Tensor-derived diffusion MRI metrics and NODDI in multiple sclerosis classification.

Overview

1. AGH University of Krakow, Poland
2. Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
3. Department of Neurology, University Hospital in Krakow, Poland

Abstract

Background: Multi-compartment diffusion models and multi-shell acquisitions are increasingly used to overcome limitations of conventional diffusion tensor imaging, but require longer scans and more complex processing. Meanwhile, diffusion MRI classification studies often rely on a narrow set of tensor-derived metrics, especially fractional anisotropy. We investigated whether broader use of tensor-derived features, including shape descriptors, could improve classification without increasing acquisition complexity.

Methods: Multi-shell diffusion MRI (b = 0, 1000 and 2000 s/mm2) was acquired in 220 participants, including 84 healthy controls and 136 patients with relapsing-remitting multiple sclerosis. Thirteen tensor-derived metrics and three neurite orientation dispersion and density imaging (NODDI) metrics were extracted from 98 automatically parcellated brain regions. Classification was performed using L2-regularized logistic regression within a repeated stratified cross-validation framework.

Results: A four-metric set comprising fractional anisotropy, mean diffusivity, spherical and linear anisotropy reached an AUC of 0.967, above fractional anisotropy alone (0.922) and comparable to the full 13-metric representation. The point-estimate gain was concentrated in linear anisotropy; spherical anisotropy did not increase performance. NODDI showed no advantage at matched dimensionality; single-shell yielded similar point estimates, with no significant difference detected. Removing lesion voxels reduced performance only in white matter, where classification remained well above chance. Free-water correction and adjustment for age, sex and intracranial volume did not improve classification.

Conclusions: In this ROI-based diffusion MRI classification, broader use of conventional tensor-derived information showed similar within-cohort discrimination to more complex representations. Linear anisotropy, computed from eigenvalues already available, adds complementary information without requiring additional diffusion contrasts. These findings support exploiting conventional tensor-derived features more comprehensively before adopting more complex diffusion MRI frameworks.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

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Data availability

The code and data supporting the findings of this study are available in Zenodo at Doi: https://doi.org/10.5281/zenodo.20512670

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

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Recorded: type, language, journal, volume, pages, dates, 7 authors, 5 keywords, 12 MeSH terms, 1 funder, 37 references.

Cite

This paper

Lasek, J., Mazur-Rosmus, W., Wnuk, M., Marona, M., Kaczówka, K., Słowik, A., & Krzyżak, A. (2026). Tensor-derived diffusion MRI metrics and NODDI in multiple sclerosis classification. NeuroImage. Clinical, 51, 104057. https://doi.org/10.1016/j.nicl.2026.104057

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