Before revision: Objective: Sliding and tapered sliding window methods are the most often used approaches in computing dynamic correlations in biomedical signals such as the brain resting-state fMRI  . However,  due to the discrete nature of windows, the window methods suffer spurious high-frequency fluctuations and the zig-zag pattern in dynamic correlations. 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.
After revision: Objective: Sliding and tapered sliding window methods are the most often used approaches in computing dynamic correlations in biomedical signals such as the brain resting-state fMRI . However, due to the discrete nature of windows, the window methods suffer spurious high-frequency fluctuations and the zig-zag pattern in the estimated time-varying correlation measures. To address the problem and obtain more stable correlation estimates, we propose a novel windowless approach for computing dynamic correlations via heat kernel smoothing.