001033960 001__ 1033960
001033960 005__ 20241213210712.0
001033960 0247_ $$2doi$$a10.25493/FP6M-FRP
001033960 037__ $$aFZJ-2024-06796
001033960 1001_ $$0P:(DE-HGF)0$$aStangier, Jeanette$$b0
001033960 245__ $$aProbabilistic cytoarchitectonic map of Area 6v1 (PreCG) (v11.0)
001033960 260__ $$bEBRAINS$$c2024
001033960 3367_ $$2BibTeX$$aMISC
001033960 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1734081829_7860
001033960 3367_ $$026$$2EndNote$$aChart or Table
001033960 3367_ $$2DataCite$$aDataset
001033960 3367_ $$2ORCID$$aDATA_SET
001033960 3367_ $$2DINI$$aResearchData
001033960 520__ $$aThis dataset contains the probabilistic map (PM) of the ventral premotor area (Area 6v1 (PreCG)) of the ventral precentral gyrus of the human brain. As part of the Julich-Brain Atlas (JBA), the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to common brain reference spaces, where each voxel was assigned the probability to belong to Area 6v1 (PreCG). The PM of Area 6v1 (PreCG) is provided in NifTi-1 format for each brain reference space and hemisphere. The JBA relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures that was described by [Amunts et al. in 2020](https://doi.org/10.1126/science.abb4588). Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.
001033960 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001033960 536__ $$0G:(EU-Grant)101147319$$aEBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319)$$c101147319$$fHORIZON-INFRA-2022-SERV-B-01$$x1
001033960 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
001033960 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x3
001033960 536__ $$0G:(DE-HGF)InterLabs-0015$$aHIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)$$cInterLabs-0015$$x4
001033960 588__ $$aDataset connected to DataCite
001033960 650_7 $$2Other$$aNeuroscience
001033960 7001_ $$0P:(DE-Juel1)131675$$aCaspers, Svenja$$b1$$ufzj
001033960 7001_ $$0P:(DE-Juel1)144196$$aRuland, Sabine Helene$$b2$$eCorresponding author$$ufzj
001033960 7001_ $$0P:(DE-Juel1)131660$$aMohlberg, Hartmut$$b3$$ufzj
001033960 7001_ $$0P:(DE-Juel1)131636$$aBludau, Sebastian$$b4$$eCorresponding author$$ufzj
001033960 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b5$$eCorresponding author
001033960 773__ $$a10.25493/FP6M-FRP
001033960 909CO $$ooai:juser.fz-juelich.de:1033960$$popenaire$$pVDB$$pec_fundedresources
001033960 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131675$$aForschungszentrum Jülich$$b1$$kFZJ
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001033960 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131636$$aForschungszentrum Jülich$$b4$$kFZJ
001033960 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b5$$kFZJ
001033960 9131_ $$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$$vDecoding Brain Organization and Dysfunction$$x0
001033960 9141_ $$y2024
001033960 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001033960 980__ $$adataset
001033960 980__ $$aVDB
001033960 980__ $$aI:(DE-Juel1)INM-1-20090406
001033960 980__ $$aUNRESTRICTED