Multimodal Quantitative Parameter Mapping Characterizes Low-dimensional Feature Organization and Tract-specific Signatures in White Matter.
PURPOSE To investigate relationships among relaxation, susceptibility, and diffusion parameters in healthy white matter (WM) and to characterize WM MRI feature organization using an integrative multimodal quantitative MRI framework. METHODS Twenty-two healthy volunteers underwent 3T MRI. Quantitative parameter mapping provided R1, R2*, R1·R2*, and quantitative susceptibility mapping, while diffusion kurtosis imaging provided fractional anisotropy (FA), mean kurtosis, axial kurtosis (AK), and radial kurtosis. All maps were spatially normalized to Montreal Neurological Institute (MNI) space, and mean values were extracted from WM tracts defined by the Johns Hopkins University (JHU) White Matter Atlas. Pearson correlation analysis with false discovery rate correction, principal component analysis (PCA), hierarchical clustering, and bootstrap stability analysis were performed. RESULTS The first 2 principal components explained 77.7% of the total variance. The first principal component (PC1) was mainly associated with relaxation-related parameters and diffusion kurtosis metrics, whereas the second principal component (PC2) was characterized by opposing contributions from AK and FA. Quantitative susceptibility mapping showed weak correlations with other parameters (r = -0.37 to -0.08), suggesting relatively independent susceptibility-related information. The PCA structure was preserved after excluding R1·R2*, and bootstrap analysis supported loading stability, with mean loading correlations of 0.94 for PC1 and 0.90 for PC2. WM tracts formed 4 major cluster-like groups and showed tract-specific multimodal fingerprints. CONCLUSION Multimodal quantitative MRI provides a concise tract-level MRI feature representation of WM by integrating relaxation, susceptibility, and diffusion information. This exploratory framework may support future studies investigating subtle WM alterations, although validation in pathological cohorts is required.