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Improved Gray Matter Atrophy Detection in Alzheimer Disease in Chinese Populations Using Chinese Brain Template

Jia, Xiuqin, PhD*,†; Shi, Lin, PhD‡,§; Qian, Tianyi, PhD; Li, Ying, MD, PhD*,†; Wang, Defeng, PhD¶,#; Liang, Peipeng, PhD*,†; Li, Kuncheng, PhD, MD*,†

Alzheimer Disease & Associated Disorders: October-December 2018 - Volume 32 - Issue 4 - p 309–313
doi: 10.1097/WAD.0000000000000264
Original Articles

Objective: This study aimed to test the hypothesis that the statistical Chinese brain template would be more effective to detect gray matter (GM) changes in patients with Alzheimer disease (AD) in Chinese populations.

Materials and Methods: In total, 50 patients with AD and 50 sex-matched and age-matched healthy controls were included in this study. Chinese2020, a typical statistical Chinese brain template, and MNI152, a typical Caucasian template were used for spatial normalization respectively. The GM volume alterations in patients with AD were examined by using voxel-based morphometry with education level and total intracranial volume as nuisance variables. The GM proportions of the identified brain areas with group difference were compared.

Results: By using Chinese2020 and MNI152, significant GM atrophies in patients with AD were commonly detected in the bilateral medial temporal lobe, lateral temporal lobe, inferior/medial frontal cortex, as well as left thalamus. However, higher GM percentages of detected regions were acquired when Chinese2020 was used rather than MNI152. Furthermore, stronger statistical powers in the detected clusters were observed using Chinese2020 than MNI152. In addition, the laterality index analysis showed the bilateral atrophies with no hemispheric laterality in the para/hippocampus when using population-specific brain atlas (ie, Chinese2020).

Conclusions: These findings indicated that applying the population-specific brain atlas to neuroimaging studies may achieve higher accuracy in activation detection. This may have implications to the imaging study of neurodegenerative diseases.

*Department of Radiology, Xuanwu Hospital, Capital Medical University

Beijing Key Laboratory of MRI and Brain Informatics

MR Collaboration, Northeast Asia, Siemens Healthcare, Beijing

§Chow Yuk Ho Technology Centre for Innovative Medicine

Department of Imaging and Interventional Radiology, Research Center for Medical Image Computing

Departments of #Imaging and Interventional Radiology

Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China

Supported partly by the grant from the National Natural Science Foundation of China (31400958, 61473196, and 61672065), China Postdoctoral Science Foundation (2015M570165), Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding (ZYLX201609), and Key Projects in the National Science and Technology Pillar Program during the Twelfth Five-year Plan Period (2012BAI10B04).

The authors declare no conflicts of interest.

Reprints: Peipeng Liang, PhD, Department of Radiology, Xuanwu Hospital, Capital Medical University, 45 Chang Chun Street, Xi Cheng District, Beijing 100053, China (e-mail: p.p.liang@163.com).

Received November 26, 2017

Accepted May 28, 2018

Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved