001034446 001__ 1034446
001034446 005__ 20241219210859.0
001034446 0247_ $$2doi$$a10.25493/29RQ-MSM
001034446 037__ $$aFZJ-2024-07212
001034446 1001_ $$0P:(DE-HGF)0$$aVogt, Brent A.$$b0
001034446 245__ $$aProbabilistic cytoarchitectonic map of Area p29 (retrosplenial) (v11.0)
001034446 260__ $$bEBRAINS$$c2024
001034446 3367_ $$2BibTeX$$aMISC
001034446 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1734602422_7060
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001034446 3367_ $$2DataCite$$aDataset
001034446 3367_ $$2ORCID$$aDATA_SET
001034446 3367_ $$2DINI$$aResearchData
001034446 520__ $$aThis dataset contains the distinct probabilistic cytoarchitectonic map of Area p29 (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 p29 (retrosplenial). The probability map of Area p29 (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 p29 (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)
001034446 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001034446 536__ $$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
001034446 588__ $$aDataset connected to DataCite
001034446 650_7 $$2Other$$aNeuroscience
001034446 7001_ $$0P:(DE-Juel1)131660$$aMohlberg, Hartmut$$b1$$ufzj
001034446 7001_ $$0P:(DE-Juel1)131714$$aZilles, Karl$$b2
001034446 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b3$$eCorresponding author$$ufzj
001034446 7001_ $$0P:(DE-Juel1)131701$$aPalomero-Gallagher, Nicola$$b4$$ufzj
001034446 773__ $$a10.25493/29RQ-MSM
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001034446 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131660$$aForschungszentrum Jülich$$b1$$kFZJ
001034446 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b3$$kFZJ
001034446 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131701$$aForschungszentrum Jülich$$b4$$kFZJ
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001034446 9141_ $$y2024
001034446 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001034446 980__ $$adataset
001034446 980__ $$aVDB
001034446 980__ $$aI:(DE-Juel1)INM-1-20090406
001034446 980__ $$aUNRESTRICTED