Wang Kun, Jiang Tianzi, Liang Meng, Wang Liang, Tian Lixia, Zhang Xinqing, Li Kuncheng, Liu Zhening
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):340-7. doi: 10.1007/11866763_42.
In this work, we proposed a discriminative model of Alzheimer's disease (AD) on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model used the correlation/anti-correlation coefficients of two intrinsically anti-correlated networks in resting brains, which have been suggested by two recent studies, as the feature of classification. Pseudo-Fisher Linear Discriminative Analysis (pFLDA) was then performed on the feature space and a linear classifier was generated. Using leave-one-out (LOO) cross validation, our results showed a correct classification rate of 83%. We also compared the proposed model with another one based on the whole brain functional connectivity. Our proposed model outperformed the other one significantly, and this implied that the two intrinsically anti-correlated networks may be a more susceptible part of the whole brain network in the early stage of AD.
在这项工作中，我们基于多变量模式分类和功能磁共振成像（fMRI）提出了一种阿尔茨海默病（AD）的判别模型。该模型使用静息大脑中两个内在反相关网络的相关/反相关系数作为分类特征，这两个网络是最近的两项研究提出的。然后在特征空间上进行伪费舍尔线性判别分析（pFLDA）并生成线性分类器。使用留一法（LOO）交叉验证，我们的结果显示正确分类率为83%。我们还将提出的模型与另一个基于全脑功能连接的模型进行了比较。我们提出的模型明显优于另一个模型，这意味着这两个内在反相关网络可能是AD早期全脑网络中更易受影响的部分。
