Diagnosis of Parkinson’s disease based on 3D ResNet: The frontal lobe is crucial

Abstract

Parkinson’s disease (PD) is a serious neurological disease. Many studies have preseted regions of interest such as substantia nigra (SN) for PD detection from magnetic resonance imaging (MRI). However, the SN is not the only region with remarkable tissue changes in PD MRIs. Patients with Prodromal Parkinson’s Disease usually present with non-motor symptoms, and the associated brain regions may show varying degrees of damage on imaging. Therefore, exploring PD-related regions from whole-brain MRI is essential. In this study, we proposed an interpretable PD detection framework, including PD classification and feature region visualization. Specifically, we constructed a 3D ResNet model that could detect PD from whole-brain MRIs and discover other brain regions related to PD through 3D Gradient-weighted Class Activation Mapping (Grad-CAM) and Unified Parkinson’s Disease Rating Scale (UPDRS). We obtained T1-Weighted MRIs from the Parkinson’s Progression Markers Initiative (PPMI) database. The average classification accuracy of the 5-fold cross-validation and held-out dataset reached 96.1% and 94.5%, respectively. In addition, we used the 3D Grad-CAM framework to extract the weight of the feature map and obtain visual interpretation. The heat map highlighted the regions that were crucial for PD classification and found significant differences between PD and HC in frontal lobe related to linguistic semantic disorders. The UPDRS scores of PD and HC on the linguistic semantic function items were also remarkably different. Combined with previous studies, this work verified the significance of the frontal lobe and proved that the correlation between the frontal lobe and the pathogenesis of PD was explanatory.