Structural neural networks subserving oculomotor function in first-episode schizophrenia

BACKGROUND: Smooth pursuit and antisaccade abnormalities are well documented in schizophrenia, but their neuropathological correlates remain unclear. METHODS: In this study, we used statistical parametric mapping to investigate the relationship between oculomotor abnormalities and brain structure in a sample of first-episode schizophrenia patients (n = 27). In addition to conventional volumetric magnetic resonance imaging, we also used magnetization transfer ratio, a technique that allows more precise tissue characterization. RESULTS: We found that smooth pursuit abnormalities were associated with reduced magnetization transfer ratio in several regions, predominantly in the right prefrontal cortex. Antisaccade errors correlated with gray matter volume in the right medial superior frontal cortex as measured by conventional magnetic resonance imaging but not with magnetization transfer ratio. CONCLUSIONS: These preliminary results demonstrate that specific structural abnormalities are associated with abnormal eye movements in schizophrenia

The Evaluation of Optic Nerves Using 7 Tesla 'Silent' Zero Echo Time Imaging in Patients with Leber's Hereditary Optic Neuropathy with or without Idebenone Treatment

Magnetic Resonance Imaging (MRI) of the Optic Nerve is difficult due to the fine extended nature of the structure, strong local magnetic field distortions induced by anatomy, and large motion artefacts associated with eye movement. To address these problems we used a Zero Echo Time (ZTE) MRI sequence with an Adiabatic SPectral Inversion Recovery (ASPIR) fat suppression pulse which also imbues the images with Magnetisation Transfer contrast. We investigated an application of the sequence for imaging the optic nerve in subjects with Leber's hereditary optic neuropathy (LHON). Of particular note is the sequence's near-silent operation, which can enhance image quality of the optic nerve by reducing the occurrence of involuntary saccades induced during Magnetic Resonance (MR) scanning

Exploring configuration-based relaxometry and imaging with balanced steady-state free precession

Over the years, magnetic resonance (MR) imaging has become a fundamental part of the diagnostic process in hospitals worldwide. While the underlying physics dates back more than 60 years with the development of nuclear magnetic resonance, methods that aim to accurately measure the multitude of parameters governing the signal formation are still a topic of active research and developments today. The main aim of this work is to explore the possibilities of developing quantification techniques based on a particular type of MR acquisitions: balanced Steady-State Free Precession (bSSFP). The first chapter briefly introduces the most relevant basic concepts of MR physics that will serve as foundation for the development of the methods presented thereafter. In the second chapter, a new method using multiple bSSFP scans is presented that aim to achieve motion insensitive three-dimensional quantification of relaxation times and thereby improve a recently published technique based on unbalanced gradient echo acquisitions. The method is then evaluated both in phantoms and in vivo studies at 3 T and the results discussed. These include an interesting bias of the method that might provide useful insights into the underlying tissue micro-structure. One the major challenges with bSSFP imaging is the presence of dark regions inside the images, which are due to inhomogeneities in the main magnetic field. In the third chapter, a new approach to address those issues is proposed, termed trueCISS, which combines fast imaging using sparse sampling with compressed sensing reconstructions and multi-parametric fitting, which ultimately allows the synthesis of artefact-free images. Evaluation of the new method is done at 3 T for the human brain. Finally, an extension of the trueCISS technique is presented in chapter four, where the process used to model the data to the signal equation is replaced by a novel algorithm based on configuration theory, which is essentially a representation of the signal formation processes in the Fourier domain. The improved trueCISS imaging method is then successfully evaluated with measurements at ultra high field strengths such as 7 T and 9.4 T which demonstrate the advantages of the new approach

Feasibility of in vivo multi-parametric quantitative magnetic resonance imaging of the healthy sciatic nerve with a unified signal readout protocol

Magnetic resonance neurography (MRN) has been used successfully over the years to investigate the peripheral nervous system (PNS) because it allows early detection and precise localisation of neural tissue damage. However, studies demonstrating the feasibility of combining MRN with multi-parametric quantitative magnetic resonance imaging (qMRI) methods, which provide more specific information related to nerve tissue composition and microstructural organisation, can be invaluable. The translation of emerging qMRI methods previously validated in the central nervous system to the PNS offers real potential to characterise in patients in vivo the underlying pathophysiological mechanisms involved in a plethora of conditions of the PNS. The aim of this study was to assess the feasibility of combining MRN with qMRI to measure diffusion, magnetisation transfer and relaxation properties of the healthy sciatic nerve in vivo using a unified signal readout protocol. The reproducibility of the multi-parametric qMRI protocol as well as normative qMRI measures in the healthy sciatic nerve are reported. The findings presented herein pave the way to the practical implementation of joint MRN-qMRI in future studies of pathological conditions affecting the PNS

hMRI - A toolbox for quantitative MRI in neuroscience and clinical research

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates and , proton density and magnetisation transfer saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research

The z-spectrum from human blood at 7T

Chemical Exchange Saturation Transfer (CEST) has been used to assess healthy and pathological tissue in both animals and humans. However, the CEST signal from blood has not been fully assessed. This paper presents the CEST and nuclear Overhauser enhancement (NOE) signals detected in human blood measured via z-spectrum analysis. We assessed the effects of blood oxygenation levels, haematocrit, cell structure and pH upon the z-spectrum in ex vivo human blood for different saturation powers at 7T. The data were analysed using Lorentzian difference (LD) model fitting and AREX (to compensate for changes in T1), which have been successfully used to study CEST effects in vivo. Full Bloch-McConnell fitting was also performed to provide an initial estimate of exchange rates and transverse relaxation rates of the various pools. CEST and NOE signals were observed at 3.5 ppm, -1.7ppm and -3.5 ppm and were found to originate primarily from the red blood cells (RBCs), although the amide proton transfer (APT) CEST effect, and NOEs showed no dependence upon oxygenation levels. Upon lysing, the APT and NOE signals fell significantly. Different pH levels in blood resulted in changes in both the APT and NOE (at -3.5ppm), which suggests that this NOE signal is in part an exchange relayed process. These results will be important for assessing in vivo z-spectra

Quantitative MRI and machine learning for the diagnosis and prognosis of Multiple Sclerosis

Multiple sclerosis (MS) is an immune-mediated, inflammatory, neurological disease affecting myelin in the central nervous system, whose driving mechanisms are not yet fully understood. Conventional magnetic resonance imaging (MRI) is largely used in the MS diagnostic process, but because of its lack of specificity, it cannot reliably detect microscopic damage. Quantitative MRI provides instead feature maps that can be exploited to improve prognosis and treatment monitoring, at the cost of prolonged acquisition times and specialised MR-protocols. In this study, two converging approaches were followed to investigate how to best use the available MRI data for the diagnosis and prognosis of MS. On one hand, qualitative data commonly used in clinical research for lesion and anatomical purposes were shown to carry quantitative information that could be used to conduct myelin and relaxometry analyses on cohorts devoid of dedicated quantitative acquisitions. In this study arm, named bottom-up, qualitative information was up-converted to quantitative surrogate: traditional model-fitting and deep-learning frameworks were proposed and tested on MS patients to extract relaxometry and indirect-myelin quantitative data from qualitative scans. On the other hand, when using multi-modal MRI data to classify MS patients with different clinical status, different MR-features contribute to specific classification tasks. The top-down study arm consisted in using machine learning to reduce the multi-modal dataset dimensionality only to those MR-features that are more likely to be biophysically meaningful with respect to each MS phenotype pathophysiology. Results show that there is much more potential to qualitative data than lesion and tissue segmentation, and that specific MRI modalities might be better suited for investigating individual MS phenotypes. Efficient multi-modal acquisitions informed by biophysical findings, whilst being able to extract quantitative information from qualitative data, would provide huge statistical power through the use of large, historical datasets, as well as constitute a significant step forward in the direction of sustainable research