Description: Ultrahigh resolution 3D cytoarchitectonic map of the human amygdala created by a Deep-Learning assisted workflow (v1)

This title appears in the Scientific Report : 2022

Ultrahigh resolution 3D cytoarchitectonic map of the human amygdala created by a Deep-Learning assisted workflow (v1)

This dataset contains automatically created detailed map of 13 cytoarchitectonic subdivisions of the amygdala and 6 fiber bundles in the BigBrain dataset. The mappings were created using Deep Convolutional Neural Networks based on Schiffer et al 2021, which were trained on delineations on at least e...

Personal Name(s):	Schiffer, Christian
	Kedo, O. / Amunts, Katrin / Dickscheid, Timo
Contributing Institute:	Strukturelle und funktionelle Organisation des Gehirns; INM-1
Imprint:	EBRAINS 2022
DOI:	10.25493/TKTP-7NR
Document Type:	Dataset
Research Program:	Human Brain Project Specific Grant Agreement 3 Multilevel Brain Organization and Variability
Subject (ZB):	Neuroscience
	Publikationsportal JuSER

Please use the identifier: http://dx.doi.org/10.25493/TKTP-7NR in citations.

This dataset contains automatically created detailed map of 13 cytoarchitectonic subdivisions of the amygdala and 6 fiber bundles in the BigBrain dataset. The mappings were created using Deep Convolutional Neural Networks based on Schiffer et al 2021, which were trained on delineations on at least every 15th section created based on Kedo et al 2018. Mappings are available on every section. Their quality was observed by a trained neuroscientist to exclude sections with low quality results from further processing. Automatic mappings were transformed to the 3D reconstructed BigBrain space using transformations used in Amunts et al 2013, which were provided by Claude Lepage (McGill). Mappings on individual sections were used to assemble 3D volumes of all areas. Low quality results were replaced by interpolation between nearest neighboring sections. The volumes were then smoothed using a 3D median filter and largest connected components were identified to remove false positive results of the classification algorithm. The dataset consists of a HDF5 file containing the volume in RAS dimension ordering (20-micron isotropic resolution, dataset “volume”) and an affine transformation matrix (dataset “affine”). An additional dataset “interpolation_info” contains an integer vector with an integer value for each section which indicates if a section was replaced by interpolation due to low quality results (value 2) or not (value 1). Due to the large size of the volume, it is recommended to view the data online using the provided viewer link.