Treatment of baseline drifts in fMRI time series analysis

Purpose: Gradual drifting of baseline signal intensity is common in functional MRI (fMRI) time course data. Methods for dealing with this effect are studied. Method: Simulations and fMRI data are used to study three statistical models that account for baseline drift. A method is proposed in which the time course data are linear least-squares fit to a reference function that includes the slope of the baseline drift as a free parameter. Results: It is shown that the least-squares method is equivalent to cross-correlation with Gram-Schmidt orthogonalization. Additionally, it is shown that certain paradigm designs improve the sensitivity of statistical tests when using any of the drift correction methods commonly employed. The least-squares method results in a variety of useful parameters such as activation amplitude, with a well characterized error. Conclusion: Very simple techniques can effectively account for observed drifts. It is important to design paradigms that are symmetric about the midpoint of the time series. In calculating confidence levels, a proper statistical model that accounts for baseline drifts is necessary to ensure accurate confidence level assessment.