Probabilistic cytoarchitectonic map of CM (Amygdala) (v8.2)

This dataset contains the distinct architectonic CM (Amygdala)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 CM (Amygdala). The probability map of CM (Amygdala) 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 CM (Amygdala): Kedo et al. (2018) [Data set, v6.1] [DOI: 10.25493/X0CV-G7F](https://doi.org/10.25493%2FX0CV-G7F) Kedo et al. (2019) [Data set, v6.4] [DOI: 10.25493/36FR-C95](https://doi.org/10.25493%2F36FR-C95) Kedo et al. (2020) [Data set, v8.0] [DOI: 10.25493/RQEW-YTW](https://doi.org/10.25493%2FRQEW-YTW) The most probable delineation of CM (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) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0) Amunts et al. (2021) [Data set, v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)

本数据集包含适配MNI Colin 27个体单被试模板以及MNI ICBM 152 2009c非线性不对称参考空间的、具有独特构筑学特征的皮质内侧杏仁核（CM，Amygdala）。
作为尤里希脑（Julich-Brain）细胞构筑图谱的组成部分，该脑区通过对10例取自杜塞尔多夫大学遗体捐赠项目的人类尸脑的经细胞体染色组织切片开展细胞构筑分析得以定位。
随后将细胞构筑分析结果映射至上述两种参考空间，其中每个体素均被赋予其属于皮质内侧杏仁核的概率值。
针对每种脑参考空间及每个大脑半球，均提供了皮质内侧杏仁核的概率图谱，格式为NIfTI。
尤里希脑图谱依托模块化、灵活且可适配的框架，该框架包含用于生成上述脑结构概率图谱的工作流。
请注意，方法学改进以及新脑结构的整合可能会导致早期发布的数据集出现细微偏差。
皮质内侧杏仁核的其他可用数据版本包括：
Kedo等（2018）[数据集，v6.1] [DOI: 10.25493/X0CV-G7F](https://doi.org/10.25493%2FX0CV-G7F)
Kedo等（2019）[数据集，v6.4] [DOI: 10.25493/36FR-C95](https://doi.org/10.25493%2F36FR-C95)
Kedo等（2020）[数据集，v8.0] [DOI: 10.25493/RQEW-YTW](https://doi.org/10.25493%2FRQEW-YTW)
基于当前已发布的全部尤里希脑图谱脑结构的最大概率图谱计算得到的皮质内侧杏仁核最可靠边界可通过以下资源获取：
Amunts等（2019）[数据集，v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6)
Amunts等（2019）[数据集，v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR)
Amunts等（2020）[数据集，v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
Amunts等（2020）[数据集，v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9)
Amunts等（2020）[数据集，v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53)
Amunts等（2021）[数据集，v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0)
Amunts等（2021）[数据集，v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)