A diagnosis model for amnestic mild cognitive impairment based on structural network characteristics of the brain

The aim of this work is to select topological characteristics of structural network which were higher correlated with cognitive performance and estimate the classification models to classify normal aging and amenstic mild cognitive impairment (aMCI) patients. In two groups of diffusion tensor image (DTI) dataset, a group has 52 normal aging subjects and another group has 39 aMCI patients. We employed the diffusion tensor image to construct the structural network in each group and used the method of graph theory to extract the characteristics of structural network. We selected significant features by the correlation analysis between the characteristics of brain network and subject' s mini-mental state examination (MMSE) score. These features were used to establish five kinds of classification model for evaluating the classification efficiency of the models. For normal aging dataset and aMCI dataset, 18 characteristics of the structural network were selected as features, which are significantly associated with cognitive ability and locate in 9 brain areas according to the automated anatomical labeling (AAL) template in each group. However, the features and the related regions are different for the two datasets. Among 5 algorithms, sequential minimal optimization learning algorithm for support vector machine regression model was more accurate due to the higher specificity of 88. 46% the higher sensitivity of 83.05% and the higher accuracy of 85.71%. Brain structural network metrics which were correlated greatly with the cognitive performance could be taken as biological marker pointers to establish the classification model to classify normal aging subjects and aMCI patients. Furthermore, these structural network features can provide the information of connection changes between corresponding brain regions.