Identifing two distinct cortical progression subtypes of Parkinson's disease through multimodal neuroimaging.
Authors
Affiliations (14)
Affiliations (14)
- Department of Neurology, Tongji Medical College, Union Hospital, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, People's Republic of China.
- Department of Neurology, Wuhan Red Cross Hospital, 392 Hongkong Road, Wuhan, Hubei, China.
- Department of Radiology, Shanghai Key Laboratory of Emotions and Affective Disorders, Songjiang Research Institute, Songjiang Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, P. R. China.
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022, China.
- Hubei Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, People's Republic of China.
- Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, 430022, China.
- Department of Anesthesiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- McLean Imaging Center, McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA.
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022, China. [email protected].
- Department of Neurology, Tongji Medical College, Union Hospital, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, People's Republic of China. [email protected].
- Department of Radiology, Shanghai Key Laboratory of Emotions and Affective Disorders, Songjiang Research Institute, Songjiang Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, P. R. China. [email protected].
- Institute of Neuroscience and Brain Diseases, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China. [email protected].
Abstract
To investigate whether distinct patterns of cortical involvement exist in Parkinson's disease (PD) and to characterize their potential progression trajectories using multimodal neuroimaging and data-driven disease progression modeling. In this cross-sectional multimodal imaging study, we enrolled 317 patients with clinically diagnosed PD and 61 healthy controls. All participants underwent simultaneous FDG-PET and MRI scanning. We applied the Subtype and Stage Inference (SuStaIn) model to cortical glucose metabolism and thickness data to identify latent disease progression patterns. Network-level characteristics were further examined within a whole-brain gradient framework. Robustness was assessed through age- and sex-balanced sensitivity analyses in the local cohort and external validation using harmonized T1-weighted MRI data from the Parkinson's Progression Markers Initiative (PPMI) dataset. Two distinct cortical involvement subtypes were identified. One subtype showed predominant alterations in higher-order association networks, including the default mode and frontoparietal networks, whereas the other was characterized by greater involvement of lower-order sensorimotor and limbic systems. These subtype patterns remained stable across sensitivity analyses and external validation. Disease duration showed a significant correlation with the inferred disease stage (r = 0.15, p = 0.01). Imaging findings further revealed hypermetabolism in brainstem and trans-entorhinal regions accompanied by widespread cortical hypometabolism. Our findings reveal two robust cortical progression patterns in PD, highlighting substantial heterogeneity in network-level metabolic and structural involvement. This framework provides new insights into PD phenotypic variability and may support future efforts toward disease stratification and personalized research.