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在流行的神经影像分析工具包中使用大规模统计中文脑模板(Chinese2020)。

Using Large-Scale Statistical Chinese Brain Template (Chinese2020) in Popular Neuroimage Analysis Toolkits.

作者信息

Shi Lin, Liang Peipeng, Luo Yishan, Liu Kai, Mok Vincent C T, Chu Winnie C W, Wang Defeng, Li Kuncheng

机构信息

Department of Medicine and Therapeutics, The Chinese University of Hong KongShatin, Hong Kong.

Department of Radiology, Xuanwu Hospital, Capital Medical UniversityBeijing, China.

出版信息

Front Hum Neurosci. 2017 Aug 17;11:414. doi: 10.3389/fnhum.2017.00414. eCollection 2017.

Abstract

Given that the morphology of Chinese brains statistically differs from that of Caucasian, there is an urgent need to develop a Chinese brain template for neuroimaging studies in Chinese populations. Based on a multi-center dataset, we developed a statistical Chinese brain template, named as Chinese2020 (Liang et al., 2015). This new Chinese brain atlas has been validated in brain normalization and segmentation for anatomical Magnetic Resonance Imaging (MRI) studies, and is publicly available at http://www.chinese-brain-atlases.org/. In our previous study, we have demonstrated this Chinese atlas showed higher accuracy in segmentation and relatively smaller shape deformations during registration. Because the spatial normalization of functional images is mainly based on the segmentation and normalization of anatomical image, the population-specific brain atlas should also be more appropriate for functional studies involving Chinese populations. The aim of this technology report is to validate the performance of Chinsese2020 template in functional neuroimaging studies, and demonstrated that for Chinese population studies, the use of the Chinese2010 template produces more valid results. The steps of how to use the Chinese2020 template in SPM software were given in details in this technology report, and based on an example of finger tapping fMRI study, this technology report demonstrated the Chinese2020 template could improve the performance of the neuroimaging analysis of Chinese populations.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4816/5562686/eceba5c55d84/fnhum-11-00414-g0001.jpg

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