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A robust signal detection method for fMRI data under correct Rice conditions

机译:正确的Rice条件下用于fMRI数据的可靠信号检测方法

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In this paper, we tackle the problem of signal detection in functional Magnetic Resonance Imaging (fMRI) data by means of a statistical analysis. The main problem of the commonly used statistical tests is that they are based on the assumption that the data are Gaussian distributed, which is only valid for high signal-to-noise ratios (SNRs). Hence, for low SNRs the classical statistical tests are inadequate due to the wrong normality assumption, since it is known from literature that fMRI data follow a Rice distribution. In order to handle both high and low SNRs, we present in this paper a correction for the simplest and most widely used t-test by incorporating the correct Rice conditions. The performance of the Rice-corrected statistical test is shown through simulations and compared with its uncorrected counterpart.
机译:在本文中,我们通过统计分析解决了功能性磁共振成像(fMRI)数据中的信号检测问题。常用统计测试的主要问题是,它们基于数据是高斯分布的假设,这仅对高信噪比(SNR)有效。因此,由于错误的正态性假设,对于低SNR而言,经典的统计检验是不够的,因为从文献中得知fMRI数据遵循莱斯分布。为了处理高信噪比和低信噪比,我们在本文中介绍了一种通过结合正确的莱斯条件对最简单和使用最广泛的t检验的校正方法。通过模拟显示了莱斯校正的统计检验的性能,并将其与未校正的统计检验进行了比较。

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