000905257 001__ 905257
000905257 005__ 20220131120324.0
000905257 037__ $$aFZJ-2022-00540
000905257 041__ $$aEnglish
000905257 1001_ $$0P:(DE-Juel1)178611$$aJung, Kyesam$$b0$$eCorresponding author$$ufzj
000905257 1112_ $$aINM & IBI Retreat 2021, Forschungszentrum Jülich$$cVirtual Conference$$d2021-10-05 - 2021-10-06$$wGermany
000905257 245__ $$aTractography density affects whole-brain structural architecture and resting-state dynamical modeling
000905257 260__ $$c2021
000905257 3367_ $$033$$2EndNote$$aConference Paper
000905257 3367_ $$2BibTeX$$aINPROCEEDINGS
000905257 3367_ $$2DRIVER$$aconferenceObject
000905257 3367_ $$2ORCID$$aCONFERENCE_POSTER
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000905257 520__ $$aDynamical modeling of the resting-state brain dynamics essentially relies on the empirical neuroimaging data utilized for the model derivation and validation. There is however still no standardized data processing for magnetic resonance imaging pipelines and the structural and functionalconnectomes involved in the models. In this study, we thus address how the parameters of diffusion-weighted data processing for structural connectivity (SC) can influence the validation results of thewhole-brain mathematical models informed by SC. For this, we introduce a set of simulation conditions including the varying number of total streamlines of the whole-brain tractography (WBT)used for extraction of SC, cortical parcellations based on functional and anatomical brain propertiesand distinct model fitting modalities. The main objective of this study is to explore how the qualityof the model validation can vary across the considered simulation conditions. We observed that thegraph-theoretical network properties of structural connectome can be affected by varying tractography density and strongly relate to the model performance. We also found that the optimal numberof the total streamlines of WBT can vary for different brain atlases. Consequently, we suggest a wayhow to improve the model performance based on the network properties and the optimal parameter configurations from multiple WBT conditions. Furthermore, the population of subjects can bestratified into subgroups with divergent behaviors induced by the varying WBT density such thatdifferent recommendations can be made with respect to the data processing for individual subjectsand brain parcellations. Consequently, we list a few tentative guidelines to possible evaluation ofpersonalized optimal number of the WBT streamlines for the whole-brain model of the resting-statebrain dynamics.
000905257 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000905257 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x1
000905257 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x2
000905257 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x3
000905257 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x4
000905257 536__ $$0G:(EU-Grant)826421$$aVirtualBrainCloud - Personalized Recommendations for Neurodegenerative Disease (826421)$$c826421$$fH2020-SC1-DTH-2018-1$$x5
000905257 65017 $$0V:(DE-MLZ)GC-130-2016$$2V:(DE-HGF)$$aHealth and Life$$x0
000905257 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b1$$ufzj
000905257 7001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr$$b2$$ufzj
000905257 8564_ $$uhttps://events.hifis.net/event/161/
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000905257 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$$x0
000905257 9131_ $$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
000905257 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$$x2
000905257 9141_ $$y2021
000905257 920__ $$lyes
000905257 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
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000905257 980__ $$aVDB
000905257 980__ $$aI:(DE-Juel1)INM-7-20090406
000905257 980__ $$aUNRESTRICTED