Cognitive trajectories in early Parkinson's disease: A multimodal MRI study of brain structure and cognitive decline.
Authors
Affiliations (8)
Affiliations (8)
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China; The Yancheng School of Clinical Medicine of Nanjing Medical University, Yancheng Third People's Hospital, Yancheng, China.
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China.
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China; Department of Neurology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China.
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.
- The Yancheng School of Clinical Medicine of Nanjing Medical University, Yancheng Third People's Hospital, Yancheng, China.
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China. Electronic address: [email protected].
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China. Electronic address: [email protected].
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China. Electronic address: [email protected].
Abstract
Cognitive trajectories in Parkinson's disease (PD) exhibit substantial heterogeneity, ranging from long-term stability to rapid decline. The specific neural circuit dysfunctions underlying these distinct outcomes remain unclear. Utilizing magnetic resonance imaging (MRI) data from the Parkinson's Progression Markers Initiative cohort, we employed voxel-based morphometry and free-water imaging to analyze baseline and longitudinal changes in gray matter volume (GMV) and white matter microstructure over a four-year period in cognitively normal (PD-NC) and cognitively impaired (PD-CI) PD patients. Machine learning models were then used to integrate clinical, neuroimaging, and fluid biomarker data to explore factors associated with cognitive decline. At baseline, PD-NC patients exhibited larger GMV in six cerebellar regions compared to healthy controls. Longitudinally, PD-CI patients showed accelerated GMV loss in seven regions, including the thalamus and temporal lobes. Notably, baseline cerebellar GMV and longitudinal thalamic atrophy rates emerged as factors associated with steeper cognitive decline. Exploratory free-water analysis revealed that PD-CI patients exhibited altered macro-microstructural relationships in the thalamus. These findings implicate the cerebello-thalamo-cortical (CTC) circuit in the heterogeneity of cognitive decline in PD, with cerebellar structure and thalamic atrophy showing differential associations with cognitive trajectories. They further underscore the value of integrating longitudinal structural and microstructural measures to characterize disease-related cognitive changes.