[1]郭圣文,吴聪玲,赖春任,等.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.[doi:10.3969/j.issn.1001-0505.2017.03.012]
 Guo Shengwen,Wu Congling,Lai Chunren,et al.Analysis and classification on multiple cortical features of patients with mild cognitive impairment[J].Journal of Southeast University (Natural Science Edition),2017,47(3):483-489.[doi:10.3969/j.issn.1001-0505.2017.03.012]
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轻度认知障碍患者脑皮层多特征分析与分类()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
47
期数:
2017年第3期
页码:
483-489
栏目:
生物医学工程
出版日期:
2017-05-20

文章信息/Info

Title:
Analysis and classification on multiple cortical features of patients with mild cognitive impairment
作者:
郭圣文吴聪玲赖春任吴宇鹏江行军赵地The Alzheimer’s Disease Neuroimaging Initiative(ADNI)
华南理工大学材料科学与工程学院, 广州 510006
Author(s):
Guo Shengwen Wu Congling Lai Chunren Wu Yupeng Jiang Xingjun Zhao Di The Alzheimer’s Disease Neuroimaging Initiative(ADNI)
School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China
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
Keywords:
mild cognitive impairment conversion cortical feature feature selection support vector machine longitudinal change
分类号:
R445.2,TP391.41
DOI:
10.3969/j.issn.1001-0505.2017.03.012
摘要:
揭示稳定型轻度认知障碍患者、转化型轻度认知障碍患者与健康正常人之间的脑结构及其形态变化差异,以区分3组人群.首先,选择73例健康正常人、46例稳定型MCI患者和40例转化型MCI患者,采集基线期以及1年和2年时间节点的脑结构磁共振图像;然后,应用Freesurfer软件计算皮层厚度、灰质体积、表面积和平均曲率等脑皮层形态结构特征,并分别利用T检验方法、稀疏约束降维法和递归特征消去法,选择重要特征;最后,利用线性支持向量机对3组人群进行分类,分析具有强分类能力的重要脑区及其分布.结果表明,递归特征消去法的分类性能最优,稀疏约束降维法次之,T检验最差; 4种皮层特征融合,尤其是基线与纵向变化特征融合,可显著提高分类性能.脑皮层结构特征及其随时间的变化信息,能被有效地应用于稳定型和转化型MCI患者的自动分类.
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.

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备注/Memo

备注/Memo:
收稿日期: 2016-09-07.
作者简介: 郭圣文(1971—),男,博士,教授,shwguo@scut.edu.cn.
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).
引用本文: 郭圣文,吴聪玲,赖春任,等.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. DOI:10.3969/j.issn.1001-0505.2017.03.012.
更新日期/Last Update: 2017-05-20