Paper
15 March 2011 Comparison of gray matter volume and thickness for analysis of cortical changes in Alzheimer's disease
Jiachao Liu, Ziyi Li, Kewei Chen, Li Yao, Zhiqun Wang, Kunchen Li, Xiaojuan Guo
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Abstract
Gray matter volume and cortical thickness are two indices of concern in brain structure magnetic resonance imaging research. Gray matter volume reflects mixed-measurement information of cerebral cortex, while cortical thickness reflects only the information of distance between inner surface and outer surface of cerebral cortex. Using Scaled Subprofile Modeling based on Principal Component Analysis (SSM_PCA) and Pearson's Correlation Analysis, this study further provided quantitative comparisons and depicted both global relevance and local relevance to comprehensively investigate morphometrical abnormalities in cerebral cortex in Alzheimer's disease (AD). Thirteen patients with AD and thirteen age- and gender-matched healthy controls were included in this study. Results showed that factor scores from the first 8 principal components accounted for ~53.38% of the total variance for gray matter volume, and ~50.18% for cortical thickness. Factor scores from the fifth principal component showed significant correlation. In addition, gray matter voxel-based volume was closely related to cortical thickness alterations in most cortical cortex, especially, in some typical abnormal brain regions such as insula and the parahippocampal gyrus in AD. These findings suggest that these two measurements are effective indices for understanding the neuropathology in AD. Studies using both gray matter volume and cortical thickness can separate the causes of the discrepancy, provide complementary information and carry out a comprehensive description of the morphological changes of brain structure.
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Jiachao Liu, Ziyi Li, Kewei Chen, Li Yao, Zhiqun Wang, Kunchen Li, and Xiaojuan Guo "Comparison of gray matter volume and thickness for analysis of cortical changes in Alzheimer's disease", Proc. SPIE 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging, 79652E (15 March 2011); https://doi.org/10.1117/12.877624
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Cited by 2 scholarly publications.
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KEYWORDS
Brain

Cerebral cortex

Alzheimer's disease

Magnetic resonance imaging

Neuroimaging

Principal component analysis

Statistical analysis

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