Parkinson's disease (PD) is characterized by loss of selectively vulnerable neurons within the basal ganglia circuit and progressive atrophy in subcortical and cortical regions. However, the impact of neurodegenerative pathology on the topological organization of cortical morphological networks has not been explored. The aims of this study were to investigate altered network patterns of covariance in cortical thickness and complexity, and to evaluate how morphological network integrity in PD is related to motor impairment.Individual morphological networks were constructed for 50 PD patients and 46 healthy controls (HCs) by estimating interregional similarity distributions in surface-based indices. We performed graph theoretical analysis and network-based statistics to detect PD-related alterations and further examined the correlation of network metrics with clinical scores. Furthermore, support vector regression based on topological characteristics was applied to predict the severity of motor impairment in PD.Compared with HCs, PD patients showed lower local efficiency (p = 0.004), normalized characteristic path length (p = 0.022), and clustering coefficient (p = 0.005) for gyrification index-based morphological brain networks. Nodal topological abnormalities were mainly in the frontal, parietal and temporal regions, and impaired morphological connectivity was involved in the sensorimotor and default mode networks. The support vector regression model using network-based features allowed prediction of motor symptom severity with a correlation coefficient of 0.606.This study identified a disrupted topological organization of cortical morphological networks that could substantially advance our understanding of the network degeneration mechanism of PD and might offer indicators for monitoring disease progression.Copyright © 2023. Published by Elsevier Masson SAS.