Understanding the biophysical basis of functional Magnetic Resonance Spectroscopy measurements in the human brain.

Magnetic resonance spectroscopy (MRS) is a valuable tool used to study the chemical composition of the human brain. The technique has recently gained popularity in neuroscience research as it allows robust and reliable measurements of glutamate and y-aminobutyric acid (GABA), the brain's primary excitatory and inhibitory neurotransmitters, using normal MRI scanners (Nezhad et al, 2019). Research using MRS measurements of GABA has enhanced our understanding of the underlying biochemistry of healthy brain function such as in motor learning (Floyer-Lea et al, 2006) and pain perception (Gussew et al, 2010). Similarly, MRS studies of glutamate have led us to understand the role that the brain's primary excitatory neurotransmitter plays in visual, sensory and pain processes (see (Mullins, 2019) for a review). In addition, in recent years there have been several studies measuring changes in the concentrations of metabolites as a function of time, either in response to neural activation or immediately pre and post-stimulation, which we term functional MRS (fMRS).

However, the physiological basis for the change in the fMRS signal is not clear. One hypothesis is that neurotransmitters shift between distinct metabolic pools within the neurons and surrounding cells. MRS has differing sensitivity to neurotransmitters in these pools and by controlled stimulation of the brain it is possible to cause a shift of neurotransmitters from one pool to another, resulting in an MRS signal change. However, in order to test the proposed hypothesis it is essential to link the microscopic dynamics of neurotransmitters to the output MRS signal measured in experiments. In this project, we will combine novel methods of MRS acquisition with computer simulations of the underlying physical mechanisms responsible for shaping the spectroscopic signal. The student will learn to collect and analyse MRS data, initially using an existing data-set collected to measure the glutamate response to a visual stimulus in an event-related manner. The student will also receive training in building and implementing a mathematical model linking neural activity to the MRS signal. This model will be coupled to an existing biophysical model of neurotransmitter dynamics, which together will result in a powerful tool to make testable predictions.

The ideal candidate will have a background in a mathematical or computational discipline (physics, mathematics, computer science) and familiarity with computer programming. Some previous exposure to neuroscience will be an advantage, and high motivation to undertake a challenging interdisciplinary topic is essential.