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首页> 外文会议>Computational Imaging V; Proceedings of SPIE-The International Society for Optical Engineering; vol.6498; Electronic Imaging Science and Technology >Regional Approach to Fmri Data Analysis Using Hemodynamic Response Modeling
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Regional Approach to Fmri Data Analysis Using Hemodynamic Response Modeling

机译：使用血流动力学响应模型的Fmri数据分析的区域方法

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摘要

Analysis of functional magnetic resonance imaging (fMRI) data has been performed using both model-driven (parametric) methods and data-driven methods. An advantage of model-driven methods is incorporation of prior knowledge of spatial and temporal properties of the hemodynamic response (HDR). A novel analytical framework for fMRI data has been developed that identifies multi-voxel regions of activation through iterative segmentation-based optimization over HDR estimates for both individual voxels and regional groupings. Simulations using synthetic activation embedded in autoregressive integrated moving average (ARIMA) noise reveal the proposed procedure to be more sensitive and selective than conventional fMRI analysis methods (reference set: principle component analysis, PCA; independent component analysis, ICA; k-means clustering, k=100; univariate t-est) in identification of active regions over the range of average contrast-to-noise ratios of 0.5 to 4.0. Results of analysis of extant human data (for which the average contrast-to-noise ratio is unknown) are further suggestive of greater statistical detection power. Refinement of this new procedure is expected to reduce both false positive and negative rates, without resorting to filtering that can reduce the effective spatial resolution.
机译：功能磁共振成像（fMRI）数据的分析已使用模型驱动（参数）方法和数据驱动方法进行。模型驱动方法的优点是结合了血液动力学反应（HDR）的时空特性的先验知识。已经开发了一种用于fMRI数据的新颖分析框架，该框架通过对单个体素和区域分组的HDR估计值进行基于迭代分段的优化来识别激活的多个体素区域。使用嵌入到自回归积分移动平均（ARIMA）噪声中的合成激活进行的仿真显示，与常规功能磁共振成像分析方法（参考集：主成分分析，PCA；独立成分分析，ICA； k均值聚类， （k = 100；单变量t-est）在平均反差比范围为0.5至4.0的有效区域识别中。现有人类数据的分析结果（其平均对比度与噪声比未知）进一步暗示了更高的统计检测能力。改进此新程序有望减少误报率和误报率，而无需借助可降低有效空间分辨率的过滤。

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