Identifying Brain Resting-state Networks from Arterial Spin Labeling with Spectral Clustering

We propose a spectral clustering algorithm (SCA) based on the Pearson correlation metric (SCA-PC) to identify large-scale brain networks in arterial spin labeling (ASL) images. It was shown to be more robust to Gaussian distributed noise sources based on simulations. We studied the robustness of SCA-PC vs. the traditional SCA method based on a Euclidean distance metric (SCA-ED) for deriving resting-state networks from real human fMRI data. Our results indicate that SCA-PC can derive better brain networks from ASL data than traditional SCA-ED.