Biology subjects

Daubney, G.

Publications and source records attributed to Daubney, G..

3 recordsLinked to original sources

bioRxiv2024

Linking microscopy to diffusion MRI with degenerate biophysical models: an application of the Bayesian EstimatioN of CHange (BENCH) framework

Biophysical modelling of diffusion MRI (dMRI) is used to non-invasively estimate microstructural features of tissue, particularly in the brain. However, meaningful description of tissue requires many unknown parameters, resulting in a model that is often ill-posed. The Bayesian EstimatioN of CHange (BENCH) framework was specifically designed to circumvent parameter fitting for ill-conditioned models when one is simply interested in interpreting signal changes related to some variable of interest. To understand the biological underpinning of some observed change in MR signal between different conditions, BENCH predicts which model parameter, or combination of parameters, best explains the observed change, without having to invert the model. BENCH has been previously used to identify which biophysical parameters could explain group-wise dMRI signal differences (e.g. patients vs. controls); here, we adapt BENCH to interpret dMRI signal changes related to continuous variables. We investigate how parameters from the dMRI standard model of white matter, with an additional sphere compartment to represent glial cell bodies, relate to tissue microstructure quantified from histology. We validate BENCH using synthetic dMRI data from numerical simulations. We then apply it to ex-vivo macaque brain data with dMRI and microscopy metrics of glial density, axonal density, and axonal dispersion in the same brain. We found that (i) increases in myelin density are primarily associated with an increased intra-axonal volume fraction and (ii) changes in the orientation dispersion derived from myelin microscopy are linked to variations in the orientation dispersion index. Finally, we found that the dMRI signal is sensitive to changes in glial cell soma in the WM, but that no parameter in the extended standard model was able to explain this observed signal change, suggesting model inadequacy.

neuroscience↗

bioRxiv2024

Imaging the structural connectome with hybrid diffusion MRI-microscopy tractography

Neuroanatomical tract tracing methods are fundamental in providing "gold standard" estimates of brain connectivity. However, tracer methods cannot be performed in humans and even in animals, we can only study projections from typically one or two injection sites per animal sacrificed. Orientation-sensitive microscopy techniques such as PLI provide an alternative where they can visualise detailed fibre orientations at the micron-scale across the whole brain. However, these methods are often most informative on orientations within the 2D imaging plane, with less reliable or missing through-plane information, restricting 3D tract reconstruction. Conversely, dMRI can estimate fibre orientations in 3D but at low resolution, which leads to many false positive and negative estimates of fibre trajectories. To facilitate reconstruction of the microscopy-informed connectome, we develop a data-fusion method that complements 2D microscopy with through-plane information from diffusion MRI to construct 3D hybrid orientations that are both maximally informed by the high-resolution microscopy, have whole-brain coverage and can be input into existing tractography pipelines. Diffusion MRI can be readily acquired prior to microscopy meaning the same method is translatable across species, including in humans. Here we apply our method to an existing open-access macaque dataset and demonstrate (1) whole-brain microscopy-informed tractography (2) the advantages of hybrid tractography in two known tractography challenges, the gyral bias and bottleneck problem (3) how hybrid tractography appears to outperform diffusion-only tractography when compared to tracer data and (4) the generalisability of our hybrid method to different microscopy contrasts, facilitating wider translation.

bioengineering↗

bioRxiv2022

The BigMac dataset: an open resource combining multi-contrast MRI and microscopy in the macaque brain

Understanding brain structure and function often requires combining data across different modalities and scales to link microscale cellular structures to macroscale features of whole brain organisation. Here we introduce the BigMac dataset, a resource combining in vivo MRI, extensive postmortem MRI and multi-contrast microscopy for multimodal characterisation of a single whole macaque brain. The data spans modalities (MRI and microscopy), tissue states (in vivo and postmortem), and four orders of spatial magnitude, from microscopy images with micrometre or sub-micrometre resolution, to MRI signals on the order of millimetres. Crucially, the MRI and microscopy images are carefully co-registered together to facilitate quantitative multimodal analyses. Here we detail the acquisition, curation, and first release of the data, that together make BigMac a unique, openly-disseminated resource available to researchers worldwide. Further, we demonstrate example analyses and opportunities afforded by the data, including improvement of connectivity estimates from ultra-high angular resolution diffusion MRI, neuroanatomical insight provided by polarised light imaging and myelin-stained histology, and the joint analysis of MRI and microscopy data for reconstruction of the microscopy-inspired connectome. All data and code are made openly available.

neuroscience↗