001052822 001__ 1052822
001052822 005__ 20260128204143.0
001052822 0247_ $$2doi$$a10.25493/MCV8-9T7
001052822 037__ $$aFZJ-2026-01179
001052822 1001_ $$0P:(DE-Juel1)178766$$aKuckertz, Anika$$b0$$ufzj
001052822 245__ $$aParcellation scheme of rat iso- and proisocortical areas in Waxholm space (v3)
001052822 260__ $$bEBRAINS$$c2026
001052822 3367_ $$2BibTeX$$aMISC
001052822 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1769600034_31423
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001052822 3367_ $$2DataCite$$aDataset
001052822 3367_ $$2ORCID$$aDATA_SET
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001052822 520__ $$aThe present dataset provides a three-dimensional (3D) map of the rat iso- and proisocortex anchored in the standardized coordinate system of the Waxholm rat brain space. The parcellation encompasses 53 distinct areas and is the result of a multimodal analysis based on the heterogeneous regional and laminar distribution patterns of cell bodies and of muscarinic acetylcholine M2 receptors visualized in 2D sections. The receptor autoradiographs of a rat brain serially sectioned in the coronal plane were 3D reconstructed and anchored in Waxholm space. The ensuing transformation matrices were then applied to the delineations of the identified areas on the corresponding 2D autoradiographs, resulting in the registration of the location and extent of each area to the 3D reference Waxholm Space of the rat brain. This novel parcellation scheme of the rat iso- and proisocortical areas provides a high-resolution reference resource for the architectonically informed analysis of in vivo data and computational modelling approaches of the rat brain. As a dynamic framework, the Waxholm Rat Brain Atlas can be expanded by data of e.g., further neurotransmitter receptor types and brain regions.
001052822 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001052822 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$$x1
001052822 536__ $$0G:(DE-HGF)InterLabs-0015$$aHIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)$$cInterLabs-0015$$x2
001052822 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x3
001052822 588__ $$aDataset connected to DataCite
001052822 650_7 $$2Other$$aNeuroscience
001052822 7001_ $$0P:(DE-HGF)0$$aHaghir, Hossein$$b1
001052822 7001_ $$0P:(DE-Juel1)131701$$aPalomero-Gallagher, Nicola$$b2$$ufzj
001052822 7001_ $$0P:(DE-Juel1)181092$$aFunck, Thomas$$b3$$ufzj
001052822 773__ $$a10.25493/MCV8-9T7
001052822 909CO $$ooai:juser.fz-juelich.de:1052822$$popenaire$$pVDB$$pec_fundedresources
001052822 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178766$$aForschungszentrum Jülich$$b0$$kFZJ
001052822 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131701$$aForschungszentrum Jülich$$b2$$kFZJ
001052822 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)181092$$aForschungszentrum Jülich$$b3$$kFZJ
001052822 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)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001052822 9141_ $$y2026
001052822 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001052822 980__ $$adataset
001052822 980__ $$aVDB
001052822 980__ $$aI:(DE-Juel1)INM-1-20090406
001052822 980__ $$aUNRESTRICTED