Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (12): 3703-3708.DOI: 10.11772/j.issn.1001-9081.2019050901

• Frontier & interdisciplinary applications • Previous Articles    

Application of multimodal network fusion in classification of mild cognitive impairment

WANG Xin, GAO Yuan, WANG Bin, SUN Jie, XIANG Jie   

  1. College of Information and Computer, Taiyuan University of Technology, Taiyuan Shanxi 030024, China
  • Received:2019-05-28 Revised:2019-07-25 Online:2019-12-10 Published:2019-08-05
  • Contact: 相洁
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61873178, 61876124, 61503272), the Key Research and Development Program of Shanxi Province (International Science and Technology Cooperation) (201803D421047), the Natural Science Foundation of Shanxi Province (201801D121135), the Natural Science Foundation for Young Scientists of Shanxi Province (201701D221119).


王鑫, 高原, 王彬, 孙婕, 相洁   

  1. 太原理工大学 信息与计算机学院, 太原 030024
  • 作者简介:王鑫(1996-),男,山西忻州人,硕士研究生,主要研究方向:智能信息处理、脑信息学;高原(1995-),女,山西长治人,硕士研究生,主要研究方向:智能信息处理、脑信息学;王彬(1983-),男,四川内江人,副教授,博士,主要研究方向:心理学、智能信息处理、脑信息学;孙婕(1994-),女,山西太原人,硕士研究生,主要研究方向:智能信息处理、脑信息学;相洁(1970-),女,山西太原人,教授,博士生导师,博士,主要研究方向:智能信息处理、脑信息学、大数据管理与分析。
  • 基金资助:

Abstract: Since the early Mild Cognitive Impairment (MCI) is very likely to be undiagnosed by the assessment of medical diagnostic cognitive scale, a multimodal network fusion method for the aided diagnosis and classification of MCI was proposed. The complex network analysis method based on graph theory has been widely used in the field of neuroimaging, but different effects of brain diseases on the network topology of the brain would be conducted by using imaging technologies based different modals. Firstly, the Diffusion Tensor Imaging (DTI) and resting-state functional Magnetic Resonance Imaging (rs-fMRI) data were used to construct the fusion network of brain function and structure connection. Then, the topological properties of the fusion network were analyzed by One-way ANalysis of VAriance (ANOVA), and the attributes with significant difference were selected as the classification features. Finally, the one way cross validation of Support Vector Machines (SVM) was used for the classification of healthy group and MCI group, and the accuracy was estimated. The experimental results show that, the classification result accuracy of the proposed method reaches 94.44%, which is significantly higher than that of single modal data method. Many brain regions, such as cingulate gyrus, superior temporal gyrus and parts of the frontal and parietal lobes, of the MCI patients diagnosed by the proposed method show significant differences, which is basically consistent with the existing research results.

Key words: multimodal, Mild Cognitive Impairment (MCI), Diffusion Tensor Imaging (DTI), resting-state functional Magnetic Resonance Imaging (rs-fMRI), fusion network, Support Vector Machine (SVM), classification

摘要: 针对早期轻度认知障碍(MCI)根据医学诊断认知量表评估极有可能无法判断的问题,提出了一种多模态网络融合的MCI辅助诊断分类方法。基于图论的复杂网络分析方法在神经影像领域的应用已得到广泛认可,但采用不同模态的成像技术研究脑部疾病对大脑网络拓扑结构属性的影响会产生不同结果。首先,使用弥散张量成像(DTI)与静息态功能磁共振成像(rs-fMRI)数据构建大脑结构和功能连接的融合网络。然后,融合网络的拓扑属性被施以单因素方差分析(ANOVA),选择具有显著差异的属性作为分类特征。最后,利用支持向量机(SVM)留一法交叉验证对健康组和MCI组分类,估算准确率。实验结果表明,所提方法的分类结果准确率达到94.44%,相较单一模态数据法的分类结果有明显提高。所提方法诊断出的MCI患者在扣带回、颞上回以及额叶和顶叶部分区域等许多脑区表现出显著异常,与已有研究结果基本一致。

关键词: 多模态, 轻度认知障碍, 弥散张量成像, 静息态功能磁共振成像, 融合网络, 支持向量机, 分类

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