Parcellation-induced variation of empirical and simulated functional brain connectivity

Recent developments of whole-brain models have demonstrated their potential when investigatingresting-state brain activity. However, it has not been systematically investigated how alternatingderivations of the empirical structural and functional connectivity, serving as the model input, fromMRI data influence modelling results. Here, we study the influence from one major element: thebrain parcellation scheme that reduces the dimensionality of brain networks by grouping thousandsof voxels into a few hundred brain regions. We show graph-theoretical statistics derived from theempirical data and modelling results exhibiting a high heterogeneity across parcellations. Furthermore, the network properties of empirical brain connectomes explain the lion’s share of the variancein the modelling results with respect to the parcellation variation. Such a clear-cut relationship isnot observed at the subject-resolved level per parcellation. Finally, the graph-theoretical statisticsof the simulated connectome correlate with those of the empirical functional connectivity acrossparcellations. However, this relation is not one-to-one, and its precision can vary between models.Our results imply that network properties of both empirical connectomes can explain the goodness-of-fit of whole-brain models to empirical data at a global group but not a single-subject level, whichprovides further insights into the personalisation of whole-brain models