Computational models are often used to assess how functional connectivity (FC) patterns emerge from neuronal population dynamics and anatomical brain connections. It remains unclear whether the commonly used group-averaged data can predict individual FC patterns. The Jansen and Rit neural mass model was employed, where masses were coupled using individual structural connectivity (SC). Simulated FC was correlated to individual magnetoencephalography-derived empirical FC. FC was estimated using phase-based (phase lag index (PLI), phase locking value (PLV)), and amplitude-based (amplitude envelope correlation (AEC)) metrics to analyze their goodness of fit for individual predictions. Individual FC predictions were compared against group-averaged FC predictions, and we tested whether SC of a different participant could equally well predict participants’ FC patterns. The AEC provided a better match between individually simulated and empirical FC than phase-based metrics. Correlations between simulated and empirical FC were higher using individual SC compared to group-averaged SC. Using SC from other participants resulted in similar correlations between simulated and empirical FC compared to using participants’ own SC. This work underlines the added value of FC simulations using individual instead of group-averaged SC for this particular computational model and could aid in a better understanding of mechanisms underlying individual functional network trajectories. Author Summary Employing the Jansen and Rit neural mass model, we simulated individual FC by using individual SC. The AEC, one of the applied FC metrics, proved to give the highest correlations between simulated and empirical FC. Also, individual SC as compared to group-averaged SC provided higher correlations between simulated and empirical FC. However, using SC from other participants resulted in similar correlations between simulated and empirical FC compared to using participants’ own SC. Importantly, this work underlines the value of individual SC as compared to group-averaged SC to simulate FC. The insights obtained from this work might lead to a better understanding of mechanisms underlying individual functional network trajectories.