Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural mass model

S.D. Kulik*, L. Douw, E. Dellen, M. D. Steenwijk, J. J.G. Geurts, C. J. Stam, A. Hillebrand, M. M. Schoonheim, P. Tewarie

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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.

Original languageEnglish
Pages (from-to)950-965
Number of pages16
JournalNetwork Neuroscience
Issue number3
Early online date1 Oct 2023
Publication statusPublished - 1 Oct 2023
Externally publishedYes


  • Computational modeling
  • Functional connectivity
  • Individual prediction
  • Magnetoencephalography


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