001     888103
005     20210130010817.0
024 7 _ |a 10.25493/XTRR-172
|2 doi
037 _ _ |a FZJ-2020-04680
100 1 _ |a Palomero-Gallagher, Nicola
|0 P:(DE-Juel1)131701
|b 0
|u fzj
245 _ _ |a Probabilistic cytoarchitectonic map of Area s32 (sACC) (v16.1)
260 _ _ |c 2019
|b Human Brain Project Neuroinformatics Platform
336 7 _ |a MISC
|2 BibTeX
336 7 _ |a Dataset
|b dataset
|m dataset
|0 PUB:(DE-HGF)32
|s 1605882431_972
|2 PUB:(DE-HGF)
336 7 _ |a Chart or Table
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336 7 _ |a Dataset
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336 7 _ |a DATA_SET
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336 7 _ |a ResearchData
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520 _ _ |a This dataset contains the distinct architectonic Area s32 (sACC) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using 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 both reference spaces, where each voxel was assigned the probability to belong to Area s32 (sACC). The probability map of Area s32 (sACC) is provided in the NifTi format for each brain reference space and hemisphere. 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 integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area s32 (sACC): Palomero-Gallagher et al. (2018) [Data set, v16.0] [DOI: 10.25493/3PBV-WH0](https://doi.org/10.25493%2F3PBV-WH0) The most probable delineation of Area s32 (sACC) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
536 _ _ |a 574 - Theory, modelling and simulation (POF3-574)
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536 _ _ |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)
|0 G:(EU-Grant)785907
|c 785907
|f H2020-SGA-FETFLAG-HBP-2017
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588 _ _ |a Dataset connected to DataCite
700 1 _ |a Eickhoff, Simon
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700 1 _ |a Hoffstaedter, Felix
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700 1 _ |a Schleicher, Axel
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700 1 _ |a Mohlberg, Hartmut
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700 1 _ |a Vogt, Brent Alan
|0 P:(DE-Juel1)159352
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700 1 _ |a Amunts, Katrin
|0 P:(DE-Juel1)131631
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700 1 _ |a Zilles, Karl
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773 _ _ |a 10.25493/XTRR-172
909 C O |o oai:juser.fz-juelich.de:888103
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914 1 _ |y 2020
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