Multimodal Quantitative Parameter Mapping Characterizes Low-dimensional Feature Organization and Tract-specific Signatures in White Matter
Abstract
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: *, 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: *, 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.
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BibTeX
@article{sato2026multimodal,
title = {{Multimodal Quantitative Parameter Mapping Characterizes Low-dimensional Feature Organization and Tract-specific Signatures in White Matter}},
author = {Emi Sato and Yuki Kanazawa and Masafumi Harada and Yo Taniguchi and Masahiro Takizawa and Hiroaki Hayashi and Motoharu Sasaki and Akihiro Haga},
journal = {Magnetic Resonance in Medical Sciences},
year = {2026},
month = jul,
doi = {10.2463/mrms.mp.2026-0105},
url = {https://www.jstage.jst.go.jp/article/mrms/25/4/25_mp.2026-0105/_article},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multimodal Quantitative Parameter Mapping Characterizes Low-dimensional Feature Organization and Tract-specific Signatures in White Matter
AU - Emi Sato
AU - Yuki Kanazawa
AU - Masafumi Harada
AU - Yo Taniguchi
AU - Masahiro Takizawa
AU - Hiroaki Hayashi
AU - Motoharu Sasaki
AU - Akihiro Haga
PY - 2026
DA - 2026/07/28
JO - Magnetic Resonance in Medical Sciences
DO - 10.2463/mrms.mp.2026-0105
UR - https://www.jstage.jst.go.jp/article/mrms/25/4/25_mp.2026-0105/_article
AB - 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: *, 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: *, 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.
SN - 1880-2206
SN - 1347-3182
ER -