An average sliding window correlation method for dynamic functional connectivity

Sliding window correlation (SWC) is utilized in many studies to analyze the temporal characteristics of brain connectivity. However, spurious artifacts have been reported in simulated data using this technique. Several suggestions have been made through the development of the SWC technique. Recently, it has been proposed to utilize a SWC window length of 100 s given that the lowest nominal fMRI frequency is 0.01 Hz. The main pitfall is the loss of temporal resolution due to a large window length. In this work, we propose an average sliding window correlation (ASWC) approach that presents several advantages over the SWC. One advantage is the requirement for a smaller window length. This is important because shorter lengths allow for a more accurate estimation of transient dynamicity of functional connectivity. Another advantage is the behavior of ASWC as a tunable high pass filter. We demonstrate the advantages of ASWC over SWC using simulated signals with configurable functional connectivity dynamics. We present analytical models explaining the behavior of ASWC and SWC for several dynamic connectivity cases. We also include a real data example to demonstrate the application of the new method. In summary, ASWC shows lower artifacts and resolves faster transient connectivity fluctuations, resulting in a lower mean square error than in SWC.