MNC-Net: Multi-task graph structure learning based on node clustering for early Parkinson’s disease diagnosis

• Published in: Computers in biology and medicine 2023-01, Vol.152, p.106308-106308, Article 106308
• Main Authors: Huang, Liqin, Ye, Xiaofang, Yang, Mingjing, Pan, Lin, Zheng, Shao hua
• Format: Article
• Language: English
• Subjects: Brain; Brain - diagnostic imaging; Classification; Clinical scores; Clustering; Deep learning; Diagnosis; Early Diagnosis; Early Parkinson’s disease; Feature extraction; Graph neural networks; Humans; Information technology; Machine learning; Magnetic Resonance Imaging - methods; Medical diagnosis; Modularity; Movement disorders; Neural networks; Neural Networks, Computer; Neurodegenerative diseases; Neuroimaging; Nodes; Parkinson Disease - diagnostic imaging; Parkinson's disease; Signs and symptoms; Statistical analysis; Structural brain network; Topology
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.106308

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. •Clinical score is an important data to assist Parkinson’s di