The non-separability of physiologic noise in functional connectivity MRI with spatial ICA at 3T

Abstract

The impact of physiologic noise on spatial ICA analyses of resting state BOLD-weighted MRI data is investigated. Using FastICA and Infomax ICA, two common ICA algorithms, we apply a group spatial ICA method across multiple subjects. We compare the spatial maps from five commonly identified functional networks and show that physiologic noise correction techniques introduce significant changes in the spatial ICA decomposition of all five networks, greater than the changes introduced by either algorithmic indeterminacy (re-running ICA) or the changes introduced by decreasing the decomposition dimensionality due to physiologic noise removal. In addition, we demonstrate that the sources associated with these components have significant temporal correlation to parallel measures of cardiac and respiratory rates, and these are reduced after correction. We conclude that ICA decomposition is significantly affected by physiologic noise and the ICA process alone is not sufficient to separate physiologic noise effects in the brain. It is recommended that physiologic noise correction be applied to timeseries data prior to ICA decomposition.