轻度认知障碍磁共振信号中固有频率动态功能性连接的聚类研究
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作者单位:

1.云南民族大学电气信息工程学院, 昆明 650500;2.云南省高校智能传感网络及信息系统创新团队, 昆明 650500

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基金项目:

国家自然科学基金(62161052);云南省高校科技创新团队。


Clustering Related Factors of Intrinsic Frequency Dynamic Functional Connection in MRI Signal of Mild cognitive Impairment
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Affiliation:

1.School of Electrical and Information Technology, Yunnan Minzu University, Kunming 650500, China;2.Intelligent Senor Network & Information System Innovative Research Team in Science and Technology in University of Yunnan Province, Kunming 650500, China

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    摘要:

    功能性连接(Functional connectivity, FC)可以表示脑区的协同工作能力,目前广泛采用动态功能性连接(Dynamic functional connectivity,DFC)和聚类分析相结合的方法研究疾病的显著性差异分析和分类。但现有研究,对于聚类个数的确定和聚类结果选用并没有明确的标准,且传统的DFC无法表示不同频率的FC信息。因此,本文对轻度认知障碍(Mild cognitive impairment, MCI)磁共振信号中固有频率DFC聚类问题进行研究。首先对被试的时间进程(Time course,TC)数据做噪音辅助的多元经验模态分解并计算DFC;然后通过评判辅助的聚类方法做聚类分析,再采用最小二乘对聚类结果做拟合;最后采用分类器做分类。实验采用阿尔茨海默病神经影像学(Alzheimer’s disease nearoimaging, ADNI)数据库的数据对本文算法进行测试。实验结果表明,有监督聚类分类准确率高于无监督聚类;引入固有频率的DFC分类准确率要高于传统的DFC;最小二乘拟合能提升分类准确率。

    Abstract:

    Functional connectivity (FC) can represent the ability of brain regions to work together. At present, a combination of dynamic functional connectivity (DFC) and cluster analysis is widely used to study the significant difference analysis and classification of diseases. However, in the existing study, there is no clear standard for the determination of the number of clusters and the selection of clustering results, and the traditional DFC cannot represent the FC information of different frequencies. Therefore, this paper studies the clustering related factors of intrinsic frequency DFC in MRI signal of mild cognitive impairment (MCI). First, the noise-assisted multivariate empirical mode decomposition of the time course (TC) data is performed and the DFC is calculated. Then, the cluster is analyzed through the evaluation-assisted clustering method, and the least square method is used to fit the clustering results. Finally, classifier is used for classification. The contribution of this paper is to suggest a more reasonable clustering method and a more number of clusters to obtain functional connections at different intrinsic frequencies. In the experiment, we used the Alzheimer’s disease neuroimaging (ANDI) database to test the proposed method. The experimental results show that the accuracy of supervised clustering used in this paper is higher than that of unsupervised clustering; the classification accuracy of DFC with natural frequency is higher than that of traditional DFC; the least square fitting can improve classification accuracy.

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李栋,吴海锋,保涵,马佳,曾玉.轻度认知障碍磁共振信号中固有频率动态功能性连接的聚类研究[J].数据采集与处理,2022,37(4):798-813

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  • 收稿日期:2022-02-08
  • 最后修改日期:2022-06-10
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  • 在线发布日期: 2022-08-11