001033983 001__ 1033983
001033983 005__ 20241212210728.0
001033983 0247_ $$2doi$$a10.25493/YCEZ-0H1
001033983 037__ $$aFZJ-2024-06819
001033983 1001_ $$0P:(DE-599)DNB931807816$$aVogt, Brent A.$$b0$$eCorresponding author
001033983 245__ $$aProbabilistic cytoarchitectonic map of Area a30 (retrosplenial) (v11.0)
001033983 260__ $$bEBRAINS$$c2024
001033983 3367_ $$2BibTeX$$aMISC
001033983 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1734014652_5342
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001033983 3367_ $$2DataCite$$aDataset
001033983 3367_ $$2ORCID$$aDATA_SET
001033983 3367_ $$2DINI$$aResearchData
001033983 520__ $$aThis dataset contains the distinct probabilistic cytoarchitectonic map of Area a30 (retrosplenial) in the individual, single subject template of the MNI Colin 27 reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to the reference space, where each voxel was assigned the probability to belong to Area a30 (retrosplenial). The probability map of Area a30 (retrosplenial) is provided in NifTi format for each hemisphere in the reference space. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and updated probability estimates for new brain structures may in some cases lead to measurable but negligible deviations of existing probability maps, as compared to earlier released datasets. The most probable delineation of Area a30 (retrosplenial) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-64D)
001033983 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001033983 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$$x1
001033983 588__ $$aDataset connected to DataCite
001033983 650_7 $$2Other$$aNeuroscience
001033983 7001_ $$0P:(DE-Juel1)131660$$aMohlberg, Hartmut$$b1
001033983 7001_ $$0P:(DE-Juel1)131714$$aZilles, Karl$$b2
001033983 7001_ $$0P:(DE-Juel1)131701$$aPalomero-Gallagher, Nicola$$b3
001033983 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b4$$eCorresponding author
001033983 773__ $$a10.25493/YCEZ-0H1
001033983 909CO $$ooai:juser.fz-juelich.de:1033983$$popenaire$$pVDB$$pec_fundedresources
001033983 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131660$$aForschungszentrum Jülich$$b1$$kFZJ
001033983 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131701$$aForschungszentrum Jülich$$b3$$kFZJ
001033983 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b4$$kFZJ
001033983 9131_ $$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
001033983 9141_ $$y2024
001033983 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001033983 980__ $$adataset
001033983 980__ $$aVDB
001033983 980__ $$aI:(DE-Juel1)INM-1-20090406
001033983 980__ $$aUNRESTRICTED