In this thesis, new acquisition and analysis methods are described for multinuclear magnetic resonance spectroscopy (MRS) for the quantification of brain metabolites at ultra high magnetic field strengths (7T). An analytical model was derived for the optimisation of the stimulated echo acquisition mode (STEAM) sequence timing parameters for lactate detection. The effects of the chemical shift displacement artefact on the J-modulated signal for a weakly-coupled spin system were considered in the three applied directions of field gradients and the product operator formalism was used to obtain expressions for the signal modulation in each compartment of the excited volume. The validity of this model was demonstrated experimentally in a phantom and acquisitions with optimised parameters were performed on a healthy volunteer. The spectra acquired with an echo time (TE) of 144 ms and with an optimised mixing time and TE of 288 ms showed easily detectable lactate peaks in the normal human brain. Additionally, the acquisition with the longer TE resulted in a spectrum with less lipid/macromolecular (MM) contamination. The simulations demonstrated that the proposed analytical model is suitable for correctly predicting the resulting lactate signal. With the optimised parameters, it was possible to use a simple sequence with sufficient signal-to-noise ratio (SNR) to reliably distinguish lactate from overlapping resonances in a healthy brain at ultra high-field. Estimation of metabolic changes during neuronal activation represents a challenge for in vivo MRS, especially for metabolites with low concentration and signal overlap, such as lactate. This thesis also includes work focused on the reliable quantification of lactate during a paradigm with 15 minutes of visual stimulation. The lipid and MM signals were significantly reduced by using a long TE (144 ms) sequence and the remaining MM signals in the vicinity of the lactate peak were individually fitted with simulated Lorentzian peaks, to ensure a good fit of the inverted lactate doublet. Statistically significant changes in lactate (~10%) and glutamate (~3%) levels during stimulation were detected in the visual cortex and agree with previous measurements. Furthermore, the use of a prolonged stimulation period unveiled a distinctive metabolic response pattern, which can provide further insight into brain activation mechanisms. 13C MRS combined with the infusion of labelled substrates is able to provide unique information on the relationship between neuroenergetics and brain function. However, the lack of sensitivity associated with the general complexity of 13C experiments has hampered its widespread use for research into human brain disease. In this study, a new methodology for acquisition and analysis of 13C signal is presented for the study of neuroenergetics and neurotransmission in a deep brain structure - anterior cingulate cortex - that is thought to play a major role in the processing of sensory information and can be impaired in patients with schizophrenia. In vitro testing was performed to evaluate the performance of the implemented sequence for signal localisation and polarisation transfer, both proving adequate for the intended purpose. In vivo data were acquired in four subjects, one diagnosed with early schizophrenia, with a protocol which involved 60 minutes of infusion of [1-13C]glucose. Turnover curves for the labelled products were generated from the dynamic 13C spectra with a temporal resolution of 10 minutes and were in agreement with the ones obtained from rodent experiments. Therefore, the feasibility of 13C experiments for the study of psychosis was here demonstrated, taking advantage of the increase in SNR at ultra high-field for determination of metabolic fluxes.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:728570 |
| Date | January 2017 |
| Creators | Fernandes, Carolina C. |
| Publisher | University of Nottingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://eprints.nottingham.ac.uk/46607/ |
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