
图 3:在检索过程中, ε4载体和比较之间的前海马种子连接性变化之间的差异。在检索过程中, APOEε4载体和比较在左缘回 (深蓝色)、右缘/角交界处 (橙色) 以及右前 (紫色) 之间存在显著差异。这两个样本 t 检验的结果是值的, 以揭示在阿尔法和 #60 的重要性; 0.05 具有 voxelwise 阈值的 p 和 #60; 0.005。每个簇的峰值坐标在 MNI 空间中报告, 在 x、y、z 平面 (mm)。为了说明各组之间的差异的方向和大小, 每个簇中的参数估计的对比是按组绘制的。红色水平线显示为零, 并突出表明, 在这些区域中, 在检索过程中, 载流子对前海马区的功能连通性下降。方框内的带代表中位数, 而方框的上下边缘分别代表第一和第三四。胡须延伸到分范围的1.5 倍。此范围之外的数据点被绘制为异常值。此图已被转载, 并具有12权限。<a href="https://ecsource-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55394/55394fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>

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

Universidad San Sebastian

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

Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease

Our laboratory uses event-related EEG potentials (ERPs) to understand and support behavioral investigations of episodic memory in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). Whereas behavioral data inform us about the patients' performance, ERPs allow us to record discrete changes in brain activity. Further, ERPs can give us insight into the onset, duration, and interaction of independent cognitive processes associated with memory retrieval. In patient populations, these types of studies are used to examine which aspects of memory are impaired and which remain relatively intact compared to a control population. The methodology for collecting ERP data from a vulnerable patient population while these participants perform a recognition memory task is reviewed. This protocol includes participant preparation, quality assurance, data acquisition, and data analysis. In addition to basic setup and acquisition, we will also demonstrate localization techniques to obtain greater spatial resolution and source localization using high-density (128 channel) electrode arrays.

The methodology for collecting high-density event-related potential data while patients with Alzheimer's disease perform a recognition memory task is reviewed. This protocol will include subject preparation, quality assurance, data acquisition, and data analysis.

情节记忆的特点，采用事件相关电位研究在阿尔茨海默氏症患者

Our laboratory uses event-related EEG potentials (ERPs) to understand and support behavioral investigations of episodic memory in patients with amnestic ...

科研

医学

The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease

This protocol describes the administration of the 4 Mountains Test (4MT), a short test of spatial memory, in which memory for the topographical layout of four mountains within a computer-generated landscape is tested using a delayed match-to-sample paradigm. Allocentric spatial memory is assessed by altering the viewpoint, colors and textures between the initially presented and target images.
Allocentric spatial memory is a key function of the hippocampus, one of the earliest brain regions to be affected in Alzheimer&#39;s disease (AD) and impairment of hippocampal function predates the onset of dementia. It was hypothesized that performance on the 4MT would aid the diagnosis of predementia AD, which manifests clinically as Mild Cognitive Impairment (MCI).
The 4MT was applied to patients with MCI, stratified further based on cerebrospinal fluid (CSF) AD biomarker status (10 MCI biomarker positive, 9 MCI biomarker negative), and with mild AD dementia, as well as healthy controls. Comparator tests included tests of episodic memory and attention widely accepted as sensitive measures of early AD. Behavioral data were correlated with quantitative MRI measures of the hippocampus, precuneus and posterior cingulate gyrus.
4MT scores were significantly different between the two MCI groups (p = 0.001), with a test score of &#8804;8/15 associated with 100% sensitivity and 78% specificity for the classification of MCI with positive AD biomarkers, i.e., predementia AD. 4MT test scores correlated with hippocampal volume (r = 0.42) and cortical thickness of the precuneus (r = 0.55).
In conclusion, the 4MT is effective in identifying the early stages of AD. The short duration, easy application and scoring, and favorable psychometric properties of the 4MT fulfil the need for a simple but accurate diagnostic test for predementia AD.

This article describes the 4 Mountains Test (4MT), a hippocampus-dependent test of working allocentric spatial memory. The hippocampus is affected early in Alzheimer's disease (AD) and this article outlines the 4MT methodology and results of patient testing, which demonstrates the value of the 4MT in the diagnosis of pre-dementia AD.

四山测试:空间记忆的一个简短的测试与高灵敏度的预痴呆诊断阿尔茨海默氏病

This protocol describes the administration of the 4 Mountains Test (4MT), a short test of spatial memory, in which memory for the topographical layout of ...

行为学

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Genome-wide association studies (GWAS) have successfully identified hundreds of genomic loci that are associated with human traits and disease. However, because the majority of the genome-wide significant (GWS) loci fall onto the non-coding genome, the functional impact of many remain unknown. Three-dimensional chromatin interactions identified by Hi-C or its derivatives can provide useful tools to annotate these loci by linking non-coding variants to their actionable genes. Here, we outline a protocol to map GWAS non-coding variants to their putative genes using Alzheimer&#39;s disease (AD) GWAS and Hi-C datasets from human adult brain tissue. Putative causal single-nucleotide polymorphisms (SNPs) are identified by application of fine-mapping algorithms. SNPs are then mapped to their putative target genes using enhancer-promoter interactions based on Hi-C. The resulting gene set represents AD risk genes, as they are potentially regulated by AD risk variants. To garner further biological insights into molecular mechanisms underlying AD, we characterize AD risk genes using developmental brain expression data and brain single-cell expression profiles. This protocol can be expanded to any GWAS and Hi-C datasets to identify putative target genes and molecular mechanisms underlying various human traits and diseases.

We present a protocol to identify functional implications of non-coding variants identified by genome-wide association studies (GWAS) using three-dimensional chromatin interactions.

利用染色质配置的计算分析，将阿尔茨海默氏病变异体映射到其目标基因

Genome-wide association studies (GWAS) have successfully identified hundreds of genomic loci that are associated with human traits and disease. However, ...

遗传学

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage

Multiple factors&#8212;such as aging and genes&#8212;are frequently associated with cognitive decline. Genetically modified mouse models of cognitive decline, such as Alzheimer&#39;s disease (AD), have become a promising tool to elucidate the underlying mechanisms and promote the therapeutic advances. An important step is the validation and characterization of expected behavioral abnormality in the models, in the case of AD, cognitive decline. The long-term behavioral investigations of laboratory animals to study the effect of aging demand substantial efforts from researchers. The IntelliCage system is a high-throughput and cost-effective test battery for mice that eliminates the need for daily human handling. Here, we describe how the system is utilized in the long-term phenotyping of a genetic Alzheimer&#39;s disease model, specifically focusing on the cognitive functions. The experiment employs repeated battery of tests that assess spatial learning and executive functions. This cost-effective age-dependent phenotyping allows us to identify the transient and/or permanent effects of genes on various cognitive aspects.

This paper describes a protocol for cognitive assessments for genetic models of the Alzheimer&#39;s disease using the IntelliCage system, which is a high throughput automated behavioral monitoring system with operant conditioning.

基于 IntelliCage 的阿尔茨海默病多基因模型认知功能的自动、长期行为检测

Multiple factors&#8212;such as aging and genes&#8212;are frequently associated with cognitive decline. Genetically modified mouse models of cognitive ...

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are exacerbated when significant brain atrophy and age-related white matter changes (e.g. Leukoaraiosis) are present. Lesion Explorer (LE) is an accurate and reliable neuroimaging pipeline specifically developed to address such issues commonly observed on MRI of Alzheimer's disease and normal elderly. The pipeline is a complex set of semi-automatic procedures which has been previously validated in a series of internal and external reliability tests1,2. However, LE's accuracy and reliability is highly dependent on properly trained manual operators to execute commands, identify distinct anatomical landmarks, and manually edit/verify various computer-generated segmentation outputs.
LE can be divided into 3 main components, each requiring a set of commands and manual operations: 1) Brain-Sizer, 2) SABRE, and 3) Lesion-Seg. Brain-Sizer's manual operations involve editing of the automatic skull-stripped total intracranial vault (TIV) extraction mask, designation of ventricular cerebrospinal fluid (vCSF), and removal of subtentorial structures. The SABRE component requires checking of image alignment along the anterior and posterior commissure (ACPC) plane, and identification of several anatomical landmarks required for regional parcellation. Finally, the Lesion-Seg component involves manual checking of the automatic lesion segmentation of subcortical hyperintensities (SH) for false positive errors.
While on-site training of the LE pipeline is preferable, readily available visual teaching tools with interactive training images are a viable alternative. Developed to ensure a high degree of accuracy and reliability, the following is a step-by-step, video-guided, standardized protocol for LE's manual procedures.

Lesion Explorer (LE) is a semi-automatic, image-processing pipeline developed to obtain regional brain tissue and subcortical hyperintensity lesion volumetrics from structural MRI of Alzheimer's disease and normal elderly. To ensure a high level of accuracy and reliability, the following is a video-guided, standardized protocol for LE's manual procedures.

病变资源管理器：一个视频引导，标准化为老年痴呆症准确，可靠的MRI衍生Volumetrics和正常老年人协议

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are ...

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard scores from paper and pencil symptom- measures traditionally used to track disease progression are too coarse (discrete) to capture the granularity of the clinical phenomena under consideration, in the face of tremendous symptom diversity. For this reason, sensors, wearables, and mobile devices are increasingly incorporated into PD research and routine care. These digital measures, while more precise, yield data that are less standardized and interpretable than traditional measures, and consequently, the two types of data remain largely siloed. Both of these issues present barriers to the broad clinical application of the field&#8217;s most precise assessment tools. This protocol addresses both problems. Using traditional tasks to measure cognition and motor control, we test the participant, while co-registering biophysical signals unobtrusively using wearables. We then integrate the scores from traditional paper-and-pencil methods with the digital data that we continuously register. We offer a new standardized data type and unifying statistical platform that enables the dynamic tracking of change in the person&#8217;s stochastic signatures under different conditions that probe different functional levels of neuromotor control, ranging from voluntary to autonomic. The protocol and standardized statistical framework offer dynamic digital biomarkers of physical and cognitive function in PD that correspond to validated clinical scales, while significantly improving their precision.&#160;

This protocol offers a digitization of portions of traditional clinical tasks commonly used to measure cognition and motor control in Parkinson&#8217;s disease. Clinical tasks are digitized while biophysical rhythms are co-registered from different functional levels of the nervous systems, ranging from voluntary, spontaneous, automatic to autonomic.

帕金森病中运动与认知功能的动态数字生物标志物

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard ...

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Complementary structural and functional neuroimaging techniques used to examine the Default Mode Network (DMN) could potentially improve assessments of psychiatric illness severity and provide added validity to the clinical diagnostic process. Recent neuroimaging research suggests that DMN processes may be disrupted in a number of stress-related psychiatric illnesses, such as posttraumatic stress disorder (PTSD).
Although specific DMN functions remain under investigation, it is generally thought to be involved in introspection and self-processing. In healthy individuals it exhibits greatest activity during periods of rest, with less activity, observed as deactivation, during cognitive tasks, e.g., working memory. This network consists of the medial prefrontal cortex, posterior cingulate cortex/precuneus, lateral parietal cortices and medial temporal regions.
Multiple functional and structural imaging approaches have been developed to study the DMN. These have unprecedented potential to further the understanding of the function and dysfunction of this network. Functional approaches, such as the evaluation of resting state connectivity and task-induced deactivation, have excellent potential to identify targeted neurocognitive and neuroaffective (functional) diagnostic markers and may indicate illness severity and prognosis with increased accuracy or specificity. Structural approaches, such as evaluation of morphometry and connectivity, may provide unique markers of etiology and long-term outcomes. Combined, functional and structural methods provide strong multimodal, complementary and synergistic approaches to develop valid DMN-based imaging phenotypes in stress-related psychiatric conditions. This protocol aims to integrate these methods to investigate DMN structure and function in PTSD, relating findings to illness severity and relevant clinical factors.

This protocol describes the complementary neuroimaging techniques of resting state structural connectivity, task-induced deactivation, and structural connectivity analyses to examine the default network in post-traumatic stress disorder. The use of synergistic methods could potentially lead to improved diagnostics and assessments of severity, outcome, and other relevant clinical factors.

发展中的默认模式网络的神经影像学表型在创伤后应激障碍:整合静息状态下，工作记忆和结构连接

Complementary structural and functional neuroimaging techniques used to examine the Default Mode Network (DMN) could potentially improve assessments of ...

Motor and Hippocampal Dependent Spatial Learning and Reference Memory Assessment in a Transgenic Rat Model of Alzheimer's Disease with Stroke

Alzheimer&#39;s disease (AD) is a debilitating neurodegenerative disease that results in neurodegeneration and memory loss. While age is a major risk factor for AD, stroke has also been implicated as a risk factor and an exacerbating factor. The co-morbidity of stroke and AD results in worsened stroke-related motor control and AD-related cognitive deficits when compared to each condition alone. To model the combined condition of stroke and AD, a novel transgenic rat model of AD, with a mutated form of amyloid precursor protein (a key protein involved in the development of AD) incorporated into its DNA, is given a small unilateral striatal stroke.
For a model with the combination of both stroke and AD, behavioral tests that assess stroke-related motor control, locomotion and AD-related cognitive function must be implemented. The cylinder task involves a cost-efficient, multipurpose apparatus that assesses spontaneous forelimb motor use. In this task, a rat is placed in a cylindrical apparatus, where the rat will spontaneously rear and contact the wall of the cylinder with its forelimbs. These contacts are considered forelimb motor use and quantified during video analysis after testing. Another cost-efficient motor task implemented is the beam-walk task, which assesses forelimb control, hindlimb control and locomotion. This task involves a rat walking across a wooden beam allowing for the assessment of limb motor control through analysis of forelimb slips, hindlimb slips and falls. Assessment of learning and memory is completed with Morris water maze for this behavioral paradigm. The protocol starts with spatial learning, whereby the rat locates a stationary hidden platform. After spatial learning, the platform is removed and both short-term and long-term spatial reference memory is assessed. All three of these tasks are sensitive to behavioral differences and completed within 28 days for this model, making this paradigm time-efficient and cost-efficient.

To investigate the co-morbid Alzheimer&#39;s disease (AD) and stroke condition in a novel model, three behavior tasks are described that assess both motor control and cognitive behaviors. These tasks include the beam-walk task, cylinder task and Morris water maze.

汽车和海马依赖空间学习和记忆参考评估与中风阿尔茨海默氏病的转基因大鼠模型

Alzheimer&#39;s disease (AD) is a debilitating neurodegenerative disease that results in neurodegeneration and memory loss. While age is a major risk ...

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Impaired functional connectivity in the Default Mode Network (DMN) may be involved in the progression of Alzheimer&#39;s Disease (AD). The Posterior Cingulate Cortex (PCC) is a potential imaging marker for monitoring the progression of AD. Previous studies did not focus on the functional connectivity between the PCC and nodes in regions outside the DMN, but our study is an effort to explore these overlooked functional connections. For collecting data, we used functional Magnetic Resonance Imaging (fMRI) and Granger Causality Analysis (GCA). fMRI provides a non-invasive method for studying the dynamic interactions between the different brain regions. GCA is a statistical hypothesis test for determining whether one-time series is useful in forecasting another. In simple terms, it is judged by comparing the &#34;Known all the information on the last moment, the distribution of the probability of X at this time&#34; and the &#34;Known all the information on the last moment except Y, the distribution of the probability of X at this time&#34;, to determine whether there is a causal relationship between Y and X. This definition is based on the complete information source and stationary chronological sequence. The main step of this analysis is to use X and Y to establish the regression equation and draw a causal relationship by a hypothetical test. Since GCA can measure causal effects, we used it to investigate the anisotropy of the functional connectivity and explore the hub function of the PCC. Here, we screened 116 participants for MRI scanning, and after preprocessing the data obtained from neuroimaging, we used GCA to derive the causal relationship of each node. Finally, we concluded that the directed connection is significantly different between the Mild Cognitive Impairment (MCI) and AD groups, both from the PCC to the whole brain and from the whole brain to the PCC.

Based on resting-state functional magnetic resonance imaging with Granger causality analysis, we investigated the alterations in the directed functional connectivity between the posterior cingulate cortex and whole brain in patients with Alzheimer&#39;s Disease (AD), patients with Mild Cognitive Impairment (MCI), and healthy controls.

在阿尔茨海默病和轻度认知功能损害的定向功能连接格兰杰因果关系分析中的应用

Impaired functional connectivity in the Default Mode Network (DMN) may be involved in the progression of Alzheimer&#39;s Disease (AD). The Posterior ...

Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains

In a constantly aging population, the prevalence of neurodegenerative disorders is expected to rise. Understanding disease mechanisms is the key to find preventive and curative measures. The most effective way to achieve this is through direct examination of diseased and healthy brain tissue. The authors present a protocol to obtain, process, characterize and store good quality brain tissue donated by individuals registered in an antemortem brain donation program. The donation program includes a face-to-face empathic approach to people, a collection of complementary clinical, biological, social and lifestyle information and serial multi-dimensional assessments over time to track individual trajectories of normal aging and cognitive decline. Since many neurological diseases are asymmetrical, our brain bank offers a unique protocol for slicing fresh specimens. Brain sections of both hemispheres are alternately frozen (at -80 &#176;C) or fixed in formalin; a fixed slice on one hemisphere corresponds to a frozen one on the other hemisphere. With this approach, a complete histological characterization of all frozen material can be obtained, and omics studies can be performed on histologically well-defined tissues from both hemispheres thus offering a more complete assessment of neurodegenerative disease mechanisms. Correct and definite diagnosis of these diseases can only be achieved by combining the clinical syndrome with the neuropathological evaluation, which often adds important etiological clues necessary to interpret the pathogenesis. This method can be time consuming, expensive and limited as it only covers a limited geographical area. Regardless of its limitations, the high degree of characterization it provides can be rewarding. Our ultimate goal is to establish the first Italian Brain Bank, all the while emphasizing the importance of neuropathologically verified epidemiological studies.

This protocol describes a method to track individual brain-aging trajectories through a brain donation program and proper characterization of brains. Brain donors are involved in a long-term longitudinal study including serial multi-dimensional assessments. The protocol contains a detailed description of brain processing and an accurate diagnostic methodology.

Abbiategrasso 脑库协议，用于收集、处理和描述老化的大脑

In a constantly aging population, the prevalence of neurodegenerative disorders is expected to rise. Understanding disease mechanisms is the key to find ...