000905236 001__ 905236
000905236 005__ 20220131120322.0
000905236 037__ $$aFZJ-2022-00519
000905236 041__ $$aEnglish
000905236 1001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr$$b0$$eCorresponding author$$ufzj
000905236 1112_ $$aSIAM Conference on Applications of Dynamical Systems (DS21)$$cVirtual Conference$$d2021-05-23 - 2021-05-27$$wUSA
000905236 245__ $$aImpact of Brain Parcellation and Empirical Data on Modeling of the Resting-State Brain Dynamics
000905236 260__ $$c2021
000905236 3367_ $$033$$2EndNote$$aConference Paper
000905236 3367_ $$2DataCite$$aOther
000905236 3367_ $$2BibTeX$$aINPROCEEDINGS
000905236 3367_ $$2DRIVER$$aconferenceObject
000905236 3367_ $$2ORCID$$aLECTURE_SPEECH
000905236 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1642507934_6597$$xInvited
000905236 520__ $$aModern approaches to investigation of complex brain dynamics suggest to represent the brain as a functional network where nodes encapsulate brain-region-specific function while edges consolidate the structural or functional connectivity among these regions. Brain regions can be delimited using a parcellation, i.e., brain atlas. There is however no consensus on which brain atlas is more adequate for one or another analysis. We address this problem by a dynamical modeling approach, where the resting-state brain dynamics is simulated by the whole-brain personalized models derived from and validated against empirical neuroimaging structural and functional data. We investigate the impact of the fitting modalities and brain atlases based on distinct anatomical and functional parcellation techniques. We show that these simulation conditions may strongly influence the modeling results including the quality of the model fitting and structure of the model parameter space. We also assess the variation of the fitting results across subjects and parcellations and observe that variation of selected data indices extracted from the experimental data may greatly account for the variations in the fitting results. At this, a few correlative types of the data variables can be distinguished depending on their intra- and inter-parcellation explanatory power. The obtained results can contribute to the improvement of the resting-state brain modeling and data analytics.
000905236 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000905236 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x1
000905236 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x2
000905236 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x3
000905236 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x4
000905236 536__ $$0G:(EU-Grant)826421$$aVirtualBrainCloud - Personalized Recommendations for Neurodegenerative Disease (826421)$$c826421$$fH2020-SC1-DTH-2018-1$$x5
000905236 65017 $$0V:(DE-MLZ)GC-130-2016$$2V:(DE-HGF)$$aHealth and Life$$x0
000905236 7001_ $$0P:(DE-Juel1)178611$$aJung, Kyesam$$b1$$ufzj
000905236 7001_ $$0P:(DE-Juel1)164577$$aManos, Thanos$$b2
000905236 7001_ $$0P:(DE-Juel1)165859$$aDiaz, Sandra$$b3$$ufzj
000905236 7001_ $$0P:(DE-Juel1)131684$$aHoffstaedter, Felix$$b4$$ufzj
000905236 7001_ $$0P:(DE-Juel1)169295$$aSchreiber, Jan$$b5
000905236 7001_ $$0P:(DE-HGF)0$$aThomas Yeo, B. T.$$b6
000905236 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b7$$ufzj
000905236 8564_ $$uhttps://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=71474
000905236 909CO $$ooai:juser.fz-juelich.de:905236$$pec_fundedresources$$pVDB$$popenaire
000905236 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131880$$aForschungszentrum Jülich$$b0$$kFZJ
000905236 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178611$$aForschungszentrum Jülich$$b1$$kFZJ
000905236 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165859$$aForschungszentrum Jülich$$b3$$kFZJ
000905236 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131684$$aForschungszentrum Jülich$$b4$$kFZJ
000905236 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a National University of Singapore$$b6
000905236 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b7$$kFZJ
000905236 9131_ $$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
000905236 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$$x1
000905236 9131_ $$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
000905236 9141_ $$y2021
000905236 920__ $$lyes
000905236 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
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000905236 980__ $$aI:(DE-Juel1)INM-7-20090406
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