Song Chaofan, Liu Tongqiang, Wang Huan, Shi Haifeng, Jiao Zhuqing
School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China.
Department of Nephrology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China.
Math Biosci Eng. 2023 Jul 10;20(8):14827-14845. doi: 10.3934/mbe.2023664.
Effectively selecting discriminative brain regions in multi-modal neuroimages is one of the effective means to reveal the neuropathological mechanism of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI). Existing multi-modal feature selection methods usually depend on the Euclidean distance to measure the similarity between data, which tends to ignore the implied data manifold. A self-expression topological manifold based multi-modal feature selection method (SETMFS) is proposed to address this issue employing self-expression topological manifold. First, a dynamic brain functional network is established using functional magnetic resonance imaging (fMRI), after which the betweenness centrality is extracted. The feature matrix of fMRI is constructed based on this centrality measure. Second, the feature matrix of arterial spin labeling (ASL) is constructed by extracting the cerebral blood flow (CBF). Then, the topological relationship matrices are constructed by calculating the topological relationship between each data point in the two feature matrices to measure the intrinsic similarity between the features, respectively. Subsequently, the graph regularization is utilized to embed the self-expression model into topological manifold learning to identify the linear self-expression of the features. Finally, the selected well-represented feature vectors are fed into a multicore support vector machine (MKSVM) for classification. The experimental results show that the classification performance of SETMFS is significantly superior to several state-of-the-art feature selection methods, especially its classification accuracy reaches 86.10%, which is at least 4.34% higher than other comparable methods. This method fully considers the topological correlation between the multi-modal features and provides a reference for ESRDaMCI auxiliary diagnosis.
在多模态神经图像中有效选择具有区分性的脑区是揭示终末期肾病合并轻度认知障碍（ESRDaMCI）神经病理机制的有效手段之一。现有的多模态特征选择方法通常依赖欧几里得距离来衡量数据之间的相似性，这往往会忽略隐含的数据流形。提出了一种基于自表达拓扑流形的多模态特征选择方法（SETMFS）来解决这一问题，该方法采用自表达拓扑流形。首先，使用功能磁共振成像（fMRI）建立动态脑功能网络，然后提取介数中心性。基于此中心性度量构建fMRI的特征矩阵。其次，通过提取脑血流量（CBF）构建动脉自旋标记（ASL）的特征矩阵。然后，通过计算两个特征矩阵中每个数据点之间的拓扑关系来构建拓扑关系矩阵，以分别衡量特征之间的内在相似性。随后，利用图正则化将自表达模型嵌入到拓扑流形学习中，以识别特征的线性自表达。最后，将选择的具有良好代表性的特征向量输入多核支持向量机（MKSVM）进行分类。实验结果表明，SETMFS的分类性能明显优于几种现有的特征选择方法，尤其是其分类准确率达到86.10%，比其他可比方法至少高4.34%。该方法充分考虑了多模态特征之间的拓扑相关性，为ESRDaMCI辅助诊断提供了参考。
