Diffusion kurtosis imaging (DKI) is an imaging modality that yields novel disease biomarkers and in combination with nervous tissue modeling, provides access to microstructural parameters. Recently, DKI and subsequent estimation of microstructural model parameters has been used for assessment of tissue changes in neurodegenerative diseases and associated animal models. In this study, mouse spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using DKI in combination with biophysical modeling to study the relationship between microstructural metrics and degree of animal dysfunction. Thirteen spinal cords were extracted from animals with varied grades of disability and scanned in a high-field MRI scanner along with five control specimen. Diffusion weighted data were acquired together with high resolution T2* images. Diffusion data were fit to estimate diffusion and kurtosis tensors and white matter modeling parameters, which were all used for subsequent statistical analysis using a linear mixed effects model. T2* images were used to delineate focal demyelination/inflammation. Our results reveal a strong relationship between disability and measured microstructural parameters in normal appearing white matter and gray matter. Relationships between disability and mean of the kurtosis tensor, radial kurtosis, radial diffusivity were similar to what has been found in other hypomyelinating MS models, and in patients. However, the changes in biophysical modeling parameters and in particular in extra-axonal axial diffusivity were clearly different from previous studies employing other animal models of MS. In conclusion, our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes correlating with clinical disability.

Thanks to its proven utility in both clinical and research applications, diffusion tensor tractography (DTT) is regularly employed as a means of delineating white-matter tracts. While successful efforts have been made to validate tractographic predictions, comparative methods which would permit the validation of such predictions at microscopic resolutions in complex biological tissues have remained elusive. In a previous study, we attempted to validate for the first time such predictions at microscopic resolutions in rat and pig spinal cords using a semi-quantitative analysis method. In the current study, we report improved quantitative analysis methods that can be used to determine the accuracy of DTT through comparative histology and apply these techniques for the first time to human tissue (spinal cord) samples. Histological images are down-sampled to resolutions equivalent to our magnetic resonance microscopy (MRM) and converted to binary maps using an automated thresholding tool. These maps (n=3) are co-registered to the MRM allowing us to quantify the agreement based on the number of pixels which contain tracts common to both imaging datasets. In our experiments, we find that-on average-89% of imaging pixels predicted by DTT to contain in-plane white-matter tract structure correspond to physical tracts identified by histology. In addition, angular analysis comparing the orientation of fiber tracts measured in histology to their corresponding in-plane primary eigenvector components is presented. Thus, as well as demonstrating feasibility in human tissue, we report a robust agreement between imaging datasets taken at microscopic resolution and confirm the primary eigenvector's role as a fundamental parameter with clear physical correlates in the microscopic regime.

Since its first description and development in the late 20th century, diffusion magnetic resonance imaging (dMRI) has proven useful in describing the microstructural details of biological tissues. Signal generated from the protons of water molecules undergoing Brownian motion produces contrast based on the varied diffusivity of tissue types. Images employing diffusion contrast were first used to describe the diffusion characteristics of tissues, later used to describe the fiber orientations of white matter through tractography, and most recently proposed as a functional contrast method capable of delineating neuronal firing in the active brain. Thanks to the molecular origins of its signal source, diffusion contrast is inherently useful at describing features of the microenvironment; however, limitations in achievable resolution in magnetic resonance imaging (MRI) scans precluded direct visualization of tissue microstructure for decades following MRI's inception as an imaging modality. Even after advancements in MRI hardware had permitted the visualization of mammalian cells, these specialized systems could only accommodate fixed specimens that prohibited the observation and characterization of physiological processes. The goal of the current study was to visualize cellular structure and investigate the subcellular origins of the functional diffusion contrast mechanism (DfMRI) in living, mammalian tissue explants. Using a combination of ultra-high field spectrometers, micro radio frequency (RF) coils, and an MRI-compatible superfusion device, we are able to report the first live, mammalian cells-α-motor neurons-visualized with magnetic resonance microscopy (MRM). We are also able to report changes in the apparent diffusion of the stratum oriens within the hippocampus-a layer comprised primarily of pyramidal cell axons and basal dendrites-and the spinal cord's ventral horn following exposure to kainate.