001034468 001__ 1034468
001034468 005__ 20241218210704.0
001034468 0247_ $$2doi$$a10.25493/78A4-KTU
001034468 037__ $$aFZJ-2024-07234
001034468 1001_ $$0P:(DE-Juel1)184804$$aOberstraß, Alexander$$b0$$eCorresponding author$$ufzj
001034468 245__ $$aDeep texture features characterizing fiber architecture in the vervet monkey occipital lobe (v1)
001034468 260__ $$bEBRAINS$$c2024
001034468 3367_ $$2BibTeX$$aMISC
001034468 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1734523250_24846
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001034468 3367_ $$2DataCite$$aDataset
001034468 3367_ $$2ORCID$$aDATA_SET
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001034468 520__ $$aThis dataset contains features for characterizing fiber architecture in three-dimensional polarized light imaging (3D-PLI) for the right occipital lobe from a single vervet monkey brain, comprising 234 brain sections. The data include volumetric PCA projections of deep texture features, clusterings, as well as measures of cortical morphology, such as curvature, cortical depth, white matter depth, and section obliqueness. PCA projections form 20-dimensional vectors, each representing a square image patch of 169 μm, sampled at a particular section and location in the brain. Morphological features were obtained through a cortex segmentation of the volume to explore correlations between extracted features and morphology. Deep texture features were extracted using a self-supervised 3D-Context Contrastive Learning (CL-3D) model, trained on high-resolution microscopic 3D-PLI images. These features reveal patterns of fiber architecture, including distinctions between gray and white matter, myeloarchitectonic layer structures, fiber bundles, fiber crossings, and fiber fannings. Of the 234 sections, 117 were used to train the feature extraction model, and the remaining 117 are provided for analysis. All volumes are spatially aligned with the Average MRI Vervet Atlas of the UCLA Brain Mapping Center using affine matrices. The alignment allows each texture feature to be assigned to a 3D coordinate in this space, referring to the centroid of the patch, for comparison with other data.
001034468 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001034468 536__ $$0G:(DE-HGF)InterLabs-0015$$aHIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)$$cInterLabs-0015$$x1
001034468 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
001034468 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$$x3
001034468 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x4
001034468 588__ $$aDataset connected to DataCite
001034468 650_7 $$2Other$$aNeuroscience
001034468 7001_ $$aMuenzing, Sascha E. A.$$b1
001034468 7001_ $$0P:(DE-Juel1)171512$$aNiu, Meiqi$$b2$$ufzj
001034468 7001_ $$0P:(DE-Juel1)131701$$aPalomero-Gallagher, Nicola$$b3$$ufzj
001034468 7001_ $$0P:(DE-Juel1)170068$$aSchiffer, Christian$$b4$$ufzj
001034468 7001_ $$aJorgensen, Matthew J.$$b5
001034468 7001_ $$aWoods, Roger$$b6
001034468 7001_ $$0P:(DE-Juel1)131632$$aAxer, Markus$$b7$$ufzj
001034468 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b8$$ufzj
001034468 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b9$$eCorresponding author$$ufzj
001034468 773__ $$a10.25493/78A4-KTU
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001034468 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)184804$$aForschungszentrum Jülich$$b0$$kFZJ
001034468 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171512$$aForschungszentrum Jülich$$b2$$kFZJ
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001034468 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b8$$kFZJ
001034468 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165746$$aForschungszentrum Jülich$$b9$$kFZJ
001034468 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$$x0
001034468 9141_ $$y2024
001034468 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001034468 980__ $$adataset
001034468 980__ $$aVDB
001034468 980__ $$aI:(DE-Juel1)INM-1-20090406
001034468 980__ $$aUNRESTRICTED