Multimodal MRI reveals microstructural and macrostructural alterations in Parkinson's disease: A combined 3D...

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.

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.

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.

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.

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.