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

Journal: Journal of Huntington's disease
Published Date: Jan 8, 2026

Abstract

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.

Authors

  • Mengying Zhu
    Institute of Artificial Intelligence, Xiamen University, Xiamen, China.
  • Ming Ye
    Department of Scientific Computing, Florida State University, Tallahassee, FL 32306, USA.
  • Zejun Wu
    Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, People's Republic of China.
  • Jianzhong Lin
    Magnetic Resonance Center, Zhongshan Hospital Xiamen University, Xiamen 361004, China.
  • Fei Wu
    Zhejiang University, 38 Zheda Road, Hangzhou 310058, Zhejiang, China.
  • Xiao Wang
    Research Centre of Basic Integrative Medicine, School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
  • Haiyang Luo
    Department of Geosciences, Princeton University, Princeton, NJ, USA.
  • Yong Zhang
    Outpatient Department of Hepatitis, The Sixth Affiliated People's Hospital of Dalian Medical University, Dalian, Liaoning, China.
  • Jianfeng Bao
    Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China. [email protected].
  • Shuhui Cai
    Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.
  • Congbo Cai
    Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.

Keywords

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