000905261 001__ 905261
000905261 005__ 20220131120324.0
000905261 037__ $$aFZJ-2022-00544
000905261 041__ $$aEnglish
000905261 1001_ $$0P:(DE-Juel1)179582$$aDomhof, Justin$$b0$$eCorresponding author$$ufzj
000905261 1112_ $$aINM & IBI Retreat 2021, Forschungszentrum Jülich$$cVirtual Conference$$d2021-10-05 - 2021-10-06$$wGermany
000905261 245__ $$aParcellation-induced variation of empirical and simulated functional brain connectivity
000905261 260__ $$c2021
000905261 3367_ $$033$$2EndNote$$aConference Paper
000905261 3367_ $$2BibTeX$$aINPROCEEDINGS
000905261 3367_ $$2DRIVER$$aconferenceObject
000905261 3367_ $$2ORCID$$aCONFERENCE_POSTER
000905261 3367_ $$2DataCite$$aOutput Types/Conference Poster
000905261 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1642166571_5793$$xAfter Call
000905261 520__ $$aRecent 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.
000905261 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000905261 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x1
000905261 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x2
000905261 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x3
000905261 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x4
000905261 536__ $$0G:(EU-Grant)826421$$aVirtualBrainCloud - Personalized Recommendations for Neurodegenerative Disease (826421)$$c826421$$fH2020-SC1-DTH-2018-1$$x5
000905261 65017 $$0V:(DE-MLZ)GC-130-2016$$2V:(DE-HGF)$$aHealth and Life$$x0
000905261 7001_ $$0P:(DE-Juel1)178611$$aJung, Kyesam$$b1$$ufzj
000905261 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b2$$ufzj
000905261 7001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr$$b3$$ufzj
000905261 8564_ $$uhttps://events.hifis.net/event/161/
000905261 909CO $$ooai:juser.fz-juelich.de:905261$$pec_fundedresources$$pVDB$$popenaire
000905261 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)179582$$aForschungszentrum Jülich$$b0$$kFZJ
000905261 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178611$$aForschungszentrum Jülich$$b1$$kFZJ
000905261 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b2$$kFZJ
000905261 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131880$$aForschungszentrum Jülich$$b3$$kFZJ
000905261 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5232$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
000905261 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5231$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x1
000905261 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5254$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x2
000905261 9141_ $$y2021
000905261 920__ $$lyes
000905261 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
000905261 980__ $$aposter
000905261 980__ $$aVDB
000905261 980__ $$aI:(DE-Juel1)INM-7-20090406
000905261 980__ $$aUNRESTRICTED