我们感谢预测高清调查员, 特别是 Pauslen 博士和爱荷华州大学的简·约翰逊博士, 他们慷慨地分享了 MRI 数据, 并对数据分析和结果进行了建设性的讨论。
这项工作由 NIH 赠款 R21 NS098018、P50 NS16375、NS40068、R01 NS086888、R01 NS084957、P41 EB015909、P41 EB015909、R01 EB000975、R01 EB008171 和 U01 NS082085 支持。

1. 基于 Atlas 的全脑分割
<ol><li>数据准备
	<ol>
<li>转换三维 (3D) T1-weighted 图像, 通常获得与 MPRAGE (磁化准备快速梯度回声) 序列, 从供应商特定的 DICOM (数字成像和通信) 格式, 以分析格式。请注意, 云计算需要将用户的数据传输到远程群集。根据《健康保险可移植性和责任法》 (HIPPA), 从图像文件中删除患者的个人身份信息。 注: MRICloud 提供了一个 DICOM 到分析转换器 (https://braingps.mricloud.org/t1prep), 用于文件格式转换以及个人健康信息的取消标识。<ol>
<li>双击打开 Dcm2Analyze.exe。将打开一个弹出窗口 (图 1A)。</li>
			<li>指定输入 DICOM 数据目录路径作为输入, 并分析图像路径和文件名作为输出。</li>
			<li>单击 "转到" 以完成转换。 注意: 需要对每个患者执行转换, 并且在一个研究文件夹中输出所有转换后的图像以进行批处理 (1.2.2.1) 是有益的。</li>
		</ol>
</li>
	</ol></li>
	<li>基于多地图集的 T1 图像分割方法, 采用 MRICloud19。
	<ol>
<li>登录 "大脑 GPS" (注册为首次用户) 从 https://mricloud.org。从主菜单中选择 "分割" 工具 (图 1B)。在 "分割" 下面有两个应用程序编程接口 (API) 选项: "T1-MultiAtlas", 用于单 T1 图像分割, 以及批处理的 "T1-MultiAtlas 批次"。改变点分析需要大量的人口数据, 因此, 批处理往往是选择。</li>
		<li>在 "T1-MultiAtlas 批处理" API 上提交作业。<ol>
<li>将多个分析图像文件压缩到 zip 文件中。单击图 1B中的 "+. zip" 以上载 zip 文件。 注意: 当前的云门户将每个 zip 文件夹中的图像数限制为30。可以将大型数据集分成多个要处理的 zip 文件夹。可以为每个 zip 文件提供特殊的请求以容纳更多图像。将来, 当有更多的远程计算资源可用时, 我们可能会增加有限的数字, 甚至取消限制。</li>
			<li>填写图 1B中所需的字段。<ol>
<li>处理服务器: 选择 "计算解剖学科学网关"。</li>
				<li>切片类型: 选择从 "矢状", "轴向", 或 "矢状转换为轴向"。 注: "矢状" 指的是在前-后和头足方向上以平面视角获得的矢状方向图像。"轴向" 指的是从左向右和前后方向的平面内视图获得的轴向方向的图像。"矢状转换为轴向" 是指最初以轴向方向获得的图像, 后来转换成矢状图像 (这些通常是长颈区的 "轴向" 图像)。</li>
				<li>多地图集库: 选择最接近年龄范围的地图集库到用户数据, 优化分割精度。我们提供了预先生成的多地图集18的不同年龄范围, 如 "儿科 4-8yr", "儿科 8-12yr", "成人 22-50", 和 "成人 50-90", 以及不同的阿特拉斯版本。有关 atlas 版本的信息, 可以在 https://braingps.mricloud.org/atlasrepo 中找到。</li>
			</ol>
</li>
		</ol>
</li>
		<li>通过 "我的作业状态" 检查作业状态 (图 1C)。完成作业后, 将出现一个 "下载结果" 按钮, 允许用户将分割结果下载为. zip 文件。</li>
		<li>可视化结果。对于单个主题, 从分割结果获得的卷可以在线可视化 (对于批处理结果来说不可行)。<ol>
<li>单击图 1C中的 "查看结果" 按钮。网页将转向可视化界面 (图 1D)。在 T1-weighted 解剖图像上, 分割图的轴向、矢状、冠状均有重叠。3D. 分段脑结构的渲染显示在左上窗。叠加分割图的颜色表示结构体积的 z 分。</li>
			<li>调整可视化选项, 包括叠加开/关、覆盖的不透明度、放大和缩小以及从图 1D右上角面板中的切片位置。</li>
			<li>把最好的分割包裹分成不同的粒度。在我们的地图集, 我们定义了五级的粒度与两种类型的本体关系18,30。层次结构多级解剖定义的树视图显示在左下面板中 (图 1D)。单击级别编号以在相应的本体级别展开结构。分割映射将同时切换到相应的本体层次。 注: 双月刊 MRICloud 研讨会是约翰霍普金斯大学医学院的帮助提供上述在线操作的亲身教程。车间信息可从 https://braingps.mricloud.org/workshops 中找到。</li>
		</ol>
</li>
		<li>下载细分结果进行后续分析。将结果解压缩到用户研究目录, 例如, 将每个研究目录中的所有主题的分割结果放在一起, 每个主题文件夹中都有它们。 注意: 结果包括</li>
	</ol>
<ul style="margin-left: 40px;">
<li>示例. img: 在轴向方向的原始 T1 图像。</li>
		<li>example_MNI: T1 图像在 MNI 空间 Talairach 坐标之后。</li>
		<li>example_7Labels. img 和 example_7Labels_MNI: 在本机和 MNI 空间, 分别有7包裹 (灰质、白质、脑脊液、侧脑室、头骨和背景) 的粗分割。</li>
		<li>example_283Labels_M2 和 example_283Labels_M2_MNI. img: 在本机和 MNI 空间分别有283个包裹 (阿特拉斯版本 V9B) 的精细分割。标签的确切数量取决于阿特拉斯版本。</li>
		<li>example_corrected_MNI_stats 和 example_MNI_stats: 本地和 MNI 空间中每个脑包裹的体积统计。</li>
		<li>multilevel_lookup_table: 多级本体定义的脑包裹。</li>
	</ul></li>
	<li>执行批处理以获得人口中的脑容量。使用内部的 Matlab (www.mathworks.com) 批处理脚本从单个结果文件夹中提取脑卷, 并将所有科目的容量数据与电子表格相结合。图形用户界面 (GUI) 用于指定输入和输出。
	<ol>
<li>打开 Matlab.exe, 运行主图, GUI 将弹出 (图 2A)。</li>
		<li>在 "从 MRICloud 的 T1 卷提取" 面板 (图 2A的上部面板) 中, 指定输入, 包括下载的分割结果保存的学习目录 (见 1.2.5);和多级查找表文件路径和文件名。</li>
		<li>指定要写入卷数据的输出电子表格文件路径和文件名。</li>
		<li>单击 "提取卷" 按钮以运行分析。可以在用户定义的电子表格中检查结果。 注意: 此外, 还开发了一个 R 包来处理 MRICloud 输出, 并进行进一步的统计分析, 由布赖恩 Caffo31提供。软件包可以从 https://github.com/bcaffo/MRIcloudT1volumetrics 下载。</li>
	</ol></li>
</ol>2. 容积数据的变点分析
注: 请将更改点模型的理论和数学细节参考到我们以前的出版物21、24、23。简单地说, 对于 HD 数据, 建立一个回归模型来定义一个关键的变化点 (在 cap 评分), 从这开始, 线性回归之间的上限和体积成为统计学意义, 而纠正的影响, 病人的年龄,性别和颅内容积。内部的 Matlab 脚本, 连同 GUI (图 2A, 下面板), 被开发来计算各个结构的变化点, 并进行统计分析。用户可根据要求使用这些脚本。
<ol><li>
计算单个脑结构的变化点。
	<ol>
<li>在图 2A的 "更改点分析" 面板中, 指定要根据1.3 生成的多级卷电子表格的文件路径和名称。</li>
		<li>指定输出文本文件的文件路径和名称, 将为其写入更改点结果。</li>
		<li>选择下拉框 (级别 1-5) 中的粒度和本体定义类型的级别, 将执行更改点分析。</li>
		<li>单击 "计算更改点" 按钮执行更改点分析, 如参考23中所述, 结果更改点将保存在输出文本文件中。</li>
	</ol>
</li>
	<li>
对变化点的统计评价。
	<ol>
<li>在 Matlab GUI (图 2A、下面板) 中, 指定统计测试的参数, 包括置换的数量 (默认值 1万)、引导次数 (默认值 1万) 和 p 值阈值 (罗斯福更正后, 默认为 0.05)。</li>
		<li>单击 "统计测试" 按钮运行测试。在此步骤之后, p 值 (由置换测试), 在错误发现率 (罗斯福) 更正之前和之后, 以及标准偏差和95% 置信区间 (通过引导) 的变化点将被写入输出文本文件作为额外的列.请参阅23、24中统计测试程序的详细信息。</li>
	</ol>
</li>
	<li>生成更改点映射 (可选)。在 Matlab GUI (图 2A, 下面板) 中, 单击 "映射更改点" 按钮生成更改点映射。统计学意义上的变化点值将映射到 MNI 空间解剖图像, 以可视化空间格局。此映射可以在不同的粒度级别进行, 具体取决于2.1 中指定的解剖级别。更改点映射可以用 MRIcro (图 2B) (http://www.cabi.gatech.edu/mricro/mricro/) 覆盖在 T1-weighted 图像上。</li>
</ol>

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作者没有什么可透露的。

如本文所示, 使用我们的在线平台 MRICloud 可以方便地实现脑 MRI 的全脑分割。T1-weighted MRI 为基础的容积标记已表明是强健和敏感的一系列神经退行性疾病1,2,3。容积测量用于各种下游分析, 如数学建模、特征选择和分类分析, 以协助临床诊断和预后。脑容量的变化点分析可以定量描述疾病进展过程中的脑萎缩。这种统计分析采用了一个带有非线性分量的回归模型来定义脑萎缩过程中疾病指数的变化点,例如HD 评分. 与传统的群体分析相比, 在大多数情况下广泛采用现有的研究, 改变点模型寻求确切的发病点的脑萎缩, 因此, 提供了更多的具体信息的脑变性。结合自动全脑分割管线, 可以得到变化点的空间图, 揭示了 HD 疾病进展的时空格局。这在 HD 的 premanifest 阶段尤为重要, 因为治疗干预是最有效的。
在基于多地图集的图像分割中, 提供了多粒的脑分割, 使容量分析具有灵活性和可伸缩性。例如, 对于阿尔茨海默氏症, 几个小的大脑结构是特别感兴趣的, 如海马, 杏仁核, 或颞皮层的细分;而对于额颞部痴呆症, 大脑的检查在相对较粗的尺度, 如整个额叶和颞叶。根据我们以前的研究33, 分割精度和重现性可能会在高粒度下被破坏。此外, 多重比较的统计挑战将以更高的粒度增加, 如图 5所示。此外, 根据研究的年龄范围和疾病类型, 对 atlas 库进行适当的选择, 对于分割精度18、32非常重要。更改点分析需要相对大量的数据来执行回归分析,例如, 通常需要基于经验经验的 n > 50。对变化点结果的解释很大程度上依赖于统计测试, 因为模型将为任何动态数据系列产生一个变化点, 这有时是微不足道的。我们使用置换测试来评估加法的变化点是否能显著降低模型的残差, 并利用引导操作来减轻异常的影响。
根据特定的应用程序, 更改点模型可以有变化。例如, 疾病指数, 这是 HD 患者的 CAP 评分, 可以取代其他临床变量, 或简单的年龄。其他变量也可以包括在模型中, 如成像协议23和其他需要核算的因素。此外, 该模型适用于各种类型的生物标志物以外的容量标记, 包括其他成像标记 (从不同的对比机制, 如扩散, 灌注, 或功能磁共振) 和非成像标记。此外, 该模型的数学公式可以根据不同的假设和假设进行调整。举例来说, 模型可能涉及基线线性回归之前的变化点和一个额外的线性回归后发生的变化点, 如建议在参考24。
在神经退行性疾病的临床和基础研究中, 采用基于 atlas 的全脑分割的改变点分析方法, 可广泛应用。然而, 所提出的技术还是有局限性的。生物标志物的动态变化可能不是线性的, 因此, 在目前的变化点模型中使用的线性分量可能不是最优的。广义线性模型有可能被集成来处理非线性情况。其次, 改变点分析需要大量的纵向和/或横断面数据, 例如多中心预测-HD 数据在本研究中使用。不同的图像采集协议, 从多中心研究或纵向研究, 可能会强加偏差的体积测量。与生物效应 (如年龄和病理34) 相比, 我们的多地图集图像分割管线显示出相对于协议差异的鲁棒性。不过, 可能需要事先删除协议差异, 或者将协议效果作为23中描述的更改点模型中的变量之一。目前, 下游容量分析不包括在 MRICloud 中, 用户将需要从分割映射中提取卷, 或者使用我们的 Matlab 工具箱来执行多粒度卷分析。如果将此脱机处理步骤确定为用户的常见请求, 我们将来可能会将其集成到云管道中。
在神经退行性疾病的临床和基础研究中, 采用基于 atlas 的全脑分割的改变点分析方法, 可广泛应用。

磁共振成像 (MRI) 大大提高了我们的能力, 检查大脑解剖和功能的神经退行性疾病1,2,3。T1-weighted 结构 MRI 是常规临床实践中应用最广泛的影像学工具之一, 用于评估脑解剖学及相关病理学。对高分辨率 T1-weighted 图像进行定量分析, 为测量脑变性过程中的解剖变化提供了有用的标志。特别是, 基于分割的量化方法有效地将图像维数从体素水平 ((106)) 降低到解剖结构水平 ((102)), 用于高通量神经信息学4,5. 利用基于 atlas 的方法6、7、8、9将预定义的解剖标签从地图集映射到患者图像, 可以实现自动脑分割。.在基于地图集的方法中, 多地图集算法10、11、12、13、14具有较高的分割精度和鲁棒性。我们集团开发了一个全自动 T1 多地图集分割管线, 具有先进的 diffeomorphic 图像配准算法15, 多阿特拉斯融合方法16,17, 丰富的多地图集库18. 自2015年以来, 该管道已分布在一个云计算平台上, MRICloud19, 并已用于研究神经退行性疾病, 如阿尔茨海默病 (AD)20、21、初级渐进性失语症22, 亨廷顿氏病23。
一旦高分辨率图像被分割成脑结构, 区域特征, 如体积, 可以用来建立数学模型来表征神经解剖学的变化。本文以纵向和/或横断面 MRI 资料为基础, 对时间顺序进行了分析, 并对其进行了统计学意义上的变化。这一统计模型是第一次开发, 以量化基于形状的 diffeomorphometry 年龄在 AD 患者21,24;它后来被改编为研究亨廷顿氏病 (HD) 的大脑结构变化, 以及描述新生儿大脑的发育变化25。在 HD 患者中, 对 cag 年龄产品 (CAP) 评分的变化点进行了界定, 以此作为HTT 26中 cag 扩张程度的指标。众所周知, 巴萎缩是 HD 最早的标志之一, 其次是苍白27。然而, 纹状体的变化与大脑中其他的灰质和白质结构有关, 仍然不清楚。这种关系对我们了解疾病进展至关重要。对所有脑结构的容积变化进行改变点分析, 可能会提供 HD premanifest 阶段脑萎缩的系统信息。
在这里, 我们演示了使用 MRICloud (www.mricloud.org) 进行全脑分割的过程, 以及对 premanifest HD 主题中的容量数据进行更改点分析的步骤。MRI 数据从一大群多中心预测-hd 研究28,29与大约400控制和 premanifest hd 主题收集。基于地图集的分割和变化点分析的结合, 带来了关于大脑结构变化的时空顺序和整个大脑疾病进展模式的独特信息。这些技术可能适用于一系列神经退行性疾病, 各种生物标志物可以映射脑变性。

<table><tbody><tr><td>MATLAB</td><td>Mathworks</td><td>N/A</td><td>Version 2015b and above</td></tr><tr><td>Dell Workstation</td><td>Dell</td><td></td><td>Dell Precision T5500 (Intel Xeon CPU)</td></tr></tbody></table>

whole-brain segmentation and change-point analysis of anatomical brain mri&#8212;application in premanifest huntington's disease

最近的 MRI 研究进展提供了多种有用的标记来识别神经退行性疾病。在亨廷顿氏病 (HD), 区域脑萎缩开始前许多年在马达发病之前 (在 "premanifest" 期间), 但区域萎缩的时空格局在脑子没有被充分地描绘。在这里, 我们展示了一个在线云计算平台, "MRICloud", 它提供了基于地图集的全脑分割的 T1-weighted 图像在多个粒度水平, 从而使我们能够访问的区域特征的大脑解剖学。然后, 我们描述了一个回归模型, 它检测出统计学意义上的拐点, 区域脑萎缩开始明显,即"改变点", 关于疾病进展指数。我们使用 CAG 年龄的产品 (CAP) 评分指数的疾病进展, HD 患者。因此, 从分割管道的容积测量的变化点分析, 提供了关于大脑结构萎缩的顺序和模式的重要信息。本文从一个大的多中心预测-hd 研究中, 阐述了这些技术对 premanifest HD T1-weighted MRI 数据的应用。这种设计可能在一系列神经退行性疾病中有广泛的应用, 以研究脑解剖学的动态变化。

使用 1.1-1.3 中描述的过程, 可以从 MRICloud 中获得全脑分割图。在当前版本的阿特拉斯 (V9B), 283 包裹被分割在最佳的粒度 (等级 5), 可以分组到不同的粒度级别, e., 从半球到小叶和包裹, 根据具体的本体定义。图 3显示了两种类型的多级分割在五级, 在轴向和日冕视图。例如, 在粗糙级别, I 型分段定义了前脑、间脑、中脑、后脑和 myelencephalon 的经典脑区 (图 3A), 而第二类定义了临床和放射学上使用半球、小脑和脑干的约定 (图 3B)。在这里, 我们使用 II 型定义进行以下分析, 我们平均的数量从左和右半球, 因为没有已知的偏侧在 HD 病理学。
根据 2.1-2.2 节的细分结果, 对脑容量进行了改变点分析。在图 4中, 我们以基底节为例, 检查不同粒度水平的相关变化点。对基底节的3D 层次结构定义在顶行进行了说明。在单个结构中, 变化点分析的散布图显示在下面的行中, 其中蓝色点表示 premanifest HD 患者 (规范化到控制) 的结构卷的 z 分数, 在修正了年龄、性别和颅内容积;黑色曲线为拟合体积 z 分数, 回归模型23中的变点分量;红色线条指示检测到的更改点的位置。在1级, 只有半球分离是可利用的, 并且相应的散点图显示整个半球有逐渐萎缩, 当盖帽比分增加, 以在360和95% 信心间隔的 a 的 a 的 a 的 a 的 a 的 a 的 a 的 a 的水平的一个变分在 [352.8, 367.2] (更正)。在2级, 基底神经节是大脑细胞核的一部分, 大脑细胞核在 232 [227.7、236.3] (罗斯福之后的 p < 0.01) 上显示一个变化点。在3级, 基底节是一个独立的结构, 它显示一个变化点在 233 [228.6, 237.4] (p < 0.01 后, 罗斯福) 的上限。在4级, 基底节分为纹状体和球苍白, 在 230 [225.6, 234.4] 和 243 [238.6, 247.4] 分别有改变点 (在罗斯福以后的 p < 0.01)。在5级, 纹状体进一步分为尾状和壳核, 在 240 [234.9, 245.1] 和 211 [206.8, 215.2] 分别显示变化点 (在罗斯福之后的 p < 0.01)。
一旦个体的变化点被计算为所有脑子结构, 变动点的全脑空间地图可以根据2.3 获得。图 5显示了不同粒度级别的变化点映射和第二类本体定义。请注意, 只有有重大变化点的结构 (罗斯福修正后的 p < 0.05) 才会被映射。在1级, 整个脑实质 (除了小脑) 显示一个重要但相对较晚的变化点在盖帽 360 [352.8, 367.2] (p = 0.011 在罗斯福以后);而脑脊液 (包括侧脑室, III 和 IV 脑室, 沟空间) 显示稍早的变化点在盖帽 319 [313.0, 325.0] (p = 0.022 在罗斯福以后)。随着粒度水平的提高和空间方差开始揭示, 具有显著变化点的区域变得更加本地化。例如, 在等级 3, 下等白色物质陈列一个更早的变动点 (盖帽 = 305 [298.8, 311.2], p = 0.038 在罗斯福以后), 与前白色物质 (盖帽 = 371 [363.9, 378.1], p = 0.042 在罗斯福以后)。在5级, 壳核、尾状和球苍白之间的差异变得明显, 壳核显示最早的变化点, 其次是尾状和球苍白。
变化率计算为每盖帽评分的体积变化 (规范化到健康控制) 的百分比, 在变化点之后。相应结构的变化率映射在图 6中, 其中温暖的颜色表示心室和沟脑脊液的体积增加, 而寒冷的颜色表明脑实质的体积减少。在3级, 深灰色物质结构显示最高的变化率, 其次是前白质, 然后是劣质的白质。在5级, 壳核和球苍白表现出最快的萎缩 (每盖0.1% 容积损失), 后跟尾状 (每帽 0.07%)。随着粒度的提高, 在更多的本地化区域中, 变化率会变得更高。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57256/57256fig1.jpg"> 图 1: MRICloud 上 T1-weighted 图像分割的图示.(A) 使用 Dcm2Analyzed.exe 将 DICOM 文件转换为分析格式并执行取消身份验证。(B) "T1-MultiAtlas 批处理" 网页。(C) "我的工作状况" 网页。(D) 用于可视化分割结果的网页。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57256/57256fig1large.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/57256/57256fig2.jpg"> 图 2: 图形用户界面 (GUI) 进行卷分析和更改点分析.(A) Matlab GUI, 用于批量提取 MRICloud 分割输出 (上部面板) 和变化点分析 (下面板) 的容量数据。(B) MRICro 界面, 以可视化变化点地图。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57256/57256fig2large.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/57256/57256fig3.jpg"> 图 3: 基于 Atlas 的全脑分割在多个粒度级别上, 具有两种本体关系.根据 I 型 (A) 和 II 型 (B) 本体论定义, 多层分割图的轴向和日冕视图覆盖在 T1-weighted 解剖图像上。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57256/57256fig3large.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/57256/57256fig4.jpg"> 图 4: 在多粒度水平上对基底神经节的变化点分析.在3D 渲染的顶部行显示半球 (1 级)、脑核 (2 级)、基底节 (3 级)、纹状体和球苍白 (4 级)、壳核和尾状 (水平 5) 之间的分层解剖关系。散射图显示了这些结构的变化点分析, 其中蓝色点表示在修正了年龄、性别和颅内容积后的体积数据的 z 分数 (规范化为健康的控制);黑色曲线为拟合 z 分数, 回归到变点相关的帽分量;红线表示变化点的位置。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57256/57256fig4large.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/57256/57256fig5.jpg"> 图 5: 在多个粒度级别上的全脑变化点映射.显示重要更改点 (5% 罗斯福) 的区域映射到 T1-weighted 图像上, 颜色指示 CAP 评分单位中的更改点值。缩写: WM: 白色物质;LV: 侧脑室;AntWM: 前白物质;InfWM: 劣质的白质;BG: 基底节;ST: 纹状体;GP: 球苍白;ALIC: 内囊的前肢;PLIC: 内囊后肢;Caud: 尾状;把: 壳核。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57256/57256fig5large.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/57256/57256fig6.jpg"> 图 6: 在多个粒度级别上的全脑变化率映射.更改率计算为每 CAP 的体积变化百分比 (规范化为健康控制) 后, 在显示显著变化点 (5% 罗斯福) 的区域中, 映射到 T1-weighted 图像。缩写: WM: 白色物质;LV: 侧脑室;AntWM: 前白物质;InfWM: 劣质的白质;BG: 基底节;ST: 纹状体;GP: 球苍白;ALIC: 内囊的前肢;PLIC: 内囊后肢;Caud: 尾状;把: 壳核。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57256/57256fig6large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>

Watch this Scientific Journal Video about Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRIâ€”Application in Premanifest Huntington's Disease at JoVE.com

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease

本文介绍了一种容积 MRI 数据分析的统计模型, 它确定了 premanifest 亨廷顿病脑萎缩开始时的 "变化点"。利用基于地图集的 T1-weighted 图像分割管道获得的脑容量, 实现了变化点的全脑映射。

解剖性脑 MRI 的全脑分割及变化点分析--Premanifest 亨廷顿病的应用

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy ...

whole-brain-segmentation-change-point-analysis-anatomical-brain

Nanyang Technological University

Research

JoVE Journal

Medicine

12.1K 次观看.  Johns Hopkins University School of Medicine. 本文介绍了一种容积 MRI 数据分析的统计模型, 它确定了 premanifest 亨廷顿病脑萎缩开始时的 "变化点"。利用基于地图集的 T1-weighted 图像分割管道获得的脑容量, 实现了变化点的全脑映射。

视频: Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon measuring activity on the surface of cerebral cortex rather than in subcortical regions such as midbrain and brainstem. Subcortical fMRI must overcome two challenges: spatial resolution and physiological noise. Here we describe an optimized set of techniques developed to perform high-resolution fMRI in human SC, a structure on the dorsal surface of the midbrain; the methods can also be used to image other brainstem and subcortical structures.
High-resolution (1.2 mm voxels) fMRI of the SC requires a non-conventional approach. The desired spatial sampling is obtained using a multi-shot (interleaved) spiral acquisition1. Since, T2* of SC tissue is longer than in cortex, a correspondingly longer echo time (TE ~ 40 msec) is used to maximize functional contrast. To cover the full extent of the SC, 8-10 slices are obtained. For each session a structural anatomy with the same slice prescription as the fMRI is also obtained, which is used to align the functional data to a high-resolution reference volume.
In a separate session, for each subject, we create a high-resolution (0.7 mm sampling) reference volume using a T1-weighted sequence that gives good tissue contrast. In the reference volume, the midbrain region is segmented using the ITK-SNAP software application2. This segmentation is used to create a 3D surface representation of the midbrain that is both smooth and accurate3. The surface vertices and normals are used to create a map of depth from the midbrain surface within the tissue4.
Functional data is transformed into the coordinate system of the segmented reference volume. Depth associations of the voxels enable the averaging of fMRI time series data within specified depth ranges to improve signal quality. Data is rendered on the 3D surface for visualization.
In our lab we use this technique for measuring topographic maps of visual stimulation and covert and overt visual attention within the SC1. As an example, we demonstrate the topographic representation of polar angle to visual stimulation in SC.

This article describes techniques to perform high-resolution functional magnetic resonance imaging with 1.2 mm sampling in human midbrain and subcortical structures using a 3T scanner. Use of these techniques to resolve topographic maps of visual stimulation in the human superior colliculus (SC) is given as an example.

高分辨率人类中脑功能磁共振成像方法

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon ...

科研

神经科学

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.

Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.

鉴定的使用神经影像学数据的与疾病-相关的的空间协方差模式

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain ...

医学

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls. 	
DTI data analysis is performed in a variate fashion, i.e. voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e. differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels.	
In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.

Diffusion tensor imaging (DTI) basically serves as an MRI-based tool to identify in vivo the microstructure of the brain and pathological processes due to neurological disorders within the cerebral white matter. DTI-based analyses allow for application to brain diseases both at the group level and in single subject data.

磁共振扩散张量成像在神经退行性疾病的分析

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present ...

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 ...

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

In lesion-symptom mapping (LSM), brain function is inferred by relating the location of acquired brain lesions to behavioral or cognitive symptoms in a group of patients. With recent advances in brain imaging and image processing, LSM has become a popular tool in cognitive neuroscience. LSM can provide fundamental insights into the functional architecture of the human brain for a variety of cognitive and non-cognitive functions. A crucial step in performing LSM studies is the segmentation of lesions on brains scans of a large group of patients and registration of each scan to a common stereotaxic space (also called standard space or a standardized brain template). Described here is an open-access, standardized method for infarct segmentation and registration for the purpose of LSM, as well as a detailed and hands-on walkthrough based on exemplary cases. A comprehensive tutorial for the manual segmentation of brain infarcts on CT scans and DWI or FLAIR MRI sequences is provided, including criteria for infarct identification and pitfalls for different scan types. The registration software provides multiple registration schemes that can be used for processing of CT and MRI data with heterogeneous acquisition parameters. A tutorial on using this registration software and performing visual quality checks and manual corrections (which are needed in some cases) is provided. This approach provides researchers with a framework for the entire process of brain image processing required to perform an LSM study, from gathering of the data to final quality checks of the results.

Provided here is a practical tutorial for an open-access, standardized image processing pipeline for the purpose of lesion-symptom mapping. A step-by-step walkthrough is provided for each processing step, from manual infarct segmentation on CT/MRI to subsequent registration to standard space, along with practical recommendations and illustrations with exemplary cases.

脑梗死分割和登记在MRI或CT上的病变症状映射

In lesion-symptom mapping (LSM), brain function is inferred by relating the location of acquired brain lesions to behavioral or cognitive symptoms in a ...

行为学

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

The human hippocampus has been broadly studied in the context of memory and normal brain function and its role in different neuropsychiatric disorders has been heavily studied. While many imaging studies treat the hippocampus as a single unitary neuroanatomical structure, it is, in fact, composed of several subfields that have a complex three-dimensional geometry. As such, it is known that these subfields perform specialized functions and are differentially affected through the course of different disease states. Magnetic resonance (MR) imaging can be used as a powerful tool to interrogate the morphology of the hippocampus and its subfields. Many groups use advanced imaging software and hardware (&#62;3T) to image the subfields; however this type of technology may not be readily available in most research and clinical imaging centers. To address this need, this manuscript provides a detailed step-by-step protocol for segmenting the full anterior-posterior length of the hippocampus and its subfields: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus (DG), strata radiatum/lacunosum/moleculare (SR/SL/SM), and subiculum. This protocol has been applied to five subjects (3F, 2M; age 29-57, avg. 37). Protocol reliability is assessed by resegmenting either the right or left hippocampus of each subject and computing the overlap using the Dice&#39;s kappa metric. Mean Dice&#39;s kappa (range) across the five subjects are: whole hippocampus, 0.91 (0.90-0.92); CA1, 0.78 (0.77-0.79); CA2/CA3, 0.64 (0.56-0.73); CA4/dentate gyrus, 0.83 (0.81-0.85); strata radiatum/lacunosum/moleculare, 0.71 (0.68-0.73); and subiculum 0.75 (0.72-0.78). The segmentation protocol presented here provides other laboratories with a reliable method to study the hippocampus and hippocampal subfields in vivo using commonly available MR tools.

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

The goal of this manuscript is to study the hippocampus and hippocampal subfields using MRI. The manuscript describes a protocol for segmenting the hippocampus and five hippocampal substructures: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus, strata radiatum/lacunosum/moleculare, and subiculum.

高分辨率<em&gt;在体内</em&gt;使用3T磁共振成像手动分割协议为人类海马子字段

The human hippocampus has been broadly studied in the context of memory and normal brain function and its role in different neuropsychiatric disorders has ...

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

The ventricular system carries and circulates cerebral spinal fluid (CSF) and facilitates clearance of solutes and toxins from the brain. The functional units of the ventricles are ciliated epithelial cells termed ependymal cells, which line the ventricles and through ciliary action are capable of generating laminar flow of CSF at the ventricle surface. This monolayer of ependymal cells also provides barrier and filtration functions that promote exchange between brain interstitial fluids (ISF) and circulating CSF. Biochemical changes in the brain are thereby reflected in the composition of the CSF and destruction of the ependyma can disrupt the delicate balance of CSF and ISF exchange. In humans there is a strong correlation between lateral ventricle expansion and aging. Age-associated ventriculomegaly can occur even in the absence of dementia or obstruction of CSF flow. The exact cause and progression of ventriculomegaly is often unknown; however, enlarged ventricles can show regional and, often, extensive loss of ependymal cell coverage with ventricle surface astrogliosis and associated periventricular edema replacing the functional ependymal cell monolayer. Using MRI scans together with postmortem human brain tissue, we describe how to prepare, image and compile 3D renderings of lateral ventricle volumes, calculate lateral ventricle volumes, and characterize periventricular tissue through immunohistochemical analysis of en face lateral ventricle wall tissue preparations. Corresponding analyses of mouse brain tissue are also presented supporting the use of mouse models as a means to evaluate changes to the lateral ventricles and periventricular tissue found in human aging and disease. Together, these protocols allow investigations into the cause and effect of ventriculomegaly and highlight techniques to study ventricular system health and its important barrier and filtration functions within the brain.

Using MRI scans (human), 3D imaging software, and immunohistological analysis, we document changes to the brain&#8217;s lateral ventricles. Longitudinal 3D mapping of lateral ventricle volume changes and characterization of periventricular cellular changes that occur in the human brain due to aging or disease are then modeled in mice.

脑室周围组织的侧脑室及组织学特性对人类和鼠标3D建模

The ventricular system carries and circulates cerebral spinal fluid (CSF) and facilitates clearance of solutes and toxins from the brain. The functional ...

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 ...

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to result from the use of different segmentation tools to generate brain volumes. Here, processing pipelines for seven automated tools that can be used to segment grey matter within the brain are presented. The protocol provides an initial step for researchers aiming to find the most accurate method for generating grey matter volumes from T1-weighted MRI scans. Steps to undertake detailed visual quality control are also included in the manuscript. This protocol covers a range of potential segmentation tools and encourages users to compare the performance of these tools within a subset of their data before selecting one to apply to a full cohort. Furthermore, the protocol may be further generalized to the segmentation of other brain regions.

This protocol describes the process of applying seven different automated segmentation tools to structural T1-weighted MRI scans to delineate grey matter regions that can be used for the quantification of grey matter volume.

t1 加权 mri 图像中皮质灰质的自动分割

Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to ...

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study

Cannabis is the illicit drug most commonly used worldwide, and its consumption can both induce psychiatric symptoms in otherwise healthy subjects and unmask a florid psychotic picture in patients with a prior psychotic risk. Previous studies suggest that chronic and long-term cannabis exposure may exert significant negative effects in brain areas enriched with cannabinoid receptors. However, whether brain alterations determined by cannabis dependency will lead to a clinically significant phenotype or to a psychotic outbreak at some point of an abuser&#8217;s life remains unclear. The aim of this study was to investigate morphological brain differences between chronic cannabis users with cannabis-induced psychosis (CIP) and non-psychotic cannabis users (NPCU) without any psychiatric conditions and correlate brain deficits with selective socio-demographic, clinical and psychosocial variables.
3T magnetic resonance imaging (MRI) scans of 10 CIP patients and 12 NPCU were acquired. The type of drug, the frequency, and the duration, as well socio-demographic, clinical and psychosocial parameters of dependency were measured. CIP patients had extensive grey matter (GM) decreases in right superior frontal gyrus, right precentral, right superior temporal gyrus, insula bilaterally, right precuneus, right medial occipital gyrus, right fusiform gyrus, and left hippocampus in comparison to chronic cannabis users without psychosis. Finally, in CIP patients, the results showed a negative correlation between a domain of the Brief Psychiatric Rating Scale (BPRS), BPRS-Activity, and selective GM volumes. Overall, the results suggest that cannabis-induced psychosis is characterized by selective brain reductions that are not present in NPCU. Therefore, neuroimaging studies may provide a potential ground for identifying putative biomarkers associated with the risk of developing psychosis in cannabis users.

This is a 3T magnetic resonance imaging study aiming to investigate grey matter volume differences between cannabis-induced psychosis patients and non-psychotic chronic cannabis users.

有或没有精神病的大麻使用者的大脑形态:试点MRI研究

Cannabis is the illicit drug most commonly used worldwide, and its consumption can both induce psychiatric symptoms in otherwise healthy subjects and ...