东南大学学报（自然科学版）2017，Vol.47Issue(3)：483-489,7.DOI:10.3969/j.issn.1001-0505.2017.03.012
轻度认知障碍患者脑皮层多特征分析与分类
Analysis and classification on multiple cortical features of patients with mild cognitive impairment
摘要
Abstract
The difference of the brain structures and morphological changes among the patients with stable mild cognitive impairment (sMCI),the patients with converted mild cognitive impairment (cMCI) and the normal control (NC) was revealed and three groups were discriminated.First,73 NC,46 sMCI and 40 cMCI were selected,and the baseline,1-year and 2-year longitudinal follow-up magnetic resonance (MR) images were acquired.Secondly,the FreeSurfer software was used to calculate the cortical morphological features including the cortical thickness,the gray matter volume,the surface area,and the mean curvature.The T-test method,the sparsity-constrained dimensionality reduction (SCDR) method and the recursive feature elimination (RFE) method were adopted to extract the salient features in discrimination.Finally,the linear support vector machine (LSVM) was applied to classify these three groups,and the brain regions with strong capability in the classification and their distributions were analyzed.The experimental results show that the RFE method exhibits the best performance in classification,followed by the SCDR method,and the T-test method is least.The combination of four types of cortical features,especially the combination of the baseline feature with the longitudinal change feature,can improve the performance of the classifier.Therefore,the cortical morphological features and their changes with time can be applied for automatic classification between the patients with sMCI and the patients with cMCI.关键词
轻度认知障碍/转化/脑皮层特征/特征选择/支持向量机/纵向变化Key words
mild cognitive impairment/conversion/cortical feature/feature selection/support vector machine/longitudinal change分类
医药卫生引用本文复制引用
郭圣文,吴聪玲,赖春任,吴宇鹏,江行军,赵地..轻度认知障碍患者脑皮层多特征分析与分类[J].东南大学学报（自然科学版）,2017,47(3):483-489,7.基金项目
国家自然科学基金资助项目(31371008)、广东省科技计划资助项目(2015A02024006)、广州市产学研协同创新重大专项资助项目(201604020170). （31371008）
