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

医学 形态学(生物学) 脑形态计量学 体素 基于体素的形态计量学 人工智能 放射科 磁共振成像 古生物学 白质 计算机科学 生物
作者
Masami Goto,Osamu Abe,Akifumi Hagiwara,Shohei Fujita,Koji Kamagata,Masaaki Hori,Shigeki Aoki,Takahiro Osada,Seiki Konishi,Yoshitaka Masutani,Hajime Sakamoto,Yasuaki Sakano,Shinsuke Kyogoku,Hiroyuki Daida
出处
期刊:Magnetic Resonance in Medical Sciences [Japan Society of Magnetic Resonance in Medicine]
卷期号:21 (1): 41-57 被引量:140
标识
DOI:10.2463/mrms.rev.2021-0096
摘要

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).

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