In task fMRI analysis, OLS is typically used to estimate task-induced activation in the brain. Since task fMRI residuals often exhibit temporal autocorrelation, it is common practice to perform prewhitening prior to OLS to satisfy the assumption of residual independence, equivalent to GLS. While theoretically straightforward, a major challenge in prewhitening in fMRI is accurately estimating the residual autocorrelation at each location of the brain. Assuming a global autocorrelation model, as in several fMRI software programs, may under- or over-whiten particular regions and fail to achieve nominal false positive control across the brain. Faster multiband acquisitions require more sophisticated models to capture autocorrelation, making prewhitening more difficult. These issues are becoming more critical now because of a trend towards subject-level analysis, where prewhitening has a greater impact than in group-average analyses. In this article, we first thoroughly examine the sources of residual autocorrelation in multiband task fMRI. We find that residual autocorrelation varies spatially throughout the cortex and is affected by the task, the acquisition method, modeling choices, and individual differences. Second, we evaluate the ability of different AR-based prewhitening strategies to effectively mitigate autocorrelation and control false positives. We find that allowing the prewhitening filter to vary spatially is the most important factor for successful prewhitening, even more so than increasing AR model order. To overcome the computational challenge associated with spatially variable prewhitening, we developed a computationally efficient R implementation based on parallelization and fast C++ backend code. This implementation is included in the open source R package BayesfMRI.
翻译:在任务 fMRI 分析中, OLS 通常用于估算大脑中任务引发的激活。 由于任务 FMRI 残留通常会显示时间上的自动反向关系,因此通常的做法是在 OLS 之前进行预白,以满足剩余独立性的假设, 相当于 GLS 。 虽然理论上直截了当, 但是在 FMRI 中,预白的主要挑战是准确估计大脑每个位置的剩余自动反向关系。 假设全球自动反向关系模式, 如在多个 FMRI 过滤软件程序中一样, 任务在特定区域下或超白, 无法在大脑中实现名义上虚假的正控。 加速多波段获取的多波段空端空间偏移需要更先进的模型, 以捕捉自动反向关系, 使预变速更加困难。 这些问题现在变得更加关键, 是因为主题分析的趋势, 即预白前影响大于集体平均分析。 在本篇文章中, 我们首先彻底地检查了多波层平流任务前的自动反向关系源。 我们发现, 甚至在多波段平流内部任务组合中, 的剩余自动变异变异, 并受了内部变异变变 。