Zhang Chi, Liu Fei, Cheng Yue, Shen Wen, Zhang Gaoyan
Tianjin Key lab of cognitive computing and application, College of Intelligence and Computing, Tianjin University, Yaguan Road, Tianjin, 300350, Tianjin, China.
Department of Radiology, Tianjin First Central Hospital, Fukang Road, Tianjin, 300192, Tianjin, China.
Neuroinformatics. 2025 Aug 20;23(3):44. doi: 10.1007/s12021-025-09734-5.
Early diagnosis of mild hepatic encephalopathy is important for the reversion of hepatic encephalopathy. Brain hyper-connectivity networks with hyperedges have showed good performance for diagnosis of neurological disorders. However, the previous hyper-connectivity networks is essentially low-level since the temporal synchronization of regional signal fluctuation is merely considered. Here, we propose a novel high-level hyper-connectivity network based on the resting state functional magnetic resonance imaging to capture the complex interactions among brain regions for better diagnosis of neurological disorders. Resting-state functional magnetic resonance imaging data from 36 mild hepatic encephalopathy patients and 36 cirrhotic patients with no mild hepatic encephalopathy are included in the study. Multi-level high-level hyper-connectivity networks are constructed firstly. Then, we define and extract node hyperdegree, hyperedge global importance and hyperedge dispersion from both low-level and high-level hyper-connectivity networks and combine them. Finally, gradient boosting decision tree is used for feature selection and classification. The leave-one-out cross-validation is used to evaluate the performance. The public ASD resting state functional magnetic resonance imaging datasets from 3 sites are also used as testing set to evaluate the generalization power of our method. Our method showed considerable performance in both experiments which confirms the effectiveness and generalization ability of the model. Besides, important regions and hyperedge features are identified for the interpretability.
轻度肝性脑病的早期诊断对于肝性脑病的逆转至关重要。具有超边的脑超连接网络在神经系统疾病诊断方面表现出良好性能。然而，先前的超连接网络本质上是低级的，因为仅考虑了区域信号波动的时间同步。在此，我们基于静息态功能磁共振成像提出一种新型高级超连接网络，以捕捉脑区之间的复杂相互作用，从而更好地诊断神经系统疾病。本研究纳入了36例轻度肝性脑病患者和36例无轻度肝性脑病的肝硬化患者的静息态功能磁共振成像数据。首先构建多级高级超连接网络。然后，我们从低级和高级超连接网络中定义并提取节点超度、超边全局重要性和超边离散度，并将它们结合起来。最后，使用梯度提升决策树进行特征选择和分类。采用留一法交叉验证来评估性能。来自3个站点的公开自闭症谱系障碍静息态功能磁共振成像数据集也用作测试集来评估我们方法的泛化能力。我们的方法在两个实验中均表现出可观的性能，证实了该模型的有效性和泛化能力。此外，为了可解释性还识别了重要区域和超边特征。
