Gray matter atrophy in patients with Parkinson disease in Chinese population: A voxel-based morphometry study using Chinese brain template Chinese2020
10.13929/j.1003-3289.201805047
- Author:
Xiuqin JIA
1
Author Information
1. Department of Radiology, Beijing Chao-yang Hospital, Capital Medical University
- Publication Type:Journal Article
- Keywords:
Chinese brain template;
Magnetic resonance imaging;
Parkinson disease;
Voxel-based morphometry
- From:
Chinese Journal of Medical Imaging Technology
2019;35(1):4-8
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the value of Chinese brain template Chinese2020 in detecting gray matter (GM) alterations in patients with Parkinson disease (PD) in Chinese population using voxel-based morphometry (VBM). Methods Totally 15 PD patients (PD group)and 15 gender- and age-matched healthy volunteers (control group) were enrolled. Chinese2020 based on Chinese and MNI152 based on Caucasian were used for spatial normalization, respectively. The deformations induced by spatial normalization and gray matter (GM) proportion in the identified brain areas were compared between Chinese2020 and MNI152. The differences of GM volume between PD group and control group were examined using VBM. Results Using MNI152, GM atrophies in PD patients were detected in bilateral temporal lobe extending into ipsilateral insula/parahippocampus/hippocampus, the left superior occipital cortex/cuneus/precuneus, as well as the right putamen. Besides the above mentioned regions, the right middle frontal cortex atrophy was additionally identified using Chinese2020. Particularly, fewer deformations were found when spatially normalizing Chinese population's brain to Chinese2020 than to MNI152. Moreover, GM percentages of detected regions using Chinese2020 were significantly higher than those using MNI152 (t=2.502, P=0.037). Conclusion CBT should be used in neuroimaging studies of Chinese population, which may achieve fewer deformations during spatial normalization, more GM proportions in the identified clusters and improve the accuracy of detection.
