000888787 001__ 888787
000888787 005__ 20201230100827.0
000888787 0247_ $$2doi$$a10.25493/C2CW-HFW
000888787 037__ $$aFZJ-2020-05216
000888787 1001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr$$b0$$eCorresponding author$$ufzj
000888787 245__ $$aAveraged structural and functional connectivities of healthy cohorts based on whole-brain parcellations
000888787 260__ $$bEBRAINS$$c2020
000888787 3367_ $$2BibTeX$$aMISC
000888787 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1609239074_11204
000888787 3367_ $$026$$2EndNote$$aChart or Table
000888787 3367_ $$2DataCite$$aDataset
000888787 3367_ $$2ORCID$$aDATA_SET
000888787 3367_ $$2DINI$$aResearchData
000888787 520__ $$aBrain connectivity is one of the main objects of brain research reflecting the interaction between neuronal populations at a variety of spatial and temporal scales and modalities. The main approaches include the extraction and analysis of the structural connectivity (SC) representing the anatomical axonal tracts and functional connectivity (FC) representing the temporal correlation between neuronal activity of brain regions. Extraction of SC and FC is a complex process requiring a deep knowledge of the data processing methods. The current project is aimed at facilitation of this process and provides an access to the extracted brain connectivities ready for further usage. The data includes SC and resting-state FC for several brain atlases of different granularity, where the parcellation is performed based on functional, anatomical and cytoarchitectonic brain properties. The connectivity is extracted and averaged over several hundreds of healthy subjects from well-known public repositories of neuroimaging data, including the Human Connectome Project (HCP) [Van Essen et al., 2013](https://doi.org/10.1016/j.neuroimage.2013.05.041) and Enhanced Nathan Klein Institute Rockland Sample (eNKI) [Nooner et al., 2012](https://dx.doi.org/10.3389/fnins.2012.00152). The provided SC and FC can be used for group-level investigations of the connectivities and for data-driven mathematical modeling of the resting-state brain dynamics.
000888787 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000888787 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x1
000888787 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x2
000888787 588__ $$aDataset connected to DataCite
000888787 65027 $$0V:(DE-MLZ)SciArea-190$$2V:(DE-HGF)$$aMedicine$$x0
000888787 65017 $$0V:(DE-MLZ)GC-130-2016$$2V:(DE-HGF)$$aHealth and Life$$x0
000888787 7001_ $$0P:(DE-Juel1)178611$$aJung, Kyesam$$b1$$ufzj
000888787 7001_ $$0P:(DE-Juel1)179582$$aDomhof, Justin$$b2$$ufzj
000888787 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b3$$ufzj
000888787 773__ $$a10.25493/C2CW-HFW
000888787 8564_ $$uhttps://kg.ebrains.eu/search/instances/Dataset/50c215bc-4c65-4f11-a4cd-98cc92750977
000888787 909CO $$ooai:juser.fz-juelich.de:888787$$popenaire$$pVDB$$pec_fundedresources
000888787 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131880$$aForschungszentrum Jülich$$b0$$kFZJ
000888787 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178611$$aForschungszentrum Jülich$$b1$$kFZJ
000888787 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)179582$$aForschungszentrum Jülich$$b2$$kFZJ
000888787 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b3$$kFZJ
000888787 9131_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000888787 9141_ $$y2020
000888787 920__ $$lyes
000888787 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
000888787 980__ $$adataset
000888787 980__ $$aVDB
000888787 980__ $$aI:(DE-Juel1)INM-7-20090406
000888787 980__ $$aUNRESTRICTED