TRD 2: Metabolic Markers

TRD2：代谢标记物 首席研究员：理查德·埃登，放射学教授 共同调查人员：Peter van Zijl，Peter Barker，Georg Oeltzschner，Jiadi Xu，Hye-Young Heo， 尼尔巴伊·亚达夫。 这项研究将利用磁共振波谱技术开发大脑新陈代谢的生物标记物。 (MRS)和化学交换饱和转移成像(CEST-MRI)。我们将开发新的 编辑多路复用编辑MRS的计划，允许同时解决多个问题 合作项目感兴趣的低浓度代谢物，包括神经递质 如GABA，抗氧化剂如谷胱甘肽，以及神经调节剂，如N- 乙酰天冬氨酸谷氨酸。我们将把这些方法与光谱成像读数结合起来。 并发展了多光谱线性组合建模方法进行定量化。编辑过的 CEST方法，调整采集参数以提高检测的特异性，CEST 将开发指纹识别和单次Z-光谱方法。深度学习工具 对于这些数据的分析也将制定，并特别注重整合 方法，以及基于MRSI的光谱分辨率增强和代谢编辑 CEST分析的培训。根据这项TRD开发的获取和分析工具将是 传播到协作项目和服务项目，以在新的 环境，以及更广泛的社区。

TRD 2: Metabolic Markers Lead Principal investigator: Richard Edden, Professor of Radiology Co-investigators: Peter van Zijl, Peter Barker, Georg Oeltzschner, Jiadi Xu, Hye-Young Heo, Nirbhay Yadav. This TRD will develop biomarkers of brain metabolism, using magnetic resonance spectroscopy (MRS) and chemical exchange-saturation transfer imaging (CEST-MRI). We will develop new editing schedules for multiplexed editing MRS, allowing the simultaneous resolution of several low-concentration metabolites of interest to collaborative projects, including neurotransmitters such as GABA, antioxidants such as glutathione, and neuromodulators such as N- acetylaspartylglutamate. We will combine these methods with spectroscopic imaging readouts and develop multi-spectrum linear combination modeling approached for quantification. Edited CEST approaches, tuning acquisition parameters to improve specificity of detection, CEST fingerprinting, and single-shot z-spectrum approaches will be developed. Deep learning tools for the analysis of these data will also be developed, with a particular focus on the integration of methods, and spectral resolution enhancement and metabolic editing by MRSI-based training of CEST analyses. Acquisition and analysis tools developed under this TRD will be disseminated to collaborative projects and service projects, to establish reproducibility in a new setting, and to the broader community.