10.25493/E7QC-B3Y
Kedo, O.
Zilles, K.
Palomero-Gallagher, N.
Bludau, S.
Amunts, K.
Probabilistic cytoarchitectonic map of LB (Amygdala) (v6.1)
Human Brain Project Neuroinformatics Platform
2018
Neuroscience
Amunts, Katrin
2018-05-22
2018-03-31
10.1007/s00429-017-1577-x
10.1007/s00429-005-0025-5
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
This dataset contains the distinct probabilistic cytoarchitectonic map of LB (Amygdala) 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 LB (Amygdala). The probability map of LB (Amygdala) 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 LB (Amygdala):
Kedo et al. (2019) [Data set, v6.4] [DOI: 10.25493/C3X0-NV3](https://doi.org/10.25493%2FC3X0-NV3)
The most probable delineation of LB (Amygdala) 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)