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 load in the brain white matter, though no single parameter in the extended standard model was able to explain this observed signal change.

We introduce FSL-DIVE, an interactive visualisation software for diffusion microstructure modelling. This software allows the user to (i) flexibly set up a multi-compartment microstructure model, including fibre orientation modelling and restricted diffusion, (ii) choose different acquisition parameters, (iii) change the model parameters and analyse the model behaviour through multiple bespoke plots, (iv) save the predicted data (including noise) for any specified acquisition scheme. We hope this tool may be useful for teaching diffusion microstructure modelling, for developing new models, or for getting a better understanding of existing models.

Acquisition of MRI and histology in the same ex-vivo tissue sample enables direct correlation between MR and histologically-derived metrics. Here, we analysed immunohistochemistry images of human visual cortex, anterior cingulate and hippocampus to produce stained area fraction maps for myelin, neurofilaments and microglia. We performed voxelwise correlations between MR parameters (FA, MD, R2*, R1) and histology maps to generally characterise the strength of relationships. We then used partial correlation to identify the unique variance in MR parameters explained by each histological feature, and multiple linear regression to explore how well multiple microstructural properties can together explain MR parameters.

Background Accurate registration between microscopy and MRI data is necessary for validating imaging biomarkers against neuropathology, and to disentangle complex signal dependencies in microstructural MRI. Existing registration methods often rely on serial histological sampling or significant manual input, providing limited scope to work with a large number of stand-alone histology sections. Here we present a customisable pipeline to automate the registration of stand-alone histology sections to whole-brain MRI data. Methods Our pipeline registers stained histology sections to whole-brain post-mortem MRI in 4 stages, with the help of two photographic intermediaries: a block face image (to undistort histology sections) and coronal brain slice photographs (to insert them into MRI space). Each registration stage is implemented as a configurable stand-alone Python script using our novel platform, Tensor Image Registration Library (TIRL), which provides flexibility for wider adaptation. We report our experience of registering 87 PLP-stained histology sections from 14 subjects and perform various experiments to assess the accuracy and robustness of each stage of the pipeline. Results All 87 histology sections were successfully registered to MRI. Histology-to-block registration (Stage 1) achieved 0.2-0.4 mm accuracy, better than commonly used existing methods. Block-to-slice matching (Stage 2) showed great robustness in automatically identifying and inserting small tissue blocks into whole brain slices with 0.2 mm accuracy. Simulations demonstrated sub-voxel level accuracy (0.13 mm) of the slice-to-volume registration (Stage 3) algorithm, which was observed in over 200 actual brain slice registrations, compensating 3D slice deformations up to 6.5 mm. Stage 4 combined the previous stages and generated refined pixelwise aligned multi-modal histology-MRI stacks. Conclusions Our open-source pipeline provides robust automation tools for registering stand-alone histology sections to MRI data with sub-voxel level precision, and the underlying framework makes it readily adaptable to a diverse range of microscopy-MRI studies. Highlights New software framework for prototyping bespoke image registration pipelines Automated pipeline to register stand-alone histology sections to whole-brain MRI Novel deformable slice-to-volume registration algorithm No strict necessity for serial histological sectioning for MRI-histology registration

Combining histology and ex vivo MRI from the same mouse brain is a powerful way to study brain microstructure. Mouse brains prepared for ex vivo MRI are often kept in storage solution for months, potentially becoming brittle and showing reduced antigenicity. Here, we describe a protocol for mouse brain dissection, tissue processing, paraffin embedding, sectioning, and staining. We then detail registration of histology to ex vivo MRI data from the same sample and extraction of quantitative histological measurements.

The acquisition of MRI and histology in the same post-mortem tissue sample enables direct correlation between MRI and histologically-derived parameters. However, there still lacks a standardised automated pipeline to process histology data, with most studies relying on manual intervention. Here, we introduce an automated pipeline to extract a quantitative histological measure for staining density (stain area fraction, SAF) from multiple immunohistochemical (IHC) stains. The pipeline is designed to directly address key IHC artefacts related to tissue staining and slide digitisation. Here, the pipeline was applied to post-mortem human brain data from multiple subjects, relating MRI parameters (FA, MD, RD, AD, R2*, R1) to IHC slides stained for myelin, neurofilaments, microglia and activated microglia. Utilising high-quality MRI-histology co-registrations, we then performed whole-slide voxelwise comparisons (simple correlations, partial correlations and multiple regression analyses) between multimodal MRI- and IHC-derived parameters. The pipeline was found to be reproducible, robust to artefacts and generalisable across multiple IHC stains. Our partial correlation results suggest that some simple MRI-SAF correlations should be interpreted with caution, due to the co-localisation of other tissue features (e.g., myelin and neurofilaments). Further, we find activated microglia-a generic biomarker of inflammation-to consistently be the strongest predictor of high DTI FA and low RD, which may suggest sensitivity of diffusion MRI to aspects of neuroinflammation related to microglial activation, even after accounting for other microstructural changes (demyelination, axonal loss and general microglia infiltration). Together, these results show the utility of this approach in carefully curating IHC data and performing multimodal analyses to better understand microstructural relationships with MRI.

Brain myelin and iron content are important parameters in neurodegenerative diseases such as multiple sclerosis (MS). Both myelin and iron content influence the brain's R2* relaxation rate. However, their quantification based on R2* maps requires a realistic tissue model that can be fitted to the measured data. In structures with low myelin content, such as deep gray matter, R2* shows a linear increase with increasing iron content. In white matter, R2* is not only affected by iron and myelin but also by the orientation of the myelinated axons with respect to the external magnetic field. Here, we propose a numerical model which incorporates iron and myelin, as well as fibre orientation, to simulate R2* decay in white matter. Applying our model to fibre orientation-dependent in vivo R2* data, we are able to determine a unique solution of myelin and iron content in global white matter. We determine an averaged myelin volume fraction of 16.02 ± 2.07% in non-lesional white matter of patients with MS, 17.32 ± 2.20% in matched healthy controls, and 18.19 ± 2.98% in healthy siblings of patients with MS. Averaged iron content was 35.6 ± 8.9 mg/kg tissue in patients, 43.1 ± 8.3 mg/kg in controls, and 47.8 ± 8.2 mg/kg in siblings. All differences in iron content between groups were significant, while the difference in myelin content between MS patients and the siblings of MS patients was significant. In conclusion, we demonstrate that a model that combines myelin-induced orientation-dependent and iron-induced orientation-independent components is able to fit in vivo R2* data.

The BigMac dataset is an open access resource combining in vivo MRI, postmortem MRI and multi-contrast microscopy data in a single, whole macaque brain. Here we perform data-driven segmentation of the BigMac histology slides to extract quantitative microscopy metrics for myelin, cell density, and cellular morphology (e.g. soma size and packing). Utilising high-quality MRI-microscopy registrations, we work towards building 3D volumes of quantitative microscopy derived metrics obtained at high resolution. This “ground truth” atlas of how cellular distributions vary across the brain is then directly related to co-registered diffusion MRI acquired in the same tissue.

Biophysical modelling of diffusion MRI (dMRI) may elucidate key microstructural features. However, most models include many input parameters, making simultaneous estimations of all parameters ill-posed. To overcome this, the recently published Bayesian framework EstimatioN for CHange (BENCH) characterises changes (variation) in parameters across multiple measurements/samples, rather than inferring the actual parameters from a single measurement/sample. BENCH has been previously applied to understand group-wise changes (e.g., patients vs. controls) in biophysical parameters. Here, we adapted BENCH to interpret situations of continuous change and validate its behaviour using synthetic dMRI data from numerical simulations.

Motivation: Voxel-wise MRI-histology comparisons routinely rely on manual segmentations of ROIs and subjective quantitative histological metrics, an error-prone and labour-intensive process. Goal(s): We developed an automated framework for investigating relationships between multiple MRI metrics and immunostains. Approach: We used MRI and histology protocols optimised for ex-vivo mouse brains. We co-registered this data and derived quantitative histological metrics. We conducted voxel-wise correlations in grey and white matter between MRI (diffusion, R2* and susceptibility) and immunohistochemistry (myelin, neurofilament and extracellular matrix proteins). Results: Our framework successfully recapitulated known relationships for myelin and neurofilaments and, interestingly, demonstrated new relationships between MRI metrics and extracellular matrix protein stains. Impact: Our optimised framework combines openly-shared software and MRI-histology protocols, addressing current challenges, such as obtaining high-quality histology data, MRI-to-histology registration and automatic extraction of quantitative histological metrics. This can benefit future MRI-histology studies in mouse brains prepared for ex-vivo MRI.