000888094 001__ 888094
000888094 005__ 20210130010814.0
000888094 0247_ $$2doi$$a10.25493/Z9JX-WKB
000888094 037__ $$aFZJ-2020-04671
000888094 1001_ $$0P:(DE-HGF)0$$aMalikovic, A.$$b0
000888094 245__ $$aProbabilistic cytoarchitectonic map of Area hOc4la (LOC) (v3.4)
000888094 260__ $$bHuman Brain Project Neuroinformatics Platform$$c2019
000888094 3367_ $$2BibTeX$$aMISC
000888094 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1605867025_1333
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000888094 3367_ $$2DataCite$$aDataset
000888094 3367_ $$2ORCID$$aDATA_SET
000888094 3367_ $$2DINI$$aResearchData
000888094 520__ $$aThis dataset contains the distinct architectonic Area hOc4la (LOC) 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 hOc4la (LOC). The probability map of Area hOc4la (LOC) 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 hOc4la (LOC): Malikovic et al. (2018) [Data set, v3.2] [DOI: 10.25493/FCQW-EZU](https://doi.org/10.25493%2FFCQW-EZU) The most probable delineation of Area hOc4la (LOC) 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)
000888094 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000888094 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x1
000888094 588__ $$aDataset connected to DataCite
000888094 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b1$$ufzj
000888094 7001_ $$0P:(DE-Juel1)145628$$aSchleicher, Axel$$b2
000888094 7001_ $$0P:(DE-Juel1)131660$$aMohlberg, Hartmut$$b3$$ufzj
000888094 7001_ $$0P:(DE-HGF)0$$aKujovic, M.$$b4
000888094 7001_ $$0P:(DE-Juel1)131701$$aPalomero-Gallagher, Nicola$$b5$$ufzj
000888094 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b6$$ufzj
000888094 7001_ $$0P:(DE-Juel1)131714$$aZilles, Karl$$b7$$ufzj
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000888094 9141_ $$y2020
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