Adaptive cyclic physiologic noise modeling and correction in functional MRI

Abstract

Physiologic noise in BOLD-weighted MRI data is known to be a significant source of the variance, reducing the statistical power and specificity in fMRI and functional connectivity analyses. We show a dramatic improvement on current noise correction methods in both fMRI and fcMRI data that avoids overfitting. The traditional noise model is a Fourier series expansion superimposed on the periodicity of parallel measured breathing and cardiac cycles. Correction using this model results in removal of variance matching the periodicity of the physiologic cycles. Using this framework allows easy modeling of noise. However, using a large number of regressors comes at the cost of removing variance unrelated to physiologic noise, such as variance due to the signal of functional interest (overfitting the data). It is our hypothesis that there are a small variety of fits that describe all of the significantly coupled physiologic noise. If this is true, we can replace a large number of regressors used in the model with a smaller number of the fitted regressors and thereby account for the noise sources with a smaller reduction in variance of interest. We describe these extensions and demonstrate that we can preserve variance in the data unrelated to physiologic noise while removing physiologic noise equivalently, resulting in data with a higher effective SNR than with current corrections techniques. Our results demonstrate a significant improvement in the sensitivity of fMRI (up to a 17% increase in activation volume for fMRI compared with higher order traditional noise correction) and functional connectivity analyses.