General Linear Modeling For Magnetic Resonance Spectroscopy

磁共振波谱学的一般线性建模

• 批准号: 10659037
• 负责人: Georg Oeltzschner
• 金额: $ 20.44万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-05 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659037
• 关键词: Address; Adoption; Algorithms; Behavior; Biochemical; Brain; Cloud Computing; Code; Communities; Computer Models; Concentration measurement; Coupled; Data; Diffusion; Dimensions; Elements; Equipment; Event; Fingerprint; Freedom; Grant; High Performance Computing; Human; Individual; Internet; Knowledge; Linear Models; Link; Magnetic Resonance Spectroscopy; Measures; Metabolic; Methods; Modeling; Modernization; Participant; Pathology; Phase; Physiologic pulse; Physiological; Property; Recovery; Relaxation; Resolution; Running; Series; Signal Transduction; Spectrum Analysis; Stimulus; Techniques; Testing; Time; Tissues; Translating; biophysical properties; candidate marker; cell type; chemical property; cloud based; density; design; diffusion weighted; experimental study; flexibility; improved; in vivo; in vivo magnetic resonance spectroscopy; innovation; model design; neurochemistry; novel; open source; pharmacologic; physical property; potential biomarker; response; tool; user-friendly

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.

项目摘要 晚期多光谱磁共振光谱（MRS）方法允许非侵入性 测量神经化学物质的浓度，也是其他生物物理特性的测量。当前 可用的一维建模工具无法充分建模此类数据，因为它们无能为力 将有关地下光谱之间关系的先验知识纳入单个多维模型中。 可以在收购中编码的其他参数，但在 定量模型包括代谢物松弛时间，代谢物扩散张量和生理 对外部刺激的代谢反应。 该项目通过引入广义线性来解决MRS当前可用的建模工具的差距 MRS的组合建模框架。这避免了由于当前的序列应用而产生的过度拟合 一维模型，大幅增加模型的模型。所有开发的代码都将提供 向社区开源，并通过网络用户在云中提供建模框架 界面。