Multimodal Quantitative Parameter Mapping Characterizes Low-dimensional Feature Organization and Tract-specific...

Area of Science:

• Neuroimaging
• Quantitative MRI
• White Matter Anatomy

Background:

• White matter (WM) integrity is crucial for neurological function.
• Understanding the complex relationships between different MRI parameters in WM is essential.
• Current methods may not fully capture the integrated nature of WM microstructural properties.

Purpose of the Study:

• To investigate the relationships among relaxation, susceptibility, and diffusion parameters in healthy white matter (WM).
• To characterize WM MRI feature organization using an integrative multimodal quantitative MRI framework.
• To establish tract-specific multimodal fingerprints for WM characterization.

Main Methods:

• Utilized 3T MRI on 22 healthy volunteers.
• Acquired quantitative parameter maps (R1, R2*, R1·R2*, quantitative susceptibility mapping) and diffusion kurtosis imaging metrics (FA, mean kurtosis, AK, RK).
• Employed principal component analysis (PCA), hierarchical clustering, and bootstrap stability analysis on spatially normalized WM tract data.

Main Results:

• The first two principal components explained 77.7% of the variance, integrating relaxation and diffusion kurtosis metrics (PC1) or opposing AK and FA (PC2).
• Quantitative susceptibility mapping showed weak correlations with other parameters, indicating independent information.
• WM tracts exhibited distinct multimodal fingerprints, forming 4 major cluster-like groups.

Conclusions:

• Multimodal quantitative MRI provides a concise, tract-level representation of WM by integrating diverse imaging information.
• This framework aids in understanding WM organization and may support future investigations into subtle WM changes.
• Further validation in pathological cohorts is necessary to confirm clinical utility.