From micro‐ to macro‐structures in multiple sclerosis: what is the added value of diffusion imaging

Diffusion imaging has been instrumental in understanding damage to the central nervous system as a result of its sensitivity to microstructural changes. Clinical applications of diffusion imaging have grown exponentially over the past couple of decades in many neurological and neurodegenerative diseases, such as multiple sclerosis (MS). For several reasons, MS has been extensively researched using advanced neuroimaging techniques, which makes it an ‘example disease’ to illustrate the potential of diffusion imaging for clinical applications. In addition, MS pathology is characterized by several key processes competing with each other, such as inflammation, demyelination, remyelination, gliosis and axonal loss, enabling the specificity of diffusion to be challenged. In this review, we describe how diffusion imaging can be exploited to investigate micro‐, meso‐ and macro‐scale properties of the brain structure and discuss how they are affected by different pathological substrates. Conclusions from the literature are that larger studies are needed to confirm the exciting results from initial investigations before current trends in diffusion imaging can be translated to the neurology clinic. Also, for a comprehensive understanding of pathological processes, it is essential to take a multiple‐level approach, in which information at the micro‐, meso‐ and macroscopic scales is fully integrated

Radiological Pathological Correlations in Chronic Traumatic Encephalopathy

Chronic traumatic encephalopathy (CTE) is a progressive neurodegenerative disease that has been increasingly linked to traumatic brain injury. The neuropathology that distinguishes CTE from other tauopathies includes hyperphosphorylated tau (pTau) tangles and tau positive astrocytes irregularly distributed in cortical sulcal depths and clustered around perivascular foci. These features are clearly identified using immunohistochemistry, but are undetectable to current clinical imaging methods. Diffusion imaging has been proposed as a noninvasive method to detect the pathognomonic lesion of CTE in vivo because of its high sensitivity to microstructural alterations in tissue structure. While several diffusion imaging approaches, ranging from diffusion tensor imaging (DTI) to more advanced schemes such as generalized q-sampling imaging (GQI) and diffusion kurtosis imaging (DKI) may prove useful, the relationship between changes in diffusion-derived metrics and the underlying pathology remains unknown. We have developed and implemented a method of perform radiological-pathological correlations in tissues with diagnoses of CTE, aimed to determine whether high spatial resolution diffusion imaging is capable of sensitively detecting pTau pathology. Human ex vivo cortical tissues diagnoses with Stage III/IV CTE, Alzheimer’s disease (AD) or frontotemporal lobar dementia (FTLD) were scanned in an 11.74T Agilent MRI scanner using DTI, GQI and DKI acquisition schemes with isotropic in-plane spatial resolution of 250µm and 500µm slice thickness. Following image acquisition, tissues were sectioned and stained for histopathological markers including AT8 (pTau), GFAP (astrocytes) and Myelin Black Gold II (myelinated white matter). A custom script was used to co-register histological to MRI images, allowing for the ability to perform high spatial resolution correlations of histological with diffusion metrics. Using this approach, we found no relationship between pTau in sulcal depths and any of our DTI, GQI and DKI based measures. Interestingly, we found that white matter underlying sulcal depths in CTE tissues showed signs of disruption, a finding that we did not observe in AD or FTLD tissues. Furthermore, white matter integrity in these regions was correlated with fractional anisotropy. These findings demonstrate that high spatial resolution diffusion imaging is capable of detecting white matter disorganization closely related to pTau pathology in CTE, and may provide a more sensitive and specific means of diagnosing CTE

Improved Quantification of Connectivity in Human Brain Mapping

Diffusion magnetic resonance imaging (dMRI) is an advanced MRI methodology that can be used to probe the microstructure of biological tissue. dMRI can provide orientation information by modeling the process of water diffusion in white matter. This thesis presents contributions in three areas of diffusion imaging technology: diffusion reconstruction, quantification, and validation of derived metrics. It presents a novel reconstruction method by combining generalized q-sampling imaging, spherical harmonic basis functions and constrained spherical deconvolution methods to estimate the fiber orientation distribution function (ODF). This method provides improved spatial localization of brain nuclei and fiber tract separation. A novel diffusion anisotropy metric is presented that provides anatomically interpretable measurements of tracts that are robust in crossing areas of the brain. The metric, directional Axonal Volume (dAV) provides an estimate of directional water content of the tract based on the (ODF) and proton density map. dAV is a directionally sensitive metric and can separate anisotropic water content for each fiber population, providing a quantification in milliliters of water. A method is provided to map voxel-based dAV onto tracts that is not confounded by crossing areas and follows the tract morphology. This work introduces a novel textile based hollow fiber anisotropic phantom (TABIP) for validation of reconstruction and quantification methods. This provides a ground truth reference for axonal scale water tubular structures arranged in various anatomical configurations, crossing and mixing patterns. Analysis shows that: 1) the textile tracts are identifiable with scans used in human imaging and produced tracts and voxel metrics in the range of human tissue; 2) the current methods could resolve crossing at 90o and 45o but not 30o; 3) dAV/NODDI model closely matches (r=0.95) the number of fibers whereas conventional metrics poorly match (i.e., FA r=0.32). This work represents a new accurate quantification of axonal water content through diffusion imaging. dAV shows promise as a new anatomically interpretable metric of axonal connectivity that is not confounded by factors such as axon dispersion, crossing and local isotropic water content. This will provide better anatomical mapping of white matter and potentially improve the detection of axonal tract pathology