Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study

The human brain is a highly dynamic system, and the methods we use to analyze the data gathered from this organ should account for this dynamism. One such family of methods that has attracted a lot of attention in the past decades is based on networks. The most well-known method for estimating the connection among these networks uses the sliding window Pearson correlation (SWPC) estimator. Although quite a useful tool, there are some important limitations. One such limitation is that SWPC applies a high pass filter to the activity time series. If we select a small window size (which is desirable to estimate rapid changes in functional connectivity), we will filter out important low-frequency activity information. In this work, we propose an approach based on single sideband modulation (SSB) in communication theory, which aims to solve this issue, allowing us to select smaller window sizes and capture rapid changes in the time-resolved functional connectivity. We use both simulation and real data to demonstrate the superior performance of the proposed method, SSB+SWPC, compared to classical SWPC. In addition, we compare the temporal recurring functional connectivity patterns between individuals with the first episode of psychosis (FEP) and typical controls (TC) and show that FEP stays more in FNC states that show weaker connectivity across the whole brain. A result exclusive to SSB+SWPC is that TC stays more in a state with negative connectivity between sub-cortical and cortical regions. All in all, based on both simulated data and real data, we argue that the proposed method, SSB+SWPC, is more sensitive for capturing temporal variation in functional connectivity.