PCA Based Noise Reduction of Delay Maps Obtained from Human Connectome Project Resting State fMRI Data

Previous work from our group has presented compelling evidence that systemic low frequency oscillations (sLFOs), the major constituent of low frequency global systemic noise overlying resting state functional networks, propagate dynamically throughout the brain with cerebral blood circulation. More specifically, it has been demonstrated that sLFOs travel with the bulk cerebral blood flow with voxel-specific arrival time delays, and their spatiotemporal pattern changes in a way that tracks cerebral blood flow dynamics. We are interested in using the Human Connectome Project (HCP) dataset to determine normative blood flow delays throughout the brain. Time delay maps were obtained by the Regressor Interpolation at Progressive Time Delays (RIPTiDe) method , which was applied to 487 subjects of the HCP 500 subjects release data. While the procedure generates extremely clean mean delay maps, the individual delay maps are quite noisy. Because the circulation delays should in general be slowly varying in space, PCA noise reduction is a natural choice to preserve the spatial structure of the delay maps while removing random noise points.