Investigation of the association between central arterial stiffness and aggregate g-ratio in cognitively unimpaired adults

Stiffness of the large arteries has been shown to impact cerebral white matter (WM) microstructure in both younger and older adults. However, no study has yet demonstrated an association between arterial stiffness and aggregate g-ratio, a specific magnetic resonance imaging (MRI) measure of axonal myelination that is highly correlated with neuronal signal conduction speed. In a cohort of 38 well-documented cognitively unimpaired adults spanning a wide age range, we investigated the association between central arterial stiffness, measured using pulse wave velocity (PWV), and aggregate g-ratio, measured using our recent advanced quantitative MRI methodology, in several cerebral WM structures. After adjusting for age, sex, smoking status, and systolic blood pressure, our results indicate that higher PWV values, that is, elevated arterial stiffness, were associated with lower aggregate g-ratio values, that is, lower microstructural integrity of WM. Compared to other brain regions, these associations were stronger and highly significant in the splenium of the corpus callosum and the internal capsules, which have been consistently documented as very sensitive to elevated arterial stiffness. Moreover, our detailed analysis indicates that these associations were mainly driven by differences in myelination, measured using myelin volume fraction, rather than axonal density, measured using axonal volume fraction. Our findings suggest that arterial stiffness is associated with myelin degeneration, and encourages further longitudinal studies in larger study cohorts. Controlling arterial stiffness may represent a therapeutic target in maintaining the health of WM tissue in cerebral normative aging

Accuracy and repeatability of joint sparsity multi-component estimation in MR Fingerprinting

MR fingerprinting (MRF) is a promising method for quantitative characterization of tissues. Often, voxel-wise measurements are made, assuming a single tissue-type per voxel. Alternatively, the Sparsity Promoting Iterative Joint Non-negative least squares Multi-Component MRF method (SPIJN-MRF) facilitates tissue parameter estima-tion for identified components as well as partial volume segmentations. The aim of this paper was to evaluate the accuracy and repeatability of the SPIJN-MRF parameter estimations and partial volume segmentations. This was done (1) through numerical simulations based on the BrainWeb phantoms and (2) using in vivo acquired MRF data from 5 subjects that were scanned on the same week-day for 8 consecutive weeks. The partial volume segmen-tations of the SPIJN-MRF method were compared to those obtained by two conventional methods: SPM12 and FSL. SPIJN-MRF showed higher accuracy in simulations in comparison to FSL-and SPM12-based segmentations: Fuzzy Tanimoto Coefficients (FTC) comparing these segmentations and Brainweb references were higher than 0.95 for SPIJN-MRF in all the tissues and between 0.6 and 0.7 for SPM12 and FSL in white and gray matter and between 0.5 and 0.6 in CSF. For the in vivo MRF data, the estimated relaxation times were in line with literature and minimal variation was observed. Furthermore, the coefficient of variation (CoV) for estimated tissue volumes with SPIJN-MRF were 10.5% for the myelin water, 6.0% for the white matter, 5.6% for the gray matter, 4.6% for the CSF and 1.1% for the total brain volume. CoVs for CSF and total brain volume measured on the scanned data for SPIJN-MRF were in line with those obtained with SPM12 and FSL. The CoVs for white and gray mat-ter volumes were distinctively higher for SPIJN-MRF than those measured with SPM12 and FSL. In conclusion, the use of SPIJN-MRF provides accurate and precise tissue relaxation parameter estimations taking into account intrinsic partial volume effects. It facilitates obtaining tissue fraction maps of prevalent tissues including myelin water which can be relevant for evaluating diseases affecting the white matter.Radiolog

Chronic Neurological Impairment in Patients with Thrombotic Thrombocytopenic Purpura: Findings in Quantitative MRI

Thrombotic thrombocytopenic purpura (TTP) is a life-threatening, microvascular blood disorder that affects approximately 5 people per million per year. The disorder is characterized by insufficient activity in ADAMTS13 (a disintegrin-like and metalloprotease with thrombospondin type 1 repeats 13), which is an important enzyme in hemostasis because it prevents thrombosis. Along with blood clotting, other predominant symptoms are fever, anaemia, kidney failure, and neurological changes. Neurological changes may include confusion and decreased levels of consciousness, as well as depression and increased risk of seizures or stroke. However, little is known about the general pathology of these neurological changes and this forms the motivation for this research. An observational study using a comprehensive MRI protocol was evaluated in 13 patients and compared to results from assessments of depression and cognition. Despite prolonged remission, there is evidence of persistent neurocognitive decline as manifested in higher scores of depression and widespread white matter lesions

Oscillatory and structural measures of connectivity in psychosis, psychosis-risk and the healthy population

Magnetoencephalography (MEG) and Diffusion Tensor Imaging (DTI) are important tools for probing functional and structural properties of the brain. The interactions between the brain’s local and long-range circuitry could provide a key to understanding schizophrenia as a disorder of dysconnectivity and related risk factors in the healthy population. The aim in the first chapters of this thesis was to understand how high frequency local visual circuitry and long-range low frequency connectivity can be best estimated from MEG data. It was shown that using a finer sampling grid in source estimation leads to improved measures of high frequency responses. Furthermore, that networks usually measured in the resting-state can be extracted from task data was another key discovery and has positive implications for data quality and participant comfort going forward. The second aim of this thesis was to understand how specific local and global entities interact by investigating the relationships between local visual circuitry and long-range structural and oscillatory connectivity. An important finding was that the magnitude of local connectivity in the superficial layers of the visual cortex, as probed by gamma amplitude, was associated with reduced long-range connectivity beyond primary visual areas. The other novel finding was that the frequency of local visual oscillations was correlated with structural measures, possibly reflecting increased myelination. The third aim of this work was to better understand how psychosis-risk relates to functional and structural connectivity in health and schizophrenia. Schizotypy was robustly correlated with reduced long-range functional connectivity but not structural connectivity. The opposite was true for correlations between polygenic risk and connectivity. However, the aforementioned risk factors were not robustly correlated with local functional connectivity. The last chapter showed novel but non-significant differences in local and global oscillatory connectivity that were related to excitatory-inhibitory copy number burden in patients

Characterising the structural brain changes in Huntington’s disease using translational neuroimaging

This thesis examined the macro-structural and micro-structural changes in Huntington’s disease (HD) in order to improve understanding of the temporal and spatial patterns of neurodegeneration, and the functional relevance of these changes. Translational techniques were employed using genetic mouse models of HD in combination with a patient cohort to examine grey and white matter changes with a particular focus on white matter microstructure. In the patient cohort, the cognitive profile was examined using a cognitive battery not before applied in HD. Specific deficits were found in set-shifting and flexibility, verbal reasoning, working memory and paired associate learning, along with subtle differences in response inhibition that were sensitive to disease burden. A composite cognitive score was produced to examine the relationship between cognitive function and brain structure. A multi-modal examination of white matter tract-specific microstructural measurements revealed abnormalities in the corpus callosum and cingulum bundle that were sensitive to disease burden (chapter 4). In chapter 5, multiple analysis techniques converged to reveal tissue macrostructure abnormalities that were also sensitive to disease burden in HD. Cortical changes were less consistent, and unlike the microstructure findings, white matter macrostructural abnormalities were not related to disease burden. In chapters 6 and 7, genetic mouse models of HD were used to examine changes across the disease course, and to pilot an interventional design. In vivo diffusion MRI and T2-weighted MRI sequences were acquired at 2 different time points in the HdhQ150 knock-in model of HD and imaging data is presented alongside behavioural results and immunohistochemistry. In chapter 7, an environmental modification regime was tested in the YAC128 mouse model using in vivo MRI. Environmental intervention reduced the degree of disease-related atrophy, altered tissue microstructure and improve motor but not cognitive performance in YAC128 mice