Age and region dependent biomechanical and ultrastructural differences in porcine white matter

Lei, Rujing, Young, Robert ORCID: https://orcid.org/0000-0002-8300-8002, Xu, Lanxi and Theobald, Peter ORCID: https://orcid.org/0000-0002-3227-7130 2026. Age and region dependent biomechanical and ultrastructural differences in porcine white matter. Acta Biomaterialia 216 , pp. 311-322. 10.1016/j.actbio.2026.04.013

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Abstract

The biofidelity of computational models for paediatric traumatic brain injury (TBI) is dependent on age and region-specific material data that captures the brain’s complex mechanical behaviour. This study performs a comprehensive, multiscale characterisation of porcine white matter, a high-fidelity model for the human brain, across three key developmental stages (0.5, 7, and 36 months) and three anatomically distinct regions (brain stem, parietal lobe, and temporal lobe). Oscillatory shear rheometry was employed to measure the linear viscoelastic properties (storage/loss moduli) and the non-linear response under 0–12% compressive pre-strain. In parallel, transmission electron microscopy was used to quantify the underlying ultrastructural architecture. The results reveal three principal findings. First, biomechanical maturation is a highly asynchronous and heterogeneous process; the brain stem undergoes rapid early stiffening, while the cerebral white matter stiffens more gradually. Second, porcine white matter exhibits pronounced non-linear compression-dependent increase in apparent shear stiffness, a behaviour whose magnitude is strongly modulated by both anatomical region and developmental age, with stiffness increasing by up to 839% under 12% compression in mature tissue. Third, a multivariate partial least squares regression identified mean axon radius as a significant microstructural correlate of bulk tissue stiffness (r = 0.70, p = 0.036). These findings provide a baseline dataset for the development and calibration of biofidelic, age-specific, non-linear constitutive models essential for more accurate brain computational modelling.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering Schools > Optometry and Vision Sciences
Publisher:	Elsevier
ISSN:	1742-7061
Date of First Compliant Deposit:	20 April 2026
Date of Acceptance:	7 April 2026
Last Modified:	02 Jun 2026 13:30
URI:	https://orca.cardiff.ac.uk/id/eprint/186507

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