基于大脑结构网络特征的遗忘型轻度认知障碍诊断模型

doi:10.3969/j.issn.0258.8021- 2014. 05.07

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摘要选取与认知表现分数相关性高的结构网络拓扑特征，利用这些特征建立分类模型，实现对正常老化者及遗忘型轻度认知障碍(aMCI）患者的分类。本研究包含两组扩散张量影像（DTI）数据，一组为52例正常老化受试者，一组为39例aMCI患者。分别对两组数据进行结构网络构建，采用图论分析法提取结构网络的特征，将所有特征与简单智能状态检查量表(MMSE)分数进行相关性分析，选取与认知表现分数高度相关的特征，基于这些特征建立5种分类模型，并对模型的分类效果进行评估。对于正常老化数据，选出18个与认知能力显著相关的结构网络特征，集中于解剖自动贴标（AAL）图谱中的9个脑区；对于aMCI数据，也选出18个与认知能力显著相关的结构网络特征，集中于AAL图谱中的9个脑区；二者选出的特征及分布的脑区是不同的。通过对分类模型的评估，得出支持向量机序列最小优化算法建立的模型分类效果最佳，特异性达到88.46%，敏感性达到83.05%，准确性达到85.71%。所提取的与认知表现相关性高的结构网络特征，可以作为生物标记指针，来建立分类模型，对正常老化者及aMCI患者进行分类，也可提供相应脑区间连接变化的信息。

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	员锐娟¹ 吴水才^1*林仲志²
	3 黄楚中^4&n

关键词 ：扩散张量影像, 遗忘型轻度认知障碍, 图论分析, 认知表现, 分类模型

Abstract：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.

Key words： diffusion tensor image (DTI) amnestic mild cognitive impairment (aMCI) graph theory cognitive performance classification model

基金资助:长庚大学研究计划(UERPD2B0301, UERPD2C0041)；长庚大学健康老化中心计划(CMRPD1B0331, EMRPD1D0261)
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引用本文:

员锐娟¹ 吴水才^1*林仲志^2,3黄楚中^{4&n. 基于大脑结构网络特征的遗忘型轻度认知障碍诊断模型[J]. 中国生物医学工程学报, 2014, 33(5): 564-573.

YUN Rui Juan¹ WU Shui Cai^1*LIN Chung Chin^2,3HUANG Chu Chung⁴LIN Ching Po⁴WANG Pei Ning⁵CHAO Y. A Diagnosis Model for Amnestic Mild Cognitive Impairment Based on Structural Network Characteristics of the Brain#br#. journal1, 2014, 33(5): 564-573.}

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http://cjbme.csbme.org/CN/ 10.3969/j.issn.0258.8021- 2014. 05.07 或 http://cjbme.csbme.org/CN/Y2014/V33/I5/564

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