NEURO IMAGING & STROKE REHABILITATION PROGNOSIS

整合实验设计、分析和 性能将有利于各种测量，包括 13C标记研究、弛豫测量和成像方案。 为了优化实验，可以采用数学建模作为优化实验的方法。 科学程序的组成部分，其中包括这些步骤： (1)一个假设或问题的形成，（2） 实验计划，（3）数据采集，以及（4） 分析和解释数据。 实验的效率 其成功的可能性取决于其设计、质量 数据及其解释，这取决于仔细的规划 和数据分析。 因此，实验方案应 设计时考虑了信噪比和对 假设 本项目介绍了 实验设计、分析和 为某些特定领域和一般领域建立假设 应用于各种各样的研究。 尤其是该项目， 在其4个具体目标中，旨在为以下方面开发建模分析： 同位素标记实验（13C），旨在优化NMR 使用改进的代谢物进行13C研究的数据采集 量化和弛豫率测量，并研究新的 设计和分析这些实验的方法。

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.