This title appears in the Scientific Report :
2020
Please use the identifier:
http://dx.doi.org/10.25493/FZXB-M6S in citations.
Probabilistic cytoarchitectonic map of Area p32 (pACC) (v16.0)
Probabilistic cytoarchitectonic map of Area p32 (pACC) (v16.0)
This dataset contains the distinct probabilistic cytoarchitectonic map of Area p32 (pACC) in the individual, single subject template of the MNI Colin 27 reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using classical histological criteria and quantitativ...
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Personal Name(s): | Palomero-Gallagher, Nicola |
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Hoffstaedter, Felix / Mohlberg, Hartmut / Eickhoff, Simon / Amunts, Katrin / Zilles, Karl | |
Contributing Institute: |
Strukturelle und funktionelle Organisation des Gehirns; INM-1 Gehirn & Verhalten; INM-7 |
Imprint: |
Human Brain Project Neuroinformatics Platform
2019
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DOI: |
10.25493/FZXB-M6S |
Document Type: |
Dataset |
Research Program: |
Human Brain Project Specific Grant Agreement 2 Theory, modelling and simulation |
Publikationsportal JuSER |
This dataset contains the distinct probabilistic cytoarchitectonic map of Area p32 (pACC) in the individual, single subject template of the MNI Colin 27 reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using classical histological criteria and quantitative 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 the reference space, where each voxel was assigned the probability to belong to Area p32 (pACC). The probability map of Area p32 (pACC) is provided in NifTi format for each hemisphere in the reference space. 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 updated probability estimates for new brain structures may in some cases lead to measurable but negligible deviations of existing probability maps, as compared to earlier released datasets. Other available data versions of Area p32 (pACC): Palomero-Gallagher et al. (2019) [Data set, v16.1] [DOI: 10.25493/3JX0-7E5](https://doi.org/10.25493%2F3JX0-7E5) The most probable delineation of Area p32 (pACC) 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, v1.18] [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) |