General Linear Modeling For Magnetic Resonance Spectroscopy

项目摘要 先进的多谱磁共振波谱（MRS）方法允许非侵入性 神经化学物质浓度的测量，以及其他生物物理特性的测量。当前 可用的一维建模工具不能充分地对这样的数据建模， 将关于子光谱之间的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充分容纳在 定量模型包括代谢物弛豫时间，代谢物扩散张量，和生理 对外界刺激的代谢反应 本项目通过引入广义线性模型，解决了MRS当前可用建模工具中的差距。 这避免了由电流的串行应用引起的过拟合 一维模型，大大增加模型的简约性。开发的所有代码都将提供给 社区开源，建模框架将通过网络用户在云中提供 接口.

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.