Huang Liqin, Ye Xiaofang, Yang Mingjing, Pan Lin, Zheng Shao Hua
College of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
College of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Comput Biol Med. 2023 Jan;152:106308. doi: 10.1016/j.compbiomed.2022.106308. Epub 2022 Nov 24.
The identification of early-stage Parkinson's disease (PD) is important for the effective management of patients, affecting their treatment and prognosis. Recently, structural brain networks (SBNs) have been used to diagnose PD. However, how to mine abnormal patterns from high-dimensional SBNs has been a challenge due to the complex topology of the brain. Meanwhile, the existing prediction mechanisms of deep learning models are often complicated, and it is difficult to extract effective interpretations. In addition, most works only focus on the classification of imaging and ignore clinical scores in practical applications, which limits the ability of the model. Inspired by the regional modularity of SBNs, we adopted graph learning from the perspective of node clustering to construct an interpretable framework for PD classification.
In this study, a multi-task graph structure learning framework based on node clustering (MNC-Net) is proposed for the early diagnosis of PD. Specifically, we modeled complex SBNs into modular graphs that facilitated the representation learning of abnormal patterns. Traditional graph neural networks are optimized through graph structure learning based on node clustering, which identifies potentially abnormal brain regions and reduces the impact of irrelevant noise. Furthermore, we employed a regression task to link clinical scores to disease classification, and incorporated latent domain information into model training through multi-task learning.
We validated the proposed approach on the Parkinsons Progression Markers Initiative dataset. Experimental results showed that our MNC-Net effectively separated the early-stage PD from healthy controls(HC) with an accuracy of 95.5%. The t-SNE figures have showed that our graph structure learning method can capture more efficient and discriminatory features. Furthermore, node clustering parameters were used as important weights to extract salient task-related brain regions(ROIs). These ROIs are involved in the development of mood disorders, tremors, imbalances and other symptoms, highlighting the importance of memory, language and mild motor function in early PD. In addition, statistical results from clinical scores confirmed that our model could capture abnormal connectivity that was significantly different between PD and HC. These results are consistent with previous studies, demonstrating the interpretability of our methods.
早期帕金森病（PD）的识别对于患者的有效管理至关重要，会影响其治疗和预后。最近，脑结构网络（SBNs）已被用于诊断PD。然而，由于大脑复杂的拓扑结构，如何从高维SBNs中挖掘异常模式一直是一个挑战。同时，深度学习模型现有的预测机制往往很复杂，难以提取有效的解释。此外，大多数研究仅关注成像分类，而在实际应用中忽略了临床评分，这限制了模型的能力。受SBNs区域模块化的启发，我们从节点聚类的角度采用图学习来构建一个用于PD分类的可解释框架。
在本研究中，提出了一种基于节点聚类的多任务图结构学习框架（MNC-Net）用于PD的早期诊断。具体而言，我们将复杂的SBNs建模为模块化图，这有助于异常模式的表征学习。传统的图神经网络通过基于节点聚类的图结构学习进行优化，从而识别潜在的异常脑区并减少无关噪声的影响。此外，我们采用回归任务将临床评分与疾病分类联系起来，并通过多任务学习将潜在领域信息纳入模型训练。
我们在帕金森病进展标记倡议数据集上验证了所提出的方法。实验结果表明，我们的MNC-Net能够有效地将早期PD与健康对照（HC）区分开来，准确率达到95.5%。t-SNE图表明，我们的图结构学习方法可以捕获更有效和更具区分性的特征。此外，节点聚类参数被用作重要权重来提取与任务相关的显著脑区（ROIs）。这些ROIs涉及情绪障碍、震颤、平衡失调等症状的发展，突出了记忆、语言和轻度运动功能在早期PD中的重要性。此外，临床评分的统计结果证实，我们的模型能够捕获PD和HC之间显著不同的异常连接。这些结果与先前的研究一致，证明了我们方法的可解释性。
