000911540 001__ 911540
000911540 005__ 20221118130917.0
000911540 0247_ $$2doi$$a10.25493/6M5A-JJ0
000911540 037__ $$aFZJ-2022-04800
000911540 1001_ $$0P:(DE-Juel1)170068$$aSchiffer, Christian$$b0$$ufzj
000911540 245__ $$aUltrahigh-resolution 3D cytoarchitectonic map of Area SFS1 of the human anterior dorsolateral prefrontal cortex (DLPFC) created by a Deep-Learning assisted workflow (v1)
000911540 260__ $$bEBRAINS$$c2022
000911540 3367_ $$2BibTeX$$aMISC
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000911540 3367_ $$2DataCite$$aDataset
000911540 3367_ $$2ORCID$$aDATA_SET
000911540 3367_ $$2DINI$$aResearchData
000911540 520__ $$aThis dataset contains automatically created detailed map of the area SFS1 of the human anterior dorsolateral prefrontal cortex (DLPFC) in the BigBrain dataset. The mappings were created using Deep Convolutional Neural Networks based on Schiffer et al. 2021, which were trained on delineations on at least every 30th section created based on Bruno et al. 2022. Mappings are available on every section. Their quality was observed by a trained neuroscientist to exclude sections with low-quality results from further processing. Automatic mappings were transformed to the 3D reconstructed BigBrain space using transformations used in Amunts et al. 2013, which were provided by Claude Lepage (McGill). Mappings on individual sections were used to assemble 3D volumes of all areas. Low-quality results were replaced by interpolation between nearest neighboring sections. The volumes were then smoothed using a 3D median filter, and the largest connected components were identified to remove false positive results of the classification algorithm. The dataset consists of an HDF5 file containing the volume in RAS dimension ordering (20-micron isotropic resolution, dataset “volume”) and an affine transformation matrix (dataset “affine”). An additional dataset, “interpolation_info”, contains an integer vector with an integer value for each section which indicates if a section was replaced by interpolation due to low-quality results (value 2) or not (value 1). Due to the large size of the volume, it is recommended to view the data online using the provided viewer link.
000911540 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
000911540 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
000911540 588__ $$aDataset connected to DataCite
000911540 650_7 $$2Other$$aNeuroscience
000911540 7001_ $$0P:(DE-Juel1)173629$$aBruno, Ariane$$b1$$ufzj
000911540 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b2$$ufzj
000911540 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b3$$ufzj
000911540 773__ $$a10.25493/6M5A-JJ0
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000911540 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)170068$$aForschungszentrum Jülich$$b0$$kFZJ
000911540 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)173629$$aForschungszentrum Jülich$$b1$$kFZJ
000911540 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b2$$kFZJ
000911540 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165746$$aForschungszentrum Jülich$$b3$$kFZJ
000911540 9131_ $$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
000911540 9141_ $$y2022
000911540 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
000911540 980__ $$adataset
000911540 980__ $$aVDB
000911540 980__ $$aI:(DE-Juel1)INM-1-20090406
000911540 980__ $$aUNRESTRICTED