cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/vwvj-6d2
title: Probabilistic cytoarchitectonic map of Area 25 (sACC) (v18.0)

description: This dataset contains the distinct architectonic Area 25 (sACC) 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 25 (sACC). The probability map of Area 25 (sACC) are 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 25 (sACC): Palomero-Gallagher et al. (2018) [Data set, v16.0] [DOI: 10.25493/NMPJ-EU](https://doi.org/10.25493%2FNMPJ-EU) Palomero-Gallagher et al. (2019) [Data set, v16.1] [DOI: 10.25493/51AM-WN4](https://doi.org/10.25493%2F51AM-WN4) The most probable delineation of Area 25 (sACC) 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)
DOI: 10.25493/daz3-f3b
title: Neurotransmitter receptors and ion channels in the cerebellum of C57BL/J6 mice

description: This dataset contains tables and histograms showing the density, expressed as number of immunoparticles/µm2) of several neurotransmitter receptors subtypes (GABAB receptors and mGlu1A receptors) and ion channel subtypes/subunits (GIRK channels, SK channels and Cav2.1 channels) along different compartments of the axo-somato-dendritic domains of Purkinje cells. We used cutting-edge experimental immunolocalization approaches, the SDS freeze-fracture labelling (SDS-FRL) in the cerebellum of 4 week-old C57BL/J6 mice. The dataset also contains tables showing the spatial relation, expressed as the nearest neighbouring distances in nm, between receptors and effector ion channels to investigate the extent of overlap in different neuronal compartment. Finally, the dataset also contains electron microscopic images of the labelling obtained using specific antibodies for the above mentioned receptors of the different neuronal compartment. Altogether, the data shows the precise subcellular localization of the receptors providing for the first time a map along the 2-dimensional surface of Purkinje cells.
DOI: 10.25493/g279-h6v
title: Probabilistic cytoarchitectonic map of Area PFcm (IPL) (v9.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area PFcm (IPL) 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 PFcm (IPL). The probability map of Area PFcm (IPL) 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 PFcm (IPL): Caspers et al. (2019) [Data set, v9.4] [DOI: 10.25493/8DP8-8HE](https://doi.org/10.25493%2F8DP8-8HE) The most probable delineation of Area PFcm (IPL) 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)
DOI: 10.25493/c3pf-e36
title: Probabilistic map of the left long cingulate fibres (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the probability map of the left long cingulate fibres (atlas of deep white matter fibre bundles, version 2018), in the MNI ICBM152 reference brain. This bundle was identified by fibre clustering, from the tractography datasets of 78 subjects in Neurospin’s ARCHI database. The map shows the probability of finding a fibre belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibres going through.
DOI: 10.25493/h5ds-p2t
title: Probability map of bundle rh_Op-Ins_0 (atlas of superficial white matter fibre bundles, version 2018)

description: This data contains the probability map of a short fibre bundle connecting the right hemisphere Pars Opercularis and Insula regions of the Desikan-Killiany atlas, in the MNI ICBM152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fibre shape (fibre clustering), from the tractography datasets of 78 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fibre belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibres going through.
DOI: 10.25493/5psj-ent
title: Probabilistic cytoarchitectonic map of Area FG4 (FusG) (v7.0)

description: This dataset contains the distinct architectonic Area FG4 (FusG) 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 FG4 (FusG). The probability map of Area FG4 (FusG) are 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 FG4 (FusG): Lorenz et al. (2018) [Data set, v6.0] [DOI: 10.25493/6A73-MZS](https://doi.org/10.25493%2F6A73-MZS) Lorenz et al. (2019) [Data set, v6.1] [DOI: 10.25493/13RG-FYV](https://doi.org/10.25493%2F13RG-FYV) The most probable delineation of Area FG4 (FusG) 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)
DOI: 10.25493/7kj2-w9v
title: Probabilistic cytoarchitectonic map of Area Id1 (Insula) (v14.0)

description: This dataset contains the distinct architectonic Area Id1 (Insula) in the MNI Colin 27 and MNI ICBM 152 reference spaces. As part of the Julich-Brain 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. Subsequently, the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 and MNI ICBM 152 reference spaces where each voxel is assigned with the probability to belong to Area Id1 (Insula). The probability map of Area Id1 (Insula) 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 Id1 (Insula): Kurth et al. (2018) [Data set, v11.0] [DOI: 10.25493/KPB6-2AX](https://doi.org/10.25493%2FKPB6-2AX) Kurth et al. (2019) [Data set, v13.1] [DOI: 10.25493/F37H-8WB](https://doi.org/10.25493%2FF37H-8WB) The most probable delineation of Area Id1 (Insula) 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)
DOI: 10.25493/nmpj-eu
title: Probabilistic cytoarchitectonic map of Area 25 (sACC) (v16.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area 25 (sACC) 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 25 (sACC). The probability map of Area 25 (sACC) 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 25 (sACC): Palomero-Gallagher et al. (2019) [Data set, v16.1] [DOI: 10.25493/51AM-WN4](https://doi.org/10.25493%2F51AM-WN4) The most probable delineation of Area 25 (sACC) 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)
DOI: 10.25493/41ke-8ht
title: Probabilistic cytoarchitectonic map of Area OP6 (Frontal Operculum) (v2.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area OP6 (Frontal Operculum) 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 OP6 (Frontal Operculum). The probability map of Area OP6 (Frontal Operculum) 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 OP6 (Frontal Operculum): Unger et al. (2019) [Data set, v1.0] DOI:10.25493/TYC2-JAH The most probable delineation of Area OP6 (Frontal Operculum) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-64D)
DOI: 10.25493/yfq9-mcp
title: Optogenetic stimulation of the cerebellar cortex in in vivo anesthetized mice

description: This project investigates the effect of optogenetic activation of the cerebellar cortex on deep cerebellar nuclei activity. It is known the Purkinje cells provide the sole output of the cerebellar cortex, inhibiting the deep cerebellar nuclei neurons. In this project, we recorded the responses of the fastigial nucleus to sensory tactile stimulation (air puffs on the whisker pad, upper lip, or lower lip). In this project, extracellular recordings of the spiking activity of single-units in the fastigial nucleus (FN) are performed using quartz-coated electrodes. The air puff stimulation is paired with optogenetic activation of the cerebellar molecular and Purkinje cell layers.
DOI: 10.25493/c5rj-dx2
title: GapMap Frontal to Temporal (v10.0)

description: This dataset contains the “GapMap Frontal to Temporal" in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. In order to provide whole-brain coverage for the cortex within the Julich-Brain Atlas, yet uncharted parts of the frontal cortex have been combined to the brain region “GapMap Frontal to Temporal”. The distributions were modeled so that probabilistic gap maps were computed in analogy to other maps of the Julich-Brain Atlas. The probabilistic map of “GapMap Frontal to Temporal” 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. New maps are continuously replacing parts of “GapMap Frontal to Temporal” with progress in mapping. Other available data versions of Gap Frontal to Temporal: Amunts et al. (2020) [Data set, v9.0] [DOI: 10.25493/KRRN-E62](https://doi.org/10.25493%2FKRRN-E62) Amunts et al. (2020) [Data set, v9.1] [DOI: 10.25493/468P-A99](https://doi.org/10.25493%2F468P-A99) Amunts et al. (2020) [Data set, v9.2] [DOI: 10.25493/DDS9-HWT](https://doi.org/10.25493%2FDDS9-HWT) The most probable delineation of GapMap Frontal to Temporal derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-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)
DOI: 10.25493/xckh-nf9
title: GapMap Frontal II (v9.0)

description: This dataset contains the “Gap Map Frontal II” in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. In order to provide whole-brain coverage for the cortex within the Julich-Brain Atlas, yet uncharted parts of the frontal cortex have been combined to the brain region “Gap Map Frontal II”. The distributions were modeled so that probabilistic gap maps were computed in analogy to other maps of the Julich-Brain Atlas. The probabilistic map of “Gap Map Frontal II” 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. New maps are continuously replacing parts of “Gap Map Frontal II” with progress in mapping. The most probable delineation of Gap Map Frontal II derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-64D)
DOI: 10.25493/x3mg-33a
title: Probabilistic map of the left inferior fronto-occipital fasciculus (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the probability map of the left inferior fronto-occipital fasciculus (atlas of deep white matter fibre bundles, version 2018), in the MNI ICBM152 reference brain. This bundle was identified by fibre clustering, from the tractography datasets of 78 subjects in Neurospin’s ARCHI database. The map shows the probability of finding a fibre belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibres going through.
DOI: 10.25493/f26z-16p
title: Probabilistic cytoarchitectonic map of Area 7M (SPL) (v8.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area 7M (SPL) 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 7M (SPL). The probability map of Area 7M (SPL) 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 7M (SPL): Scheperjans et al. (2019) [Data set, v8.4] [DOI: 10.25493/F25F-EKW](https://doi.org/10.25493%2FF25F-EKW) The most probable delineation of Area 7M (SPL) 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)
DOI: 10.34810/data72/371
title: 01CO_1.MR.0004.0146.2016.02.04.15.44.11.109375.67139745.IMA
DOI: 10.25493/858x-ba5
title: Probabilistic map of the left fornix (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the probability map of the left fornix (atlas of deep white matter fibre bundles, version 2018), in the MNI ICBM152 reference brain. This bundle was identified by fibre clustering, from the tractography datasets of 78 subjects in Neurospin’s ARCHI database. The map shows the probability of finding a fibre belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibres going through.
DOI: 10.25493/j306-r19
title: Probabilistic map of the right long cingulate fibres (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the probability map of the right long cingulate fibres (atlas of deep white matter fibre bundles, version 2018), in the MNI ICBM152 reference brain. This bundle was identified by fibre clustering, from the tractography datasets of 78 subjects in Neurospin’s ARCHI database. The map shows the probability of finding a fibre belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibres going through.
DOI: 10.25493/68s1-9r1
title: Whole brain images of selected neuronal types

description: Spatial distribution of different cell types (parvalbumin interneurons, somatostatin interneurons, VIP interneurons and pyramidal cells) across the entire brain.
DOI: 10.25493/c004-kvd
title: Probabilistic map of the right uncinate fasciculus (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the probability map of the right uncinate fasciculus (atlas of deep white matter fibre bundles, version 2018), in the MNI ICBM152 reference brain. This bundle was identified by fibre clustering, from the tractography datasets of 78 subjects in Neurospin’s ARCHI database. The map shows the probability of finding a fibre belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibres going through.