Bayesian insights into exchange and restriction in gray matter diffusion MRI

Jallais, Maëliss ORCID: https://orcid.org/0000-0001-5939-388X, Uhl, Quentin, Pavan, Tommaso, Molendowska, Malwina, Jones, Derek K. ORCID: https://orcid.org/0000-0003-4409-8049, Jelescu, Ileana and Palombo, Marco ORCID: https://orcid.org/0000-0003-4892-7967 2026. Bayesian insights into exchange and restriction in gray matter diffusion MRI. Imaging Neuroscience 4 , IMAG.a.1317. 10.1162/imag.a.1317

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Abstract

Biophysical models in diffusion MRI (dMRI) hold promise for characterizing gray matter tissue microstructure. Yet, the reliability of their parameter estimates remains largely under-studied, especially in models that incorporate water exchange. In this study, we investigate the accuracy, precision, and presence of degeneracy of two recently proposed gray matter models, NEXI and SANDIX, using established acquisition protocols, on both simulated and in vivo data. We employ µGUIDE, a Bayesian inference framework based on deep learning, to quantify parameter uncertainty and detect degeneracies, enabling a more interpretable assessment of model fits. Our results show that while some microstructural parameters, such as extra-cellular diffusivity and neurite signal fraction, are robustly estimated, others, including exchange time and soma radius, are often associated with high uncertainty and estimation bias, particularly under realistic noise conditions and reduced acquisition protocols. Comparison with non-linear least squares fitting highlights the critical advantage of uncertainty-aware methods: the ability to flag and filter out unreliable estimates. Together, these findings emphasize the need to report uncertainty and account for model degeneracies when interpreting model-based estimates. Our study advocates for the integration of probabilistic fitting approaches into imaging pipelines to improve reproducibility and biological interpretability.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Psychology Schools > Computer Science & Informatics Research Institutes & Centres > Cardiff University Brain Research Imaging Centre (CUBRIC)
Publisher:	Massachusetts Institute of Technology Press
Date of First Compliant Deposit:	20 July 2026
Date of Acceptance:	29 June 2026
Last Modified:	04 Aug 2026 11:37
URI:	https://orca.cardiff.ac.uk/id/eprint/188312

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