Qin Jiyue, Davenport Samuel, Schwartzman Armin
Division of Biostatistics and Bioinformatics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego.
Halıcıoğlu Data Science Institute, University of California, San Diego.
bioRxiv. 2025 Jan 25:2025.01.24.634784. doi: 10.1101/2025.01.24.634784.
Functional Magnetic Resonance Imaging (fMRI) is commonly used to localize brain regions activated during a task. Methods have been developed for constructing confidence regions of image excursion sets, allowing inference on brain regions exceeding non-zero activation thresholds. However, these methods have been limited to a single predefined threshold and brain volume data, overlooking more sensitive cortical surface analyses. We present an approach that constructs simultaneous confidence regions (SCRs) which are valid for all possible activation thresholds and are applicable to both volume and surface data. This approach is based on a recent method that constructs SCRs from simultaneous confidence bands (SCBs), obtained by using the bootstrap on 1D and 2D images. To extend this method to fMRI studies, we evaluate the validity of the bootstrap with fMRI data through extensive 2D simulations. Six bootstrap variants, including the nonparametric bootstrap and multiplier bootstrap are compared. The Rademacher multiplier bootstrap-t performs the best, achieving a coverage rate close to the nominal level with sample sizes as low as 20. We further validate our approach using realistic noise simulations obtained by resampling resting-state 3D fMRI data, a technique that has become the gold standard in the field. Moreover, our implementation handles data of any dimension and is equipped with interactive visualization tools designed for fMRI analysis. We apply our approach to task fMRI volume data and surface data from the Human Connectome Project, showcasing the method's utility.
功能磁共振成像（fMRI）通常用于定位任务期间激活的脑区。已经开发出了构建图像偏移集置信区域的方法，从而能够推断出超过非零激活阈值的脑区。然而，这些方法仅限于单个预定义阈值和脑容量数据，忽略了更敏感的皮质表面分析。我们提出了一种构建同时置信区域（SCR）的方法，该方法对所有可能的激活阈值均有效，并且适用于体积数据和表面数据。此方法基于一种最近开发的方法，该方法通过在一维和二维图像上使用自助法获得同时置信带（SCB）来构建SCR。为了将此方法扩展到fMRI研究中，我们通过广泛的二维模拟评估了自助法在fMRI数据中的有效性。比较了六种自助法变体，包括非参数自助法和乘数自助法。拉德马赫乘数自助法 - t表现最佳，在样本量低至20时，覆盖率接近标称水平。我们使用通过对静息态3D fMRI数据进行重采样获得的逼真噪声模拟进一步验证了我们的方法，该技术已成为该领域的金标准。此外，我们的实现可处理任何维度的数据，并配备了专为fMRI分析设计的交互式可视化工具。我们将我们的方法应用于来自人类连接组计划的任务fMRI体积数据和表面数据,展示了该方法的实用性。
