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Neuroscience

一种使用结构磁共振成像检查人小脑灰质形态测量的标准化管道

Published: February 04, 2022
doi:
10.3791/63340
, , , , , ,
1Department of Neuroscience, Central Clinical School,Monash University, 2Department of Biomedical Engineering,The Johns Hopkins University, 3Department of Psychiatry,National Institute of Mental Health & Neuro Sciences (NIMHANS), 4Faculty of Computer Science,Dalhousie University, 5Department of Electrical and Computer Engineering,The Johns Hopkins University, 6Department of Computer Science,The Johns Hopkins University, 7Brain and Mind Institute, Department for Statistical and Actuarial Sciences, Department for Computer Science,Western University, 8Monash Biomedical Imaging,Monash University

Summary

提出了一种用于检查小脑灰质形态测量的标准化管道。该管道结合了高分辨率，最先进的方法，用于优化和自动化的小脑包裹和基于体素的小脑配准，以进行体积量化。

Abstract

多项研究为小脑在各种认知和情感功能中的作用提供了令人信服的证据，远远超出了其与运动控制的历史关联。结构和功能性神经影像学研究进一步完善了对小脑功能性神经解剖学的理解，超越了其解剖划分，突出了检查健康变异性和神经系统疾病中单个小脑亚基的必要性。本文提出了一种用于检查小脑灰质形态测量的标准化管道，该管道结合了高分辨率，最先进的方法，用于优化和自动化小脑包裹（使用U-Net局部约束优化的自动小脑解剖包裹;ACAPULCO）和基于体素的小脑注册（空间无偏倚的下层模板;SUIT）用于体积定量。

该管道广泛适用于一系列神经系统疾病，并且是全自动的，仅对输出进行质量控制所需的手动干预。该管道是免费提供的，并附有大量文档，并且可以在Mac，Windows和Linux操作系统上运行。该管道应用于Friedreich共济失调（FRDA）患者的队列，并提供代表性结果以及组水平推断统计分析的建议。该管道可以促进整个领域的可靠性和可重复性，最终为表征和跟踪神经系统疾病中的小脑结构变化提供强大的方法学方法。

Introduction

小脑是大脑的一部分，历史上与运动控制1，2，3有关，并且被认为仅与一小部分罕见疾病（例如遗传性共济失调4）整体相关。然而，来自非人类灵长类动物解剖学追踪研究以及人类病变和神经影像学研究的趋同研究，为小脑在各种认知5，6，7，情感8，9，10，11和其他非运动功能中的作用提供了令人信服的证据7，12（见6 以供审查）。此外，小脑异常越来越多地与广泛的神经和精神疾病有关，包括帕金森病13，阿尔茨海默病14，15，癫痫16，17，精神分裂症18和自闭症谱系障碍19.因此，将小脑纳入人类脑部疾病和规范行为变异的功能和结构模型中变得至关重要。

在解剖学上，小脑可以沿其上下轴分为三个叶：前叶，后叶和絮状硬叶。波瓣进一步细分为10个小叶，用罗马数字I-X20，21表示（图1）。小脑也可以分为中线（蚯蚓）和侧（半球）区域，分别接收来自脊髓和大脑皮层的输入。前叶，包括小叶I-V，传统上与运动过程相关，并且与脑运动皮层22具有相互连接。后叶，包括小叶VI-IX，主要与非运动过程11相关，并且与前额叶皮层，后壁和颞上脑皮层8，23具有相互连接。最后，由小叶X组成的絮状硬核与前庭核具有相互连接，前庭核在姿势和步态21期间控制眼球运动和身体平衡。

最近越来越多的使用功能性神经影像学的工作进一步完善了对小脑功能性神经解剖学的理解，超越了其解剖结构。例如，静息状态功能性磁共振成像（fMRI）技术已被用于绘制小脑和大脑之间功能相互作用的模式24。此外，使用基于任务的包裹方法，King及其同事7证明小脑在其广度上显示出丰富而复杂的功能特化模式，这与各种运动，情感，社交和认知任务相关的不同功能边界证明了这一点。总的来说，这些研究强调了检查单个小脑亚基的重要性，以发展小脑参与健康变异性和以小脑结构和/或功能改变为特征的神经系统疾病的完整生物学特征。

本研究的重点是使用结构MRI在人体中量化小脑体积局部变化的方法。一般来说，使用MRI数据量化区域脑体积有两种基本方法：基于特征 的分割 和 基于体素的配准。基于特征的分割方法使用解剖学特征和标准化地图集来自动确定次区域之间的边界。用于细分的主流软件包包括FreeSurfer25，BrainSuite26和 FSL-FIRST27。然而，这些包仅提供小脑的粗包裹（例如，标记每个半球的整个灰质和整个白质），从而忽略了单个小脑小叶。这些方法也容易出现误断，特别是周围脉管系统的过度整合。

已经开发了新的机器学习和多图集标记算法，它们提供了更准确和更细粒度的小脑包裹，包括使用隐式多边界进化的小脑小脑自动分类算法（ACCLAIM28，29），小脑分析工具包（CATK30），多个自动生成的模板（MAGeT31），人类小脑及其小脑的快速自动分割（RASCAL32）），图形切割分割33和小脑分割（CERES34）。在最近的一篇论文中，比较了最先进的全自动小脑包裹方法，发现CERES2相对于小脑叶的黄金标准手动分割35优于其他方法。最近，Han及其同事36开发了一种名为ACAPULCO（使用U-Net进行局部约束优化的自动小脑解剖包裹）的深度学习算法，该算法与CERES2相当，对健康和萎缩的小脑具有广泛的适用性，以开源Docker和Singularity容器格式用于“现成”实现，并且比其他方法更具时效性。ACAPULCO自动将小脑包裹成28个解剖区域。

与基于特征的分割相比，基于体素的配准方法是将MRI精确映射到模板图像。要实现此映射，原始图像中的体素必须在大小和形状上失真。这种失真的幅度有效地提供了相对于黄金标准模板的每个体素的体积度量。这种形式的体积评估被称为“基于体素的形态测量”37。基于全脑体素的配准方法，如FSL-FLIRT38/FNIRT39、SPM统一分割40和CAT1241，通常用于基于体素的形态测量。然而，这些方法不能很好地解释小脑，导致在颅下区域（小脑，脑干42）的可靠性和有效性较差。为了解释这些局限性，开发了SUIT（空间无偏倚下层模板）算法来优化小脑配准并提高基于体素的形态测量42，43的准确性。

基于特征的分割和基于体素的配准方法用于估计区域小脑体积具有基本的优缺点。分割方法对于量化解剖学上定义的区域（例如，小叶35）的体积要准确得多。然而，小脑不同功能模块之间的边界不会映射到其解剖叶和裂缝上（相当于大脑的回和沟7）。由于基于配准的方法不受解剖学特征的约束，因此可以对小脑进行更细粒度的空间推断和高维结构功能映射44。总而言之，细分和注册方法是相辅相成的，可用于回答不同的研究问题。

在这里，提出了一个新的标准化管道，它集成了这些现有的，经过验证的方法，以提供优化和自动化的小脑包裹（ACAPULCO）和基于体素的配准（SUIT），用于体积量化（图2）。该管道建立在既定方法的基础上，包括质量控制协议，使用定性可视化和定量异常值检测，以及使用Freesurfer获得颅内体积（ICV）估计的快速方法。管道是完全自动化的，只需检查质量控制输出就需要手动干预，并且可以在Mac，Windows和Linux操作系统上运行。该管道是免费提供的，没有限制其用于非商业目的，并且可以在完成简短的注册表45后从ENIGMA联盟成像协议网页（在“ENIGMA小脑体积管道”下）访问。

所有必需的软件都列在 材料表中，除了下面描述的协议外，下载管道时还可以获得详细的教程，包括现场演示。最后，提供了代表性的结果，来自Friedreich共济失调（FRDA）患者队列和年龄和性别匹配的健康对照组的管道实施，以及组级统计推断分析的建议。

Protocol

注：本研究中使用的数据是莫纳什大学人类研究伦理委员会批准的项目（项目7810）的一部分。与会者提供了书面知情同意书。虽然管道可以在Mac，Windows或Linux操作系统上运行，但ACAPULCO，SUIT和QC管道已经在Linux（Ubuntu）和Mac（Catalina，Big Sur v11.0.1）操作系统上进行了明确的测试。 1. 模块 1： ACAPULCO （解剖学包裹） 数据采集 以1 mm 3或更低的分辨率收集…

Representative Results

小脑包裹术 小脑包裹面罩的质量控制：以下示例演示了 ACAPULCO 包裹输出，并指导了有关 a） 包裹式掩模在单个级别的质量以及 b） 随后在统计分析中包含或排除特定小叶的决策。归根结底，包括或排除一个主题的决定是主观的;这里提供了来自各种健康和临床群体的“良好包裹”，“微妙的包裹错误”和“全球失败”的例子。 <p class="jove_content"…

Discussion

小脑对广泛的人类运动3，认知58，情感10和语言7，59功能至关重要，并且与许多神经和精神疾病有关。用于量化区域小脑体积的标准化且易于实施的方法的可用性将有助于越来越详细的“全脑”结构功能映射，完整的疾病建模以及改善定义和跟踪小脑对脑疾病的贡献的机会。这里描述的这种标准化管…

Disclosures

The authors have nothing to disclose.

Acknowledgements

本手稿中介绍的工作由澳大利亚国家卫生和医学研究委员会（NHMRC）创意补助金资助：APP1184403。

Materials

ACAPULCO pipeline files  0.2.1 http://enigma.ini.usc.edu/protocols/imaging-protocols/ Please make sure to use acapulco version 0.2.1
Docker for Mac https://docs.docker.com/desktop/mac/install/ macOS must be version 10.14 or newer
Docker requires sudo priviledges
Docker imposes a memory (RAM) constraint on Mac OS. To increase the RAM, open Docker Desktop, go to Preferences and click on resources. Increase the Memory to the maximum
Docker for Windows https://docs.docker.com/docker-for-windows/install/
ENIGMA SUIT scripts http://enigma.ini.usc.edu/protocols/imaging-protocols/
FreeSurfer 7 https://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall Following variables need to be set everytime you work with Freesurfer:
export FREESURFER_HOME=equationfreesurfer _installation_directoryequation
source $FREESURFER_HOME/SetUpFreeSurfer.sh
export SUBJECTS_DIR=equationpathequation/enigma/Freesurfer
FSL (for FSLeyes). Optional 6 https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation
ICV pipeline files http://enigma.ini.usc.edu/protocols/imaging-protocols/ ICV pipeline can be run in two ways: 1) with docker/singularity. You will not require additionl software; 2) without docker/singularity- this involves running the ICV script (calculate_icv.py) manually. You will require the following additional software:
Python version equation=3.5
Python module pandas
Python module fire
Python module tabulate
Python module Colorama
https://github.com/Characterisation-Virtual-Laboratory/calculate_icv
MATLAB* 2019 or newer https://au.mathworks.com/ An academic license is required
Singularity 3.7 or newer https://www.sylabs.io/docs/ Prefered for high performance computing (HPC) clusters
SPM 12 http://www.fil.ion.ucl.ac.uk/spm/software/spm12/ Make sure spm12 and all subfolders are in your MATLAB path
SUIT Toolbox 3.4 http://www.diedrichsenlab.org/imaging/suit_download.htm Make sure you place SUIT toolbox in spm12/toolbox directory
Troubleshooting manual and segmentation output examples http://enigma.ini.usc.edu/protocols/imaging-protocols/
Tutorial manual and video http://enigma.ini.usc.edu/protocols/imaging-protocols/ Manual and accompanying live demonstration provide detailed step-by-step instructions on how to run the pipeline from start to finish.
*Not freely available; an academic license is required
DOWNLOAD MATERIALS LIST

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Tags

Cerebellar Grey Matter MorphometryStructural Magnetic Resonance ImagingAnatomical ParcellationVoxel-based MorphometryQuality ControlStandardized PipelineDockerSingularityMATLABSPM12AcapulcoRCIFQC ScriptsOutlier Detection

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Kerestes, R., Han, S., Balachander, S., Hernandez-Castillo, C., Prince, J. L., Diedrichsen, J., Harding, I. H. A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging. J. Vis. Exp. (180), e63340, doi:10.3791/63340 (2022).
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