Before revision:  Methods: To address the problem and obtain more stable correlation estimates, we propose a novel windowless approach for computing dynamic correlations via heat kernel smoothing. The heat kernel, the natural generalization of the Gaussian kernel to manifolds, is used to defined a smoothing kernel without boundary or end points. Results: We show that the proposed windowless approach smooths out the unwanted high-frequency noise in correlation estimations and is more stable in identifying and discriminating state spaces in resting-state fMRI.
After revision:  Methods: To address the problem and obtain more stable correlation estimates, we propose the heat kernel based dynamic correlations. We demonstrate that the proposed windowless approach smooths out the unwanted high-frequency noise in correlation estimations and is more stable in identifying and discriminating state spaces in resting-state fMRI.