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The Role of Experiment, Acquisition Method, Modeling Strategy, and Individual and Spatial Variability on Residual Autocorrelation in Task fMRI Analysis (306700)

Keywords: functional resonance imaging, blood oxygenation level dependent, hemodynamic response, linear model

In a functional resonance imaging (fMRI) experiment, participants’ brains are imaged while they are performing a series of tasks in order to infer brain regions activating in response to each task. To perform this inference, it is typical to fit a linear model at every location relating the observed fMRI data to the expected blood oxygenation level dependent (BOLD) response to each task. However, the assumption of independent residuals in the linear model is violated, resulting in underestimated standard errors. We investigate the factors driving residual autocorrelation, including various experimental factors, individual and spatial differences. We also consider the ability of different modeling strategies to reduce residual autocorrelation by more accurately capturing the shape and duration of the hemodynamic response measured by fMRI, which is known to differ across subjects, areas of the brain and tasks. We find that residual autocorrelation shows conspicuous differences due to experiments and acquisition methods and varies across individuals and areas of the brain. Further, residual autocorrelation is reduced through more flexible HRF modeling approaches.