Multimodal MRI integrating anti-motion multi-parametric mappings for investigating subcortical nuclei microstructural alterations in Huntington's disease

BackgroundHuntington's disease (HD) is a hereditary neurodegenerative disorder, with pathological changes detectable by MRI before symptom onset. Quantitative MRI (qMRI) provides tissue-specific parameters and holds potential for capturing disease-related biomarkers. However, conventional analysis methods often rely on single-modality imaging or mean features, constraining their ability to capture HD's complex microstructural evolution.PurposeTo assess the feasibility of multi-modal MRI combined with the MOLED sequence in HD patients and explore its value in early disease detection and staging.Methods22 HD patients (14 Pre-HD and 8 M-HD) and 27 healthy controls were enrolled. MOLED-derived T2 and T2* maps, along with structural MRI, were acquired using two 3.0 T scanners to assess inter-scanner consistency. The MOLED sequence incorporates ultrafast acquisition techniques to minimize motion artifacts and improve image quality. Histogram-based features (e.g., variance, skewness, and maximum) and volumes were extracted from eight deep brain regions. Multiple machine learning models were employed for classification analysis.ResultsThe MOLED demonstrated good image consistency and reproducibility across scanners. Significant group differences were observed in the volumes of several basal ganglia regions and in variance-based features across multiple modalities. Machine learning models combining clinical and mapping features achieved the highest classification performance (maximum F1-macro = 0.846, Sensitivity-macro = 0.838).ConclusionMOLED provides stable and complementary quantitative information for multi-modal MRI. Integrating multimodal multi-feature with machine learning enables a more comprehensive depiction of HD-related microstructural heterogeneity and disease progression.