Sources of residual autocorrelation in multiband task fMRI and strategies for effective mitigation - Parlak, Fatma; Pham, Damon D.; Spencer, Daniel A.; Welsh, Robert C.; Mejia, Amanda F. (Frontiers in Neuroscience, 2023)
In task fMRI analysis, ordinary least squares (OLS) is typically used in a linear regression model 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. 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 it more challenging to estimate spatially-varying prewhitening parameters with high accuracy. These issues are becoming more critical now because of a trend towards subject-level analysis and inference. In group-average analyses, the within-subject correlation structure is accounted for implicitly, so inadequate prewhitening is of little real consequence. For individual subject inference, however, accurate prewhitening is crucial to avoid inflated or spatially variable false positive rates. In this article, we first thoroughly examine the sources and patterns 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 autoregressive (AR) model-based prewhitening strategies to effectively mitigate autocorrelation and control false positives. We consider two factors: the choice of AR model order and the level of spatial regularization of AR model coefficients, ranging from local smoothing to global averaging. We find that local regularization is much more effective than global averaging at mitigating autocorrelation. Increasing the AR model order is also helpful but to a lesser degree. We find that prewhitening with an AR(6) model with local regularization is effective at reducing or even eliminating autocorrelation and controlling false positives. 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, which is included in the open source R package BayesfMRI.