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

磁共振波谱(MRS)是用于研究人脑化学成分的宝贵工具。这项技术最近在神经科学研究中越来越受欢迎，因为它可以使用正常的MRI扫描仪，对大脑主要的兴奋性和抑制性神经递质谷氨酸和γ-氨基丁酸(GABA)进行可靠而可靠的测量(Nezhad等人，2019年)。使用MRS测量GABA的研究加深了我们对健康大脑功能的潜在生物化学的理解，例如在运动学习(Floyer-Lea等人，2006年)和疼痛感知(Gussew等人，2010年)中。同样，对谷氨酸的MRS研究使我们了解了大脑的主要兴奋性神经递质在视觉、感觉和疼痛过程中所起的作用(见(Mullins，2019)的综述)。此外，近年来有几项研究测量了代谢物浓度随时间的变化，要么是对神经激活的反应，要么是刺激前后的即刻变化，我们称之为功能性MRS(FMRS)。然而，FMRS信号变化的生理基础尚不清楚。一种假设是，神经递质在神经元和周围细胞内不同的代谢池之间转移。MRS对这些池中的神经递质具有不同的敏感性，通过对大脑的受控刺激，可能会导致神经递质从一个池转移到另一个池，导致MRS信号变化。然而，为了验证提出的假设，必须将神经递质的微观动力学与实验中测量的输出MRS信号联系起来。在这个项目中，我们将结合新的MRS获取方法和计算机模拟负责形成光谱信号的基本物理机制。学生将学习收集和分析MRS数据，首先使用收集的现有数据集以事件相关的方式测量谷氨酸对视觉刺激的反应。学生还将接受建立和实施将神经活动与MRS信号联系起来的数学模型的培训。这个模型将与现有的神经递质动力学生物物理模型相结合，共同产生一个强大的工具来进行可测试的预测。理想的应聘者应具有数学或计算学科(物理、数学、计算机科学)的背景，并熟悉计算机编程。以前接触过一些神经科学将是一个优势，承担一个具有挑战性的跨学科主题的高动力是必不可少的。

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.