NEURO IMAGING & STROKE REHABILITATION PROGNOSIS

将实验设计、分析和 性能将受益于各种衡量标准，包括 13C标记研究、松弛测量和成像方案。 为了优化实验，数学建模可以作为一种 科学程序的组成部分，包括以下步骤： (1)假设或问题的形成；(2)发展 实验计划，(3)数据的获取，(4) 数据的分析和解释。一项实验的效率 它成功的可能性取决于它的设计、质量 以及对数据的解释，这取决于仔细的规划 和数据分析。因此，实验方案应该是 设计时考虑了信噪比和对 假设。本项目描述了该系统的开发和应用 实验设计、分析和测试的定量方法 针对某些特定领域和一般情况的假设构建 在各种研究中的应用。尤其是这个项目， 在其4个具体目标中，寻求为以下方面开发建模分析 用于优化核磁共振的同位素标记实验(13C) 用改进的代谢物进行13C研究的数据采集 量化和松弛速率测量，并研究新的 设计和分析此类实验的方法。

Approaches that integrate experiment design, analysis, and performance will benefit a variety of measurements, including 13C-labeling studies, relaxation measurements and imaging protocols. To optimize experiments, mathematical modeling can be employed as an integral part of the scientific procedure, which contains these steps: (1) the formation of a hypothesis or question, (2) the development of an experimental plan, (3) the acquisition of data, and (4) the analysis and interpretation of data. The efficiency of an experiment and its likelihood of success are subject to its design, the quality of the data, and its interpretation, which depend on careful planning and data analysis. Therefore, experimental protocols should be designed with considerations of signal-to-noise and sensitivity to hypotheses. This project describes the development and application of quantitative approaches to experimental design, analysis, and hypothesis building for some specific areas and for general application in a wide variety of studies. In particular the project, in its 4 specific aims, seeks to develop modeling analyses for isotopic labeling experiments (13C) with the intent of optimizing NMR data acquisition for 13C studies with improved metabolite quantification and relaxation rate measurements and to investigate new approaches to design and analysis of such experiments.