000911542 001__ 911542
000911542 005__ 20221118130917.0
000911542 0247_ $$2doi$$a10.25493/JN40-NCG
000911542 037__ $$aFZJ-2022-04802
000911542 1001_ $$0P:(DE-Juel1)170068$$aSchiffer, Christian$$b0$$ufzj
000911542 245__ $$aUltrahigh-resolution 3D cytoarchitectonic map of Area MFG1 of the human anterior dorsolateral prefrontal cortex (DLPFC) created by a Deep-Learning assisted workflow (v1)
000911542 260__ $$bEBRAINS$$c2022
000911542 3367_ $$2BibTeX$$aMISC
000911542 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1668771383_32571
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000911542 3367_ $$2DataCite$$aDataset
000911542 3367_ $$2ORCID$$aDATA_SET
000911542 3367_ $$2DINI$$aResearchData
000911542 520__ $$aThis dataset contains automatically created detailed map of the area MFG1 of the human anterior dorsolateral prefrontal cortex (DLPFC) in the BigBrain dataset. The mappings were created using Deep Convolutional Neural Networks based on Schiffer et al. 2021, which were trained on delineations on at least every 30th section created based on Bruno et al. 2022. Mappings are available on every section. Their quality was observed by a trained neuroscientist to exclude sections with low-quality results from further processing. Automatic mappings were transformed to the 3D reconstructed BigBrain space using transformations used in Amunts et al. 2013, which were provided by Claude Lepage (McGill). Mappings on individual sections were used to assemble 3D volumes of all areas. Low-quality results were replaced by interpolation between nearest neighboring sections. The volumes were then smoothed using a 3D median filter, and the largest connected components were identified to remove false positive results of the classification algorithm. The dataset consists of an HDF5 file containing the volume in RAS dimension ordering (20-micron isotropic resolution, dataset “volume”) and an affine transformation matrix (dataset “affine”). An additional dataset, “interpolation_info”, contains an integer vector with an integer value for each section which indicates if a section was replaced by interpolation due to low-quality results (value 2) or not (value 1). Due to the large size of the volume, it is recommended to view the data online using the provided viewer link.
000911542 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
000911542 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
000911542 588__ $$aDataset connected to DataCite
000911542 650_7 $$2Other$$aNeuroscience
000911542 7001_ $$0P:(DE-Juel1)173629$$aBruno, Ariane$$b1$$ufzj
000911542 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b2$$ufzj
000911542 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b3$$ufzj
000911542 773__ $$a10.25493/JN40-NCG
000911542 909CO $$ooai:juser.fz-juelich.de:911542$$popenaire$$pVDB$$pec_fundedresources
000911542 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)170068$$aForschungszentrum Jülich$$b0$$kFZJ
000911542 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)173629$$aForschungszentrum Jülich$$b1$$kFZJ
000911542 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b2$$kFZJ
000911542 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165746$$aForschungszentrum Jülich$$b3$$kFZJ
000911542 9131_ $$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
000911542 9141_ $$y2022
000911542 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
000911542 980__ $$adataset
000911542 980__ $$aVDB
000911542 980__ $$aI:(DE-Juel1)INM-1-20090406
000911542 980__ $$aUNRESTRICTED