Independent Component Analysis Optimized for fMRI

针对 fMRI 优化的独立成分分析

• 批准号: 6719656
• 负责人: Mary E. Meyerand
• 金额: $ 25.46万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-04-15 至 2007-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6719656
• 关键词: bioimaging /biomedical imaging; brain disorder diagnosis; brain electrical activity; brain imaging /visualization /scanning; brain mapping; clinical research; functional magnetic resonance imaging; human subject; statistics /biometry

This application requests support to continue our work in developing robust, independent component analysis (ICA) methods for measuring brain activity using functional magnetic Resonace Imaging (fMRI), and applying these methods to a population of brain tumor patients. The development of an accurate, noninvasive method to locate eloquent cortex in relation to brain pathologies is a goal of critical importance to the neurosurgical community. Conventional linear regression analysis methods, while robust in healthy, motivated volunteers, often fail in clinical situations. Clinical cases are frequently marred by patient movement, and failure of the patient to correctly perform the task. Our goal behind this project is to use the knowledge and experience we have gained thus far to develop a more robust ICA-based method that takes into account all of the relevant physiological parameters that describe the fMRI signal and allows a statistical significance level to to be attached to the ICA results. The primary aims of the project are: 1. Optimize the ICA algorithm and paradigms for fMRI. 2. Compare the results of an ICA and a standard regression analysis in healthy volunteers. 3. Develop a frequency representation of the fMRI time course as a means to facilitate identification of task related components in an ICA. 4. Develop a hybrid ICA method whereby time courses are computed with an ICA of a fMRI data set to build a linear model of the fMRI data set in the frequency domain.

该应用程序要求支持继续我们的工作，开发强大的，独立成分分析（伊卡）的方法来测量大脑活动，使用功能磁共振成像（fMRI），并将这些方法应用于脑肿瘤患者的人口。 发展一种准确的，非侵入性的方法来定位功能皮层与脑病理的关系是神经外科界的一个至关重要的目标。 传统的线性回归分析方法，而强大的健康，积极的志愿者，往往在临床情况下失败。 临床病例经常被患者移动和患者未能正确执行任务所破坏。 我们在这个项目背后的目标是利用我们迄今为止所获得的知识和经验来开发一种更强大的基于ICA的方法，该方法考虑了描述fMRI信号的所有相关生理参数，并允许将统计显著性水平附加到伊卡结果。该项目的主要目标是：1。优化伊卡算法和fMRI范例。2.在健康志愿者中比较伊卡和标准回归分析的结果。3.开发一个频率表示的功能磁共振成像的时间过程作为一种手段，以促进识别任务相关的组件在伊卡。4.开发一种混合伊卡方法，利用fMRI数据集的伊卡计算时间过程，以在频域中建立fMRI数据集的线性模型。

项目成果

Physiologic characterisation of glioblastoma multiforme using MRI-based hypoxia mapping, chemical shift imaging, perfusion and diffusion maps.

使用基于 MRI 的缺氧图、化学位移成像、灌注和扩散图对多形性胶质母细胞瘤进行生理表征。

• DOI: 10.1016/j.jocn.2005.12.025
• 发表时间: 2006
• 期刊: Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
• 作者: McMillan,KathrynM;Rogers,BaxterP;Field,AaronS;Laird,AngelaR;Fine,JasonP;Meyerand,MElizabeth
• 通讯作者: Meyerand,MElizabeth