Zhang Rupu, Fu Xidong, Song Chaofan, Shi Haifeng, Jiao Zhuqing
School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China.
Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China.
Brain Sci. 2023 Aug 10;13(8):1187. doi: 10.3390/brainsci13081187.
Patients with end-stage renal disease (ESRD) experience changes in both the structure and function of their brain networks. In the past, cognitive impairment was often classified based on connectivity features, which only reflected the characteristics of the binary brain network or weighted brain network. It exhibited limited interpretability and stability. This study aims to quantitatively characterize the topological properties of brain functional networks (BFNs) using multi-threshold derivative (MTD), and to establish a new classification framework for end-stage renal disease with mild cognitive impairment (ESRDaMCI). The dynamic BFNs (DBFNs) were constructed and binarized with multiple thresholds, and then their topological properties were extracted from each binary brain network. These properties were then quantified by calculating their derivative curves and expressing them as multi-threshold derivative (MTD) features. The classification results of MTD features were compared with several commonly used DBFN features, and the effectiveness of MTD features in the classification of ESRDaMCI was evaluated based on the classification performance test. The results indicated that the linear fusion of MTD features improved classification performance and outperformed individual MTD features. Its accuracy, sensitivity, and specificity were 85.98 ± 2.92%, 86.10 ± 4.11%, and 81.54 ± 4.27%, respectively. Finally, the feature weights of MTD were analyzed, and MTD-cc had the highest weight percentage of 28.32% in the fused features. The MTD features effectively supplemented traditional feature quantification by addressing the issue of indistinct classification differentiation. It improved the quantification of topological properties and provided more detailed features for diagnosing cognitive disorders.
终末期肾病（ESRD）患者的脑网络结构和功能都会发生变化。过去，认知障碍常根据连接特征进行分类，而这些特征仅反映了二值化脑网络或加权脑网络的特性。其解释性和稳定性有限。本研究旨在使用多阈值导数（MTD）对脑功能网络（BFN）的拓扑特性进行定量表征，并为轻度认知障碍的终末期肾病（ESRDaMCI）建立一个新的分类框架。构建动态BFN（DBFN）并使用多个阈值进行二值化，然后从每个二值化脑网络中提取其拓扑特性。接着通过计算它们的导数曲线并将其表示为多阈值导数（MTD）特征来对这些特性进行量化。将MTD特征的分类结果与几种常用的DBFN特征进行比较，并基于分类性能测试评估MTD特征在ESRDaMCI分类中的有效性。结果表明，MTD特征的线性融合提高了分类性能，优于单个MTD特征。其准确率、灵敏度和特异性分别为85.98±2.92%、86.10±4.11%和81.54±4.27%。最后，分析了MTD的特征权重，MTD-cc在融合特征中的权重百分比最高，为28.32%。MTD特征通过解决分类区分不明显的问题有效地补充了传统特征量化。它改进了拓扑特性的量化，并为诊断认知障碍提供了更详细的特征。
