Zhou Yongxia, Lui Yvonne W
Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, 4th Floor, 660 First Avenue, New York City, NY 10016, USA.
ISRN Geriatr. 2013;2013. doi: 10.1155/2013/542080.
Small-world network consists of networks with local specialization and global integration. Our objective is to detect small-world properties alteration based on cortical thickness in mild cognitive impairment (MCI) including stables and converters, and early Alzheimer's disease (AD) compared to controls.
MRI scans of 13 controls, 10 MCI, and 10 with early AD were retrospectively analyzed; 11 MCI converters, 11 MCI stables, and 10 controls from the ADNI website were also included.
There were significantly decreased local efficiencies in patients with MCI and AD compared to controls; and MCI patients showed increased global efficiency compared to AD and controls. The MCI converters experience the worst local efficiency during the converting period to AD; the stables, however, have highest local and global efficiency.
The abnormal cortical thickness-based small-world properties in MCI and AD as well as the distinct patterns between two MCI subtypes suggest that small-world network analysis has the potential to better differentiate different stages of early dementia.
小世界网络由具有局部专业化和全局整合性的网络组成。我们的目标是基于轻度认知障碍（MCI，包括稳定型和转化型）以及早期阿尔茨海默病（AD）与对照组相比的皮质厚度，检测小世界特性的改变。
对13名对照组、10名MCI患者和10名早期AD患者的MRI扫描进行回顾性分析；还纳入了来自ADNI网站的11名MCI转化型患者、11名MCI稳定型患者和10名对照组。
与对照组相比，MCI和AD患者的局部效率显著降低；与AD和对照组相比，MCI患者的全局效率增加。MCI转化型患者在向AD转化期间局部效率最差；然而，稳定型患者具有最高的局部和全局效率。
基于皮质厚度的MCI和AD中小世界特性异常以及两种MCI亚型之间的不同模式表明，小世界网络分析有可能更好地区分早期痴呆的不同阶段。
