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

• Published in: Magnetic Resonance in Medical Sciences Vol. 21; no. 1; pp. 41 - 57
• Main Authors: Goto, Masami, Abe, Osamu, Hagiwara, Akifumi, Fujita, Shohei, Kamagata, Koji, Hori, Masaaki, Aoki, Shigeki, Osada, Takahiro, Konishi, Seiki, Masutani, Yoshitaka, Sakamoto, Hajime, Sakano, Yasuaki, Kyogoku, Shinsuke, Daida, Hiroyuki
• Format: Journal Article
• Language: English
• Published: Japan Japanese Society for Magnetic Resonance in Medicine 2022; Japan Science and Technology Agency
• Subjects: Brain - diagnostic imaging; Brain - pathology; confounding covariate; cortex volume; Gray Matter - diagnostic imaging; Humans; Image contrast; Image processing; Image segmentation; Magnetic Resonance Imaging; Medical imaging; Morphology; Morphometry; Proton density (concentration); Reliability analysis; Reproducibility of Results; Review; smoothing; Statistical analysis; Statistical methods; Structure-function relationships; Substantia grisea; surface-based morphometry; voxel-based morphometry; surface-based morphometry; cortex volume; voxel-based morphometry; smoothing; confounding covariate
• ISSN: 1347-3182; 1880-2206
• DOI: 10.2463/mrms.rev.2021-0096

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).