Figure 1. .

A schematic of analysis steps. (A) BOLD fMRI data was preprocessed, parcellated, and individual parcel time series were extracted. (B) Functional connectivity (FC) was estimated with five methods that differed along two dimensions: static versus dynamic and bivariate versus multivariate. Static FC refers to measures that are insensitive to temporal order and can be estimated using full/Pearson’s correlation or partial correlation, whereas measures of dynamic FC are sensitive to temporal order of time points. Dynamic FC can be estimated using measures of lag-based connectivity, such as lagged correlation, or using the linear multivariate autoregressive (AR) model. The lagged correlation between two time series is calculated by shifting one time series by p time points. Similarly, a p-th order multivariate (or vector) autoregressive model predicts the activity of a particular brain region at time point t based on the activity of all regions at time point(s) from t − p to t − 1. Bivariate and multivariate FC methods differ in terms of number of variables (regions) taken into account when estimating connectivity at a single edge: bivariate connectivity between two regions depends only on the two regions, whereas multivariate connectivity between two regions includes all other regions as covariates. (C) FC matrices were vectorized. (D) FC estimates were compared (i) by calculating correlations between FC estimates, (ii) by calculating correlations between node centrality measures, and (iii) by comparing estimates of brain-behavior associations across FC methods. (E) Additionally, we performed simulation to assess the influence of random noise and signal length on the similarity between FC estimates obtained using different methods.