General Linear Modeling For Magnetic Resonance Spectroscopy

项目摘要 先进的多光谱磁共振波谱(MRS)方法允许无创 测量神经化学物质的浓度，但也测量其他生物物理性质。目前的 现有的一维建模工具无法对此类数据进行充分建模，因为它们无法 将有关子谱之间关系的先验知识合并到单个多维模型中。 可以在采集中编码，但不能在 量化模型包括代谢物松弛时间、代谢物扩散张量和生理张量。 对外界刺激的代谢反应。 该项目通过引入广义线性模型来解决目前可用于MRS的建模工具的不足 用于MRS的组合建模框架这避免了由于串联应用当前的 一维模型，大大增加了模型的简约性。所有开发的代码都将可用 向社区开放源代码，建模框架将通过Web用户在云中可用 界面。

Project Summary Advanced multi-spectrum magnetic resonance spectroscopy (MRS) methods allow the non-invasive measurement of the concentration of neurochemicals, but also of other biophysical properties. The currently available one-dimensional modeling tools cannot adequately model such data because they are incapable of incorporating prior knowledge about the relationships between sub-spectra into a single multi-dimensional model. Additional parameters that can be encoded in the acquisition, but not adequately accommodated within the quantification model include metabolite relaxation times, metabolite diffusion tensors, and physiological metabolic response to external stimulation. This project addresses the gap in currently available modeling tools for MRS by introducing a generalized linear combination modeling framework for MRS. This avoids the overfitting that arises from serial application of current one-dimensional models, dramatically increasing model parsimony. All code developed will be made available to the community open-source, and the modeling framework will be made available in the cloud via a web user interface.