001033981 001__ 1033981
001033981 005__ 20241212210728.0
001033981 0247_ $$2doi$$a10.25493/SSBT-A4U
001033981 037__ $$aFZJ-2024-06817
001033981 1001_ $$0P:(DE-HGF)0$$aVogt, Brent A.$$b0
001033981 245__ $$aProbabilistic cytoarchitectonic map of Area i30 (retrosplenial) (v11.0)
001033981 260__ $$bEBRAINS$$c2024
001033981 3367_ $$2BibTeX$$aMISC
001033981 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1733987969_21024
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001033981 3367_ $$2DataCite$$aDataset
001033981 3367_ $$2ORCID$$aDATA_SET
001033981 3367_ $$2DINI$$aResearchData
001033981 520__ $$aThis dataset contains the distinct probabilistic cytoarchitectonic map of Area i30 (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 i30 (retrosplenial). The probability map of Area i30 (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 i30 (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)
001033981 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001033981 536__ $$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
001033981 588__ $$aDataset connected to DataCite
001033981 650_7 $$2Other$$aNeuroscience
001033981 7001_ $$0P:(DE-Juel1)131660$$aMohlberg, Hartmut$$b1
001033981 7001_ $$0P:(DE-Juel1)131714$$aZilles, Karl$$b2
001033981 7001_ $$0P:(DE-Juel1)131701$$aPalomero-Gallagher, Nicola$$b3
001033981 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b4$$eCorresponding author
001033981 773__ $$a10.25493/SSBT-A4U
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001033981 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131660$$aForschungszentrum Jülich$$b1$$kFZJ
001033981 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131701$$aForschungszentrum Jülich$$b3$$kFZJ
001033981 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b4$$kFZJ
001033981 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$$x0
001033981 9141_ $$y2024
001033981 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001033981 980__ $$adataset
001033981 980__ $$aVDB
001033981 980__ $$aI:(DE-Juel1)INM-1-20090406
001033981 980__ $$aUNRESTRICTED