Independent Component Analysis Optimized for fMRI

该应用程序要求支持我们继续开发健壮的独立分量分析(ICA)方法，以使用功能磁共振成像(FMRI)测量大脑活动，并将这些方法应用于脑瘤患者群体。开发一种准确的、非侵入性的方法来定位与脑部病理有关的口才皮质是神经外科界至关重要的目标。传统的线性回归分析方法，虽然在健康、积极的志愿者中是可靠的，但在临床情况下往往失败。临床病例经常受到病人移动和病人未能正确执行任务的影响。我们这个项目背后的目标是利用我们到目前为止获得的知识和经验来开发一种更健壮的基于ICA的方法，该方法考虑到描述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ICA结果上。本课题的主要目标是：1.优化用于功能磁共振成像的ICA算法和范型。2.比较健康志愿者ICA和标准回归分析的结果。3.开发fMRI时间进程的频率表示，作为促进识别ICA中与任务相关的组成部分的一种手段。4.开发了一种混合ICA方法，其中利用fMRI数据集的ICA来计算时间进程，以在频域中建立fMRI数据集的线性模型。

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.