000905260 001__ 905260
000905260 005__ 20220131120324.0
000905260 037__ $$aFZJ-2022-00543
000905260 041__ $$aEnglish
000905260 1001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh$$b0$$ufzj
000905260 1112_ $$aINM & IBI Retreat 2021, Forschungszentrum Jülich$$cVirtual Conference$$d2021-10-05 - 2021-10-06$$wGermany
000905260 245__ $$aGender Differences in Empirical and Simulated Brain Connectomes
000905260 260__ $$c2021
000905260 3367_ $$033$$2EndNote$$aConference Paper
000905260 3367_ $$2BibTeX$$aINPROCEEDINGS
000905260 3367_ $$2DRIVER$$aconferenceObject
000905260 3367_ $$2ORCID$$aCONFERENCE_POSTER
000905260 3367_ $$2DataCite$$aOutput Types/Conference Poster
000905260 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1642166555_6966$$xAfter Call
000905260 520__ $$aInvestigating gender differences in brain connectomes has been an active area of research in neuroscience. Previous studies have, however, explored only the empirical connectomes. This projectconsiders simulated brain connectomes generated by whole-brain dynamical models and their correlation with the empirical connectomes to investigate gender differences. The analysis involves 272subjects from the human connectome project (144 females). For each individual and 11 brain parcellation schemes, we calculated an empirical structural connectivity (eSC), an empirical functionalconnectivity (eFC) of the resting-state fMRI BOLD signals and two simulated functional connectivity(sFC) matrices based on the ensembles of coupled phase- (PO) and limit-cycle (LC) oscillators. Thegender difference was then investigated using the Wilcoxon sum ranks test of the Pearson’s correlation coefficient corr(sFC, eFC) between the simulated and the empirical functional connectomes. Weobserved a significantly higher correlation for males for 11 parcellations. Since the models utilizethe empirical information, we regressed out the brain size and empirical structure-function relationship corr(eFC, eSC), to check if the gender difference still persists. After the regression, thisdifference remains significant for 10 atlases for PO model and for 8 atlases for LC model. Interestingly, the gender difference in corr(eFC, eSC) showed an opposite trend - the females showed a betterstructure-function correspondence than males. This is in contrast with the modeling results, wherea better fit between sFC and eFC is observed for males. A potential reason for this discrepancy couldbe the difference in complexity of the empirical data between genders, which in turn may influencethe quality of the model fitting. The project currently aims to examine this in more detail.
000905260 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000905260 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x1
000905260 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x2
000905260 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x3
000905260 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x4
000905260 536__ $$0G:(EU-Grant)826421$$aVirtualBrainCloud - Personalized Recommendations for Neurodegenerative Disease (826421)$$c826421$$fH2020-SC1-DTH-2018-1$$x5
000905260 65017 $$0V:(DE-MLZ)GC-130-2016$$2V:(DE-HGF)$$aHealth and Life$$x0
000905260 7001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr$$b1$$ufzj
000905260 7001_ $$0P:(DE-Juel1)187216$$aJain, Shraddha$$b2$$eCorresponding author$$ufzj
000905260 8564_ $$uhttps://events.hifis.net/event/161/
000905260 909CO $$ooai:juser.fz-juelich.de:905260$$pec_fundedresources$$pVDB$$popenaire
000905260 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172843$$aForschungszentrum Jülich$$b0$$kFZJ
000905260 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131880$$aForschungszentrum Jülich$$b1$$kFZJ
000905260 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)187216$$aForschungszentrum Jülich$$b2$$kFZJ
000905260 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$$x0
000905260 9131_ $$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
000905260 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$$x2
000905260 9141_ $$y2021
000905260 920__ $$lyes
000905260 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
000905260 980__ $$aposter
000905260 980__ $$aVDB
000905260 980__ $$aI:(DE-Juel1)INM-7-20090406
000905260 980__ $$aUNRESTRICTED