001048769 001__ 1048769
001048769 005__ 20251202203138.0
001048769 0247_ $$2doi$$a10.25493/81J8-0G9
001048769 037__ $$aFZJ-2025-04884
001048769 1001_ $$0P:(DE-HGF)0$$aLepage, Claude$$b0$$eCorresponding author
001048769 245__ $$aBigBrain2 3D whole brain model (v1)
001048769 260__ $$bEBRAINS$$c2025
001048769 3367_ $$2BibTeX$$aMISC
001048769 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1764676870_26251
001048769 3367_ $$026$$2EndNote$$aChart or Table
001048769 3367_ $$2DataCite$$aDataset
001048769 3367_ $$2ORCID$$aDATA_SET
001048769 3367_ $$2DINI$$aResearchData
001048769 520__ $$aThis dataset contains the BigBrain2 whole-brain model, a 3D reconstruction at 20µm resolution from digital scans of 7676 coronal histological sections of the brain of a deceased 30 years old, male organ donor. The brain sections were stained for cell bodies using the same procedure as for the original BigBrain whole-brain model ([Amunts et al. 2013](https://doi.org/10.1126/science.1235381)). BigBrain2 will contribute new insight into inter-subject cytoarchitectonic variability. Due to technical advances, BigBrain2 offers better quality staining, favourable to regional segmentation and registration, and contains fewer artifacts through sectioning and staining than the original BigBrain. Same as the original BigBrain, its native space can be used as a common coordinate space (full name: BigBrain2 whole-brain model; short name: BigBrain2; abbreviation: BB2; version: 1.0) to anatomical anchor data at high resolution. An additional image registration of BigBrain2 to the MNI ICBM152 Average Brain Stereotaxic Registration Model (short name: MNI152; version: 2009c, nonlinear, asymmetric) preserves comparability to functional imaging studies.
001048769 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001048769 536__ $$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
001048769 536__ $$0G:(DE-HGF)InterLabs-0015$$aHIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)$$cInterLabs-0015$$x2
001048769 588__ $$aDataset connected to DataCite
001048769 650_7 $$2Other$$aNeuroscience
001048769 7001_ $$0P:(DE-Juel1)131660$$aMohlberg, Hartmut$$b1$$eCorresponding author$$ufzj
001048769 7001_ $$0P:(DE-HGF)0$$aLewis, Lindsay B.$$b2
001048769 7001_ $$0P:(DE-HGF)0$$aToussaint, Paule-Joanne$$b3
001048769 7001_ $$0P:(DE-Juel1)174282$$aWenzel, Susanne$$b4$$ufzj
001048769 7001_ $$0P:(DE-HGF)0$$aEvans, Alan C.$$b5
001048769 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b6$$ufzj
001048769 773__ $$a10.25493/81J8-0G9
001048769 909CO $$ooai:juser.fz-juelich.de:1048769$$popenaire$$pVDB$$pec_fundedresources
001048769 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131660$$aForschungszentrum Jülich$$b1$$kFZJ
001048769 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)174282$$aForschungszentrum Jülich$$b4$$kFZJ
001048769 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b6$$kFZJ
001048769 9131_ $$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
001048769 9141_ $$y2025
001048769 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001048769 980__ $$adataset
001048769 980__ $$aVDB
001048769 980__ $$aI:(DE-Juel1)INM-1-20090406
001048769 980__ $$aUNRESTRICTED