Microscale characterisation of the time-dependent mechanical behaviour of brain white matter

TL;DR

This study combines atomic force microscopy experiments with micromechanical modeling to characterize the time-dependent, microstructure-dependent mechanical behavior of brain white matter at a microscale, aiding in better simulations and medical applications.

Contribution

It provides a detailed microscale characterization of white matter mechanics, integrating experimental AFM data with inverse modeling and finite element analysis to understand tissue heterogeneity and anisotropy.

Findings

Mechanical response depends on axon orientation.

Relaxation behavior varies across brain regions.

Models agree well with experimental data.

Abstract

Brain mechanics is a topic of deep interest because of the significant role of mechanical cues in both brain function and form. Specifically, capturing the heterogeneous and anisotropic behaviour of cerebral white matter (WM) is extremely challenging and yet the data on WM at a spatial resolution relevant to tissue components are sparse. To investigate the time-dependent mechanical behaviour of WM, and its dependence on local microstructural features when subjected to small deformations, we conducted atomic force microscopy (AFM) stress relaxation experiments on corpus callosum (CC), corona radiata (CR) and fornix (FO) of fresh ovine brain. Our experimental results show a dependency of the tissue mechanical response on axons orientation, with e.g. the stiffness of perpendicular and parallel samples is different in all three regions of WM whereas the relaxation behaviour is different for the CC and FO regions. An inverse modelling approach was adopted to extract Prony series parameters of the tissue components, i.e. axons and extra cellular matrix with its accessory cells, from experimental data. Using a bottom-up approach, we developed analytical and FEA estimates that are in good agreement with our experimental results. Our systematic characterisation of sheep brain WM using a combination of AFM experiments and micromechanical models provide a significant contribution for predicting localised time-dependent mechanics of brain tissue. This information can lead to more accurate computational simulations, therefore aiding the development of surgical robotic solutions for drug delivery and accurate tissue mimics, as well as the determination of criteria for tissue injury and predict brain development and disease progression.