We investigated the influence of physiological noise on statistical inference in fMRI at the single-subject level. By comparing two SMS sequences with a short and a long TR, we explored the interaction between repetition time, physiological noise modelling and the autoregressive model used to characterize serial correlations in fMRI data. Using variational Bayesian inference, we found that fMRI acquisitions with a short TR require accurate modelling of cardiac and respiratory processes to successfully remove serial correlations from the fMRI time series. For the SMS sequence with a longer TR, the standard AR model of order 1 proved sufficient.
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