001033957 001__ 1033957
001033957 005__ 20241213210711.0
001033957 0247_ $$2doi$$a10.25493/EK0H-B9W
001033957 037__ $$aFZJ-2024-06795
001033957 1001_ $$0P:(DE-Juel1)144196$$aRuland, Sabine Helene$$b0
001033957 245__ $$aProbabilistic cytoarchitectonic map of Area 6v2 (PreCG) (v11.0)
001033957 260__ $$bEBRAINS$$c2024
001033957 3367_ $$2BibTeX$$aMISC
001033957 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1734090593_7860
001033957 3367_ $$026$$2EndNote$$aChart or Table
001033957 3367_ $$2DataCite$$aDataset
001033957 3367_ $$2ORCID$$aDATA_SET
001033957 3367_ $$2DINI$$aResearchData
001033957 520__ $$aThis dataset contains the probabilistic map (PM) of the ventral premotor area (Area 6v2 (PreCG)) of the ventral precentral gyrus of the human brain. As part of the Julich-Brain Atlas (JBA), the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to common brain reference spaces, where each voxel was assigned the probability to belong to Area 6v2 (PreCG). The PM of Area 6v2 (PreCG) is provided in NifTi-1 format for each brain reference space and hemisphere. The JBA relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures that was described by [Amunts et al. in 2020](https://doi.org/10.1126/science.abb4588). Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.
001033957 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001033957 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$$x1
001033957 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
001033957 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
001033957 536__ $$0G:(DE-HGF)InterLabs-0015$$aHIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)$$cInterLabs-0015$$x4
001033957 588__ $$aDataset connected to DataCite
001033957 650_7 $$2Other$$aNeuroscience
001033957 7001_ $$0P:(DE-Juel1)171897$$aSigl, Benjamin$$b1
001033957 7001_ $$0P:(DE-HGF)0$$aStangier, Jeanette$$b2
001033957 7001_ $$0P:(DE-Juel1)131675$$aCaspers, Svenja$$b3$$ufzj
001033957 7001_ $$0P:(DE-Juel1)131660$$aMohlberg, Hartmut$$b4$$ufzj
001033957 7001_ $$0P:(DE-Juel1)131636$$aBludau, Sebastian$$b5$$ufzj
001033957 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b6$$eCorresponding author
001033957 773__ $$a10.25493/EK0H-B9W
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001033957 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144196$$aForschungszentrum Jülich$$b0$$kFZJ
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001033957 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131660$$aForschungszentrum Jülich$$b4$$kFZJ
001033957 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131636$$aForschungszentrum Jülich$$b5$$kFZJ
001033957 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b6$$kFZJ
001033957 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$$x0
001033957 9141_ $$y2024
001033957 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001033957 980__ $$adataset
001033957 980__ $$aVDB
001033957 980__ $$aI:(DE-Juel1)INM-1-20090406
001033957 980__ $$aUNRESTRICTED