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000894888 1001_ $$0P:(DE-Juel1)179582$$aDomhof, Justin$$b0$$ufzj
000894888 245__ $$aParcellation-induced variation of empirical and simulated brain connectomes at group and subject levels
000894888 260__ $$aCambridge, MA$$bThe MIT Press$$c2021
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000894888 520__ $$aRecent developments of whole-brain models have demonstrated their potential when investigating resting-state brain activity. However, it has not been systematically investigated how alternating derivations of the empirical structural and functional connectivity, serving as the model input, from MRI data influence modeling results. Here, we study the influence from one major element: the brain parcellation scheme that reduces the dimensionality of brain networks by grouping thousands of voxels into a few hundred brain regions. We show graph-theoretical statistics derived from the empirical data and modeling results exhibiting a high heterogeneity across parcellations. Furthermore, the network properties of empirical brain connectomes explain the lion’s share of the variance in the modeling results with respect to the parcellation variation. Such a clear-cut relationship is not observed at the subject-resolved level per parcellation. Finally, the graph-theoretical statistics of the simulated connectome correlate with those of the empirical functional connectivity across parcellations. However, this relation is not one-to-one, and its precision can vary between models. Our results imply that network properties of both empirical connectomes can explain the goodness-of-fit of whole-brain models to empirical data at a global group level but not at a single-subject level, which provides further insights into the personalization of whole-brain models.
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000894888 7001_ $$0P:(DE-Juel1)178611$$aJung, Kyesam$$b1$$ufzj
000894888 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b2$$ufzj
000894888 7001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr$$b3$$eCorresponding author$$ufzj
000894888 773__ $$0PERI:(DE-600)2900481-0$$a10.1162/netn_a_00202$$gVol. 5, no. 3, p. 798 - 830$$n3$$p798 - 830$$tNetwork neuroscience$$v5$$x2472-1751$$y2021
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