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基于体素和基于表面的形态测量学在皮质形态分析中的优势:各种应用的综述。

Advantages of Using Both Voxel- and Surface-based Morphometry in Cortical Morphology Analysis: A Review of Various Applications.

机构信息

Department of Radiological Technology, Faculty of Health Science, Juntendo University.

Department of Radiology, Graduate School of Medicine, The University of Tokyo.

出版信息

Magn Reson Med Sci. 2022 Mar 1;21(1):41-57. doi: 10.2463/mrms.rev.2021-0096. Epub 2022 Feb 18.

DOI:10.2463/mrms.rev.2021-0096
PMID:35185061
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9199978/
Abstract

Surface-based morphometry (SBM) is extremely useful for estimating the indices of cortical morphology, such as volume, thickness, area, and gyrification, whereas voxel-based morphometry (VBM) is a typical method of gray matter (GM) volumetry that includes cortex measurement. In cases where SBM is used to estimate cortical morphology, it remains controversial as to whether VBM should be used in addition to estimate GM volume. Therefore, this review has two main goals. First, we summarize the differences between the two methods regarding preprocessing, statistical analysis, and reliability. Second, we review studies that estimate cortical morphological changes using VBM and/or SBM and discuss whether using VBM in conjunction with SBM produces additional values. We found cases in which detection of morphological change in either VBM or SBM was superior, and others that showed equivalent performance between the two methods. Therefore, we concluded that using VBM and SBM together can help researchers and clinicians obtain a better understanding of normal neurobiological processes of the brain. Moreover, the use of both methods may improve the accuracy of the detection of morphological changes when comparing the data of patients and controls.In addition, we introduce two other recent methods as future directions for estimating cortical morphological changes: a multi-modal parcellation method using structural and functional images, and a synthetic segmentation method using multi-contrast images (such as T1- and proton density-weighted images).

摘要

基于表面的形态计量学(SBM)对于估计皮质形态的指数非常有用,例如体积、厚度、面积和脑回形成,而体素基于形态计量学(VBM)是一种典型的灰质(GM)体积测量方法,包括皮质测量。在使用 SBM 估计皮质形态的情况下,是否应该额外使用 VBM 来估计 GM 体积仍然存在争议。因此,本次综述有两个主要目标。首先,我们总结了这两种方法在预处理、统计分析和可靠性方面的差异。其次,我们综述了使用 VBM 和/或 SBM 估计皮质形态变化的研究,并讨论了使用 VBM 与 SBM 结合是否会产生额外的价值。我们发现了在 VBM 或 SBM 中检测形态变化的优势情况,以及在两种方法之间表现出等效性能的情况。因此,我们得出结论,使用 VBM 和 SBM 可以帮助研究人员和临床医生更好地理解大脑的正常神经生物学过程。此外,当比较患者和对照组的数据时,使用这两种方法可能会提高形态变化检测的准确性。此外,我们还介绍了另外两种估计皮质形态变化的最新方法作为未来的方向:一种是使用结构和功能图像的多模态分割方法,另一种是使用多对比度图像(如 T1 和质子密度加权图像)的合成分割方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec3/9199978/df929fed2aad/mrms-21-41-g5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec3/9199978/262c1bb97a13/mrms-21-41-g1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec3/9199978/ff411e3a6692/mrms-21-41-g2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec3/9199978/7157a2d529d0/mrms-21-41-g3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec3/9199978/0d166a290517/mrms-21-41-g4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec3/9199978/df929fed2aad/mrms-21-41-g5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec3/9199978/262c1bb97a13/mrms-21-41-g1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec3/9199978/ff411e3a6692/mrms-21-41-g2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec3/9199978/7157a2d529d0/mrms-21-41-g3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec3/9199978/0d166a290517/mrms-21-41-g4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ec3/9199978/df929fed2aad/mrms-21-41-g5.jpg

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