Slator, P. J. | bioRxiv Science

bioRxiv2025

Hierarchical Bayesian Modelling Improves Microstructural Parameter Mapping in Diffusion and Exchange MRI Data

Microstructure modelling quantifies subvoxel tissue features by combining an MRI acquisition with a mathematical model, which is typically fitted voxel-by-voxel with least-squares (LSQ) minimisation to give voxelwise maps of microstructural quantities such as diffusivity and compartmental fractions. Such approaches are susceptible to voxelwise noise, which can lead to erroneous values in parameter maps. Hierarchical Bayesian modelling (HBM) can address this limitation, but has only been demonstrated for simple models. We previously derived an HBM approach for an arbitrary microstructure model with flexible parameter constraints, utilising a Markov chain Monte Carlo algorithm for parameter estimation; here the method is demonstrated and evaluated using simulated and human data for two previously unexplored diffusion MRI techniques, namely diffusion kurtosis imaging and blood-brain barrier filter exchange imaging. When compared with LSQ minimisation, HBM increased the accuracy, precision, contrast-to-noise ratio, and parameter map quality in both simulated and human data. HBM was also able to resolve local parameter variations associated with white matter lesions in a small sample of cerebral small vessel disease subjects, which were obscured by high noise levels in the LSQ-derived parameter maps. Finally, a noise sensitivity assessment in simulations showed that HBM improved the contrast-to-noise ratio and parameter map quality even at low signal-to-noise ratios. This generalised HBM framework can improve parameter estimation for more complex diffusion MRI microstructural models that extend beyond linear combinations of exponentials.

biophysics↗

bioRxiv2018

A Hidden Markov Model for Detecting Confinement in Single Particle Tracking Trajectories

State-of-the-art single particle tracking (SPT) techniques can generate long trajectories with high temporal and spatial resolution. This offers the possibility of mechanistically interpreting particle movements and behaviour in membranes. To this end, a number of statistical techniques have been developed that partition SPT trajectories into states with distinct diffusion signatures, allowing a statistical analysis of diffusion state dynamics and switching behaviour. Here we develop a confinement model, within a hidden Markov framework, that switches between phases of free diffusion, and confinement in a harmonic potential well. By using a Markov chain Monte Carlo (MCMC) algorithm to fit this model, automated partitioning of individual SPT trajectories into these two phases is achieved, which allows us to analyse confinement events. We demonstrate the utility of this algorithm on a previously published dataset, where gold nanoparticle (AuNP) tagged GM1 lipids were tracked in model membranes. We performed a comprehensive analysis of confinement events, demonstrating that there is heterogeneity in the lifetime, shape, and size of events, with confinement size and shape being highly conserved within trajectories. Our observations suggest that heterogeneity in confinement events is caused by both individual nanoparticle characteristics and the binding site environment. The individual nanoparticle heterogeneity ultimately limits the ability of iSCAT to resolve molecular dynamics to the order of the tag size; homogeneous tags could potentially allow the resolution to be taken below this limit by deconvolution methods. In a wider context, the presented harmonic potential well confinement model has the potential to detect and characterise a wide variety of biological phenomena, such as hop diffusion, receptor clustering, and lipid rafts.

biophysics↗