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

Abstract

Background

Huntington'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.

Purpose

To 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.

Methods

22 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.

Results

The 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).

Conclusion

MOLED 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.

Plain language summary

Huntington's disease (HD) is a genetic brain disorder that causes gradual damage to nerve cells, often years before any symptoms appear. Detecting these early brain changes is important for better diagnosis and treatment. In this study, we used a special type of MRI scan called MOLED, which can quickly and clearly capture brain images with detailed tissue information. We combined this with standard MRI to examine differences between people with HD (both early-stage and mid-stage) and healthy individuals.

We looked at specific deep brain areas and measured features like shape, size, and signal patterns. Our results showed that the MOLED scan produced reliable images on different MRI machines and could detect meaningful changes in the brain, even in early stages of the disease. When we used machine learning to analyze the data, we were able to accurately distinguish between different groups based on their brain features.

This research shows that combining advanced MRI techniques with computer-based analysis can help detect HD earlier and understand how the disease progresses over time.