000888103 001__ 888103
000888103 005__ 20210130010817.0
000888103 0247_ $$2doi$$a10.25493/XTRR-172
000888103 037__ $$aFZJ-2020-04680
000888103 1001_ $$0P:(DE-Juel1)131701$$aPalomero-Gallagher, Nicola$$b0$$ufzj
000888103 245__ $$aProbabilistic cytoarchitectonic map of Area s32 (sACC) (v16.1)
000888103 260__ $$bHuman Brain Project Neuroinformatics Platform$$c2019
000888103 3367_ $$2BibTeX$$aMISC
000888103 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1605882431_972
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000888103 3367_ $$2DataCite$$aDataset
000888103 3367_ $$2ORCID$$aDATA_SET
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000888103 520__ $$aThis dataset contains the distinct architectonic Area s32 (sACC) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to Area s32 (sACC). The probability map of Area s32 (sACC) is provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area s32 (sACC): Palomero-Gallagher et al. (2018) [Data set, v16.0] [DOI: 10.25493/3PBV-WH0](https://doi.org/10.25493%2F3PBV-WH0) The most probable delineation of Area s32 (sACC) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
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000888103 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x1
000888103 588__ $$aDataset connected to DataCite
000888103 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b1$$ufzj
000888103 7001_ $$0P:(DE-Juel1)131684$$aHoffstaedter, Felix$$b2$$ufzj
000888103 7001_ $$0P:(DE-Juel1)145628$$aSchleicher, Axel$$b3
000888103 7001_ $$0P:(DE-Juel1)131660$$aMohlberg, Hartmut$$b4$$ufzj
000888103 7001_ $$0P:(DE-Juel1)159352$$aVogt, Brent Alan$$b5
000888103 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b6$$ufzj
000888103 7001_ $$0P:(DE-Juel1)131714$$aZilles, Karl$$b7$$ufzj
000888103 773__ $$a10.25493/XTRR-172
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000888103 9141_ $$y2020
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