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Multimodal MRI reveals microstructural and macrostructural alterations in Parkinson's disease: A combined 3D
Yuan Tian1, Chengyu Li2, Xinyu Song2
1Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Harbin Medical University, Heilongjiang, Harbin 150001, PR China.
Background:
Parkinson's disease (PD)is a prevalent neurodegenerative disorder, and early diagnosis remains challenging due to the lack of sensitive and specific imaging biomarkers. Multimodal MRI provides a promising approach to capture both microstructural and macrostructural alterations.
Objectives:
To investigate microstructural and macrostructural changes in the motor cortex (MC) and posterior cingulate cortex (PCC) in PD using NODDI and 3D structural MRI, and to develop a multimodal diagnostic model integrating these features.
Methods:
A total of 124 PD patients and 136 healthy controls were enrolled. NODDI-derived parameters, including neurite density index (NDI), orientation dispersion index (ODI), and isotropic volume fraction (Viso), as well as 3D structural metrics (cortical thickness, surface area, and gray matter volume), were extracted from MC and PCC. Group comparisons and correlations with clinical scales (UPDRS, NMSS, and Hoehn-Yahr stage) were performed. A multimodal diagnostic model was constructed and evaluated using receiver operating characteristic (ROC) analysis.
Results:
No significant differences were observed between groups in age or sex (P > 0.05). Compared with controls, PD patients showed significantly decreased NDI and increased Viso in both MC and PCC (FDR-corrected, P < 0.01). ODI was significantly increased in MC (P < 0.001), but not in PCC. Structurally, PD patients exhibited reduced cortical thickness, surface area, and gray matter volume in MC (FDR-corrected, P < 0.001), whereas no significant differences were found in PCC. The multimodal model achieved superior performance (AUC = 0.77, accuracy = 0.705) compared with single-modality models.
Conclusions:
NODDI and 3D structural MRI can sensitively detect microstructural and macrostructural alterations in PD. Their integration improves diagnostic accuracy and may provide valuable imaging biomarkers for early diagnosis, disease monitoring, and treatment evaluation.
Insights
This study shows that combining advanced MRI techniques, including Neurite Orientation Dispersion Imaging (NODDI) and 3D structural MRI, can identify brain changes in Parkinson's disease (PD) and improve diagnostic accuracy.
Area of Science:
- Neuroimaging
- Neuroscience
- Radiology
Background:
- Parkinson's disease (PD) diagnosis lacks sensitive imaging biomarkers.
- Multimodal MRI offers potential for detecting microstructural and macrostructural brain changes.
Purpose of the Study:
- Investigate microstructural and macrostructural changes in the motor cortex (MC) and posterior cingulate cortex (PCC) in PD.
- Develop an integrated multimodal diagnostic model for PD using MRI data.
Main Methods:
- Utilized NODDI and 3D structural MRI on 124 PD patients and 136 controls.
- Extracted NODDI parameters (NDI, ODI, Viso) and structural metrics (cortical thickness, surface area, gray matter volume) from MC and PCC.
- Constructed and evaluated a multimodal diagnostic model using ROC analysis.
Main Results:
- PD patients showed decreased NDI and increased Viso in MC and PCC, and increased ODI in MC.
- Significant reductions in cortical thickness, surface area, and gray matter volume were observed in the MC of PD patients.
- The multimodal model demonstrated superior diagnostic performance (AUC=0.77) compared to single-modality approaches.
Conclusions:
- NODDI and 3D structural MRI effectively detect PD-related microstructural and macrostructural alterations.
- Integrating these MRI modalities enhances diagnostic accuracy for Parkinson's disease.
- This multimodal approach shows promise as imaging biomarkers for early PD diagnosis and monitoring.
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