Determinants of brain glutamate functional magnetic resonance spectroscopy response

脑谷氨酸功能磁共振波谱反应的决定因素

• 批准号: RGPIN-2022-04425
• 负责人: Theberge, Jean
• 金额: $ 1.75万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760742
• 关键词: Determinants; brain; glutamate; functional; magnetic

Studying the living healthy human brain non-invasively is important in understanding the dynamic interaction between connected brain regions part of a network involved in performing a given task. Functional Magnetic Resonance Spectroscopy (fMRS) of the human brain has been a useful way to study dynamic changes in brain chemicals involved in brain excitation (glutamate), inhibition (GABA) and oxidative stress (glutathione). Using fMRS, levels of individual brain chemicals have now been published, but there is currently no single theoretical framework that allows a satisfactory explanation of the wide range of glutamatergic metabolite responses observed in various functional stimulation types or tasks. This limited understanding of how the healthy brain performs tasks ultimately limits our ability to interpret chemical responses in unhealthy brains. We previously developed optimized fMRS measurement protocols for the observation of brain glutamate, the brain's main excitatory neurotransmitter. We now propose detailed observations of a more complete array of glutamatergic metabolites markers of excitation (glutamate, glutamine), inhibition (GABA) and oxidative stress (glutathione) in basic tasks aimed at understanding the effect of stimuli intensity, duration, timing and nature to provide the data necessary to construct more broadly valid models of brain region interactions. This is a critical step in the maturing of fMRS techniques that will deepen our understanding of normal glutamatergic brain responses beyond what has already been learned from hemodynamic investigations using blood oxygenation-dependent functional MRI (fMRI), the original MRI-based approach that has powered the neuroscience revolution of the last 3 decades. To reach this objective, however, fMRS data acquisition techniques and data analysis tools still require much development and a new generation of scientist familiar with these approach needs to be trained if fMRS is to achieve the same level of scientific sophistication as functional MRI. We propose to train highly qualified personnel pursuing the following three aims of our research program: 1) Optimize the fMRS data acquisition of glutamatergic metabolites by implementing metabolite-selective fMRS methods that isolate the MRS signals of up to 3 metabolites of interest in a single-shot acquisition, extending our current metabolite-selective work in resting MRS. 2) Characterize how glutamatergic fMRS signals vary in connected brain regions of the visual network via temporally interleaved fMRS measurements of glutamatergic metabolite in response to simple photic stimulation tasks of varying duration and intensity. 3) Develop multi-voxel fMRS analysis methods to identify the portion of the fMRS signal that participates to the connection between the selected brain regions by analyzing fMRS time courses using independent component analysis agnostic of the stimulation paradigm used.

对健康的活体大脑进行非侵入性研究，对于理解参与执行特定任务的大脑网络部分的连接区域之间的动态相互作用具有重要意义。脑功能磁共振波谱（fMRS）已成为研究脑兴奋（谷氨酸）、抑制（GABA）和氧化应激（谷胱甘肽）等脑化学物质动态变化的有效方法。使用fMRS，个体大脑化学物质的水平已经发表，但目前还没有一个单一的理论框架可以令人满意地解释在各种功能刺激类型或任务中观察到的广泛的谷氨酸代谢物反应。这种对健康大脑如何执行任务的有限理解最终限制了我们解释不健康大脑中化学反应的能力。我们之前开发了优化的fMRS测量方案，用于观察脑谷氨酸，大脑的主要兴奋性神经递质。我们现在提出在基本任务中对谷氨酸能代谢产物(兴奋（谷氨酸，谷氨酰胺），抑制（GABA）和氧化应激（谷胱甘肽）的更完整的标记物进行详细观察，旨在了解刺激强度，持续时间，时间和性质的影响，为构建更广泛有效的脑区域相互作用模型提供必要的数据。这是fMRS技术成熟的关键一步，它将加深我们对正常谷氨酸脑反应的理解，超越已经从使用血氧依赖功能MRI （fMRI）的血液动力学研究中所学到的知识，fMRI是过去30年来推动神经科学革命的原始基于MRI的方法。然而，为了达到这一目标，fMRS数据采集技术和数据分析工具仍然需要大量的发展，如果fMRS要达到与功能MRI相同的科学复杂程度，就需要培训熟悉这些方法的新一代科学家。我们建议培养高素质人才，以实现我们的研究计划的以下三个目标：1)通过实施代谢物选择性fMRS方法，优化谷氨酸能代谢物的fMRS数据采集，该方法可在一次采集中分离多达3种感兴趣的代谢物的MRS信号；2)通过对不同持续时间和强度的简单光刺激任务的谷氨酸能代谢物的时间交错fMRS测量，表征谷氨酸能fMRS信号在视觉网络连接的大脑区域中的变化。3)开发多体素fMRS分析方法，利用独立分量分析方法分析fMRS时间过程，识别参与选定脑区之间连接的fMRS信号部分，而不考虑所使用的刺激范式。