A Time-Dependent Diffusion MRI Framework for Clinical Characterisation of Human Brain Cellular Architecture
- 1Institute of Neurosciences CSIC-UMH
Abstract
Diffusion-weighted MRI, beyond the commonly used diffusion tensor framework, offers a unique window into tissue microstructure in vivo, yet its clinical adoption has remained limited. Major barriers include the complexity of diffusion MRI sequence design, lengthy acquisition protocols, and the challenges associated with robust estimation of high-dimensional microstructural model parameters. Here, we address these limitations by combining optimised diffusion encoding with state-of-the-art simulation-based inference, establishing a clinically feasible framework for multi-compartment diffusion modelling. We validate the approach through i) in-depth in silico experiments and ii) in vivo studies made up of both human and rodent data. The resulting microstructural metrics are robust, reproducible across healthy individuals and show significant spatial associations with brain-wide expression patterns of cell-specific genes. Requiring less than 10 minutes of acquisition time, this framework substantially lowers the barriers to advanced microstructural imaging, a prerequisite step toward its eventual evaluation for the diagnosis, stratification, and monitoring of brain disorders.
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BibTeX
@misc{leibovici2026timedependent,
title = {{A Time-Dependent Diffusion MRI Framework for Clinical Characterisation of Human Brain Cellular Architecture}},
author = {A. Leibovici and E. Espinos Soler and D. Mesika and G. Tsarfaty and A. Livny and S. De Santis and M. F. Eggl},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.02.26362017},
url = {https://www.medrxiv.org/content/10.64898/2026.09.02.26362017v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - A Time-Dependent Diffusion MRI Framework for Clinical Characterisation of Human Brain Cellular Architecture
AU - A. Leibovici
AU - E. Espinos Soler
AU - D. Mesika
AU - G. Tsarfaty
AU - A. Livny
AU - S. De Santis
AU - M. F. Eggl
PY - 2026
DA - 2026/09/02
PB - medRxiv
DO - 10.64898/2026.09.02.26362017
UR - https://www.medrxiv.org/content/10.64898/2026.09.02.26362017v1
AB - Diffusion-weighted MRI, beyond the commonly used diffusion tensor framework, offers a unique window into tissue microstructure in vivo, yet its clinical adoption has remained limited. Major barriers include the complexity of diffusion MRI sequence design, lengthy acquisition protocols, and the challenges associated with robust estimation of high-dimensional microstructural model parameters. Here, we address these limitations by combining optimised diffusion encoding with state-of-the-art simulation-based inference, establishing a clinically feasible framework for multi-compartment diffusion modelling. We validate the approach through i) in-depth in silico experiments and ii) in vivo studies made up of both human and rodent data. The resulting microstructural metrics are robust, reproducible across healthy individuals and show significant spatial associations with brain-wide expression patterns of cell-specific genes. Requiring less than 10 minutes of acquisition time, this framework substantially lowers the barriers to advanced microstructural imaging, a prerequisite step toward its eventual evaluation for the diagnosis, stratification, and monitoring of brain disorders.
ER -