Figure 2.

Seven dFC methods under evaluation. Final dFC output is shown with grey background. Computing bivariate connectivity given two timeseries x and y. Sliding window correlation (SWC) characterizes dFC as correlation between two windowed/segmented time courses (x_swc, y_swc). Sliding window correlation with L1 regularization (SWC_L1) also penalizes the inverse covariance matrix. Delete-d jackknife correlation (dJC) excludes d consecutive observations and computes correlation using the remaining time points (x_dJC, y_dJc) and multiplies by negative one. Jackknife correlation (JC) is a special case of dJC, while only one sample is excluded each time. Multiplication of temporal derivates (MTD) first computes the first-order temporal derivatives (x_MTD, y_MTD) and then computes frame-wise MTD estimation (MTD_f), which is later smoothed using simple moving average (MA). Dynamic conditional correlation (DCC) first fits the timeseries using a GARCH (generalized autoregressive conditional heteroskedasticity) model to compute standardized residuals (x_DCC, y_DCC). Exponentially weighted moving average (EMA) is applied on element-wise product of two standardized residual timeseries resulting framewise DCC estimation. Moving average DCC (DCC_MA) is computed by applying simple moving average (MA) on DCC estimations, with the window length matching other window-based dFC methods.