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首页> 外文期刊>Magnetic resonance imaging: An International journal of basic research and clinical applications >False positive control of activated voxels in single fMRI analysis using bootstrap resampling in comparison to spatial smoothing
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False positive control of activated voxels in single fMRI analysis using bootstrap resampling in comparison to spatial smoothing

机译：与空间平滑相比，使用引导重采样的单功能磁共振成像分析中的激活体素的假阳性控制

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摘要

Functional magnetic resonance imaging (fMRI) is an effective tool for the measurement of brain neuronal activities. To date, several statistical methods have been proposed for analyzing fMRI datasets to select true active voxels among all the voxels appear to be positively activated. Finding a reliable and valid activation map is very important and becomes more crucial in clinical and neurosurgical investigations of single fMRI data, especially when pre-surgical planning requires accurate lateralization index as well as a precise localization of activation map. Defining a proper threshold to determine true activated regions, using common statistical processes, is a challenging task. This is due to a number of variation sources such as noise, artifacts, and physiological fluctuations in time series of fMRI data which affect spatial distribution of noise in an expected uniform activated region. Spatial smoothing methods are frequently used as a preprocessing step to reduce the effect of noise and artifacts. The smoothing may lead to a shift and enlargement of activation regions, and in some extend, unification of distinct regions. In this article, we propose a bootstrap resampling technique for analyzing single fMRI dataset with the aim of finding more accurate and reliable activated regions. This method can remove false positive voxels and present high localization accuracy in activation map without any spatial smoothing and statistical threshold setting.
机译：功能磁共振成像（fMRI）是测量大脑神经元活动的有效工具。迄今为止，已经提出了几种统计方法来分析fMRI数据集以在所有似乎被正激活的体素中选择真正的活动体素。找到可靠且有效的激活图非常重要，并且在单个fMRI数据的临床和神经外科研究中变得尤为重要，尤其是在术前计划需要准确的侧化指数以及激活图的精确定位时。使用常见的统计过程来定义适当的阈值以确定真正的激活区域是一项艰巨的任务。这是由于fMRI数据的时间序列中的许多变化源（例如噪声，伪影和生理波动）影响了预期的均匀激活区域中噪声的空间分布。空间平滑方法经常用作减少噪声和伪影影响的预处理步骤。平滑可能导致激活区域的移动和扩大，并在某种程度上导致不同区域的统一。在本文中，我们提出了一种用于分析单个fMRI数据集的引导重采样技术，目的是寻找更准确和可靠的激活区域。该方法可以消除假阳性体素，并在激活图中显示高定位精度，而无需任何空间平滑和统计阈值设置。

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