cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/88qg-jms
title: Probabilistic cytoarchitectonic map of Area Id7 (Insula) (v6.1)

description: This dataset contains the distinct architectonic Area Id7 (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 Id7 (Insula). The probability map of Area Id7 (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 Id7 (Insula): Amunts et al. (2019) [Data set, v6.0] [DOI: 10.25493/B2E3-JQR](https://doi.org/10.25493%2FB2E3-JQR) The most probable delineation of Area Id7 (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, v1.18] [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/rng0-92j
title: Probabilistic cytoarchitectonic map of CA2 (Hippocampus) (v13.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of CA2 (Hippocampus) 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 CA2 (Hippocampus). The probability map of CA2 (Hippocampus) 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 CA2 (Hippocampus): Amunts et al. (2018) [Data set, v11b.0] [DOI: 10.25493/K236-R0V](https://doi.org/10.25493/K236-R0V) Palomero-Gallagher et al. (2020) [Data set, v11.1][DOI: 10.25493/46G6-T33](https://doi.org/10.25493%2F46G6-T33) The most probable delineation of CA2 (Hippocampus) 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. (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/fssr-5p3
title: Probability map of bundle lh_CMF-RMF_0 (atlas of superficial white matter fibre bundles, version 2018)

description: This data contains the probability map of a short fibre bundle connecting the left hemisphere Caudal Middle Frontal and Rostral Middle Frontal 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: 10.25493/6v9e-6zf
title: Probabilistic cytoarchitectonic map of Area 3a (PostCG) (v10.0)

description: This dataset contains the distinct architectonic Area 3a (PostCG) 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 3a (PostCG). The probability map of Area 3a (PostCG) 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 3a (PostCG): Geyer et al. (2018) [Data set, v8.2] [DOI: 10.25493/R19N-B8E](https://doi.org/10.25493%2FR19N-B8E) Geyer et al. (2019) [Data set, v8.4] [DOI: 10.25493/C5QQ-EFB](https://doi.org/10.25493%2FC5QQ-EFB) The most probable delineation of Area 3a (PostCG) 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: 10.25493/9ddz-sjp
title: Density measurements of different receptors for stratum cellulare of CA (Hippocampus) [human, v2.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in stratum cellulare of CA (Hippocampus) obtained by means of quantitative _in vitro_ autoradiography. The receptor densities are visualized as _fingerprints_ (**fp**), which provide the mean density and standard deviation for each of the analyzed receptor types, averaged across samples. Overview of available measurements [ **receptor** \| **_neurotransmitter_** \| _labeling agent_ ]: **AMPA** \| **_glutamate_** \| _[<sup>3</sup>H]AMPA_ **kainate** \| **_glutamate_** \| _[<sup>3</sup>H]kainate_ **NMDA** \| **_glutamate_** \| _[<sup>3</sup>H]MK-801_ **GABA<sub>A</sub>** \| **_GABA_** \| _[<sup>3</sup>H]muscimol_ **GABA<sub>B</sub>** \| **_GABA_** \| _[<sup>3</sup>H]CGP54626_ **muscarinic M<sub>1</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]pirenzepine_ **muscarinic M<sub>2</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]oxotremorine-M_ **muscarinic M<sub>3</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]4-DAMP_ **nicotinic α<sub>4</sub>β<sub>2</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]epibatidine_ **α<sub>1</sub>** \| **_noradrenalin/norepinephrine_** \| _[<sup>3</sup>H]prazosin_ **α<sub>2</sub>** \| **_noradrenalin/norepinephrine_** \| _[<sup>3</sup>H]UK-14,304_ **5-HT<sub>1A</sub>** \| **_serotonin_** \| _[<sup>3</sup>H]8-OH-DPAT_ **5-HT<sub>2</sub>** \| **_serotonin_** \| _[<sup>3</sup>H]ketanserin_ **D<sub>1</sub>** \| **_dopamine_** \| _[<sup>3</sup>H]SCH23390_ Information on the used tissue samples and corresponding subjects, as well as analyzed receptors accompanies the provided dataset. **For methodological details, see:** Zilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press. Palomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387
DOI: 10.25493/xet5-3es
title: Anterograde visualization of projections from lateral orbitofrontal cortex in rat (case: F13)

description: High-resolution bright-field microscopy images (43) of serial coronal brain sections showing anterogradely labeled axons in orbitofrontal cortex (OFC) in a female adult Sprague Dawley rat. The anterograde tracer Phaseolus vulgaris-leucoagglutinin (PHA-L) was injected into the lateral orbitofrontal cortex (LOC). Brain sections are 300 µm apart and 50 µm thick. The dataset contains a complete series throughout the anterior-posterior extent of the brain, contributing to visualization of a topographically organized connectivity from OFC throughout the brain, including the published data on a topographically organized projection to the parahippocampal areas.
DOI: 10.25493/kx2g-ke0
title: Probability map of bundle lh_PoC-PrC_0 (atlas of superficial white matter fibre bundles, version 2018)

description: This data contains the probability map of a short fibre bundle connecting the left hemisphere Postcentral and Precentral 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/j2k4-8hf
title: Probability map of bundle lh_PrC-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 left hemisphere Precentral 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/x499-rbu
title: Probabilistic cytoarchitectonic map of Area Id3 (Insula) (v8.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area Id3 (Insula) 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 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 both reference spaces, where each voxel was assigned the probability to belong to Area Id3 (Insula). The probability map of Area Id3 (Insula) 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 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 Id3 (Insula): Quabs et al. (2019) [Data set, v7.1] [DOI: 10.25493/AE2S-KT6](https://doi.org/10.25493%2FAE2S-KT6) The most probable delineation of Area Id3 (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, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493/8EGG-ZAR) 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: 10.25493/2j43-ht2
title: Probabilistic cytoarchitectonic map of Area Fo6 (OFC) (v3.0)

description: This dataset contains the distinct architectonic Area Fo6 (OFC) 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 Fo6 (OFC). The probability map of Area Fo6 (OFC) 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 Fo6 (OFC): Wojtasik et al. (2019) [Data set, v2.1] [DOI: 10.25493/34Q4-H62](https://doi.org/10.25493%2F34Q4-H62) The most probable delineation of Area Fo6 (OFC) 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/10.25493/8EGG-ZAR) 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/a0ge-1yj
title: GapMap Temporal to Parietal (v10.0)

description: This dataset contains the “GapMap Temporal to Parietal" 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 Temporal to Parietal”. 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 Temporal to Parietal” 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 Temporal to Parietal” with progress in mapping. Other available data versions of Gap Map Temporal to Parietal: Amunts et al. (2020) [Data set, v9.0] [DOI: 10.25493/96J9-BVV](https://doi.org/10.25493%2F96J9-BVV) Amunts et al. (2020) [Data set, v9.1] [DOI: 10.25493/EZPF-Y21](https://doi.org/10.25493%2FEZPF-Y21) Amunts et al. (2020) [Data set, v9.2] [DOI: 10.25493/YXTZ-AB5](https://doi.org/10.25493%2FYXTZ-AB5) The most probable delineation of GapMap Temporal to Parietal 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: 10.34810/data72/383
title: 01CO_1.MR.0004.0096.2016.02.04.15.44.11.109375.67137741.IMA
DOI: 10.25493/jqh3-0a4
title: Recordings of spontaneous firing of cerebellar interneurons (Golgi cells)

description: Study and characterization of the intrinsic properties of Golgi cells.
DOI: 10.25493/3fnq-5kg
title: Morphological reconstructions of the striatal fast-spiking cells

description: Digital 3D reconstruction of the neuron morphology. Original reconstructions, publicly available at NeuroMorpho.Org, are further corrected to assure uniform and standardized representation in computer simulations. In particular, the neurites, which were cut at the slice border, are repaired using intact branches inside the slice.
DOI: 10.25493/6ded-7ar
title: Probabilistic cytoarchitectonic map of Area TE 2.2 (STG) (v6.0)

description: This dataset contains the distinct architectonic Area TE 2.2 (STG) 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 TE 2.2 (STG). The probability map of Area TE 2.2 (STG) 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 TE 2.2 (STG): Zachlod et al. (2019) [Data set, v5.1] [DOI: 10.25493/RTTN-R5F](https://doi.org/10.25493%2FRTTN-R5F) The most probable delineation of Area TE 2.2 (STG) 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/f25f-ekw
title: Probabilistic cytoarchitectonic map of Area 7M (SPL) (v8.4)

description: This dataset contains the distinct architectonic Area 7M (SPL) 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 7M (SPL). The probability map of Area 7M (SPL) 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 7M (SPL): Scheperjans et al. (2018) [Data set, v8.2] [DOI: 10.25493/F26Z-16P](https://doi.org/10.25493%2FF26Z-16P) 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)
DOI: 10.25493/fdge-pqw
title: Probabilistic cytoarchitectonic map of Area Fo4 (OFC) (v3.0)

description: This dataset contains the distinct architectonic Area Fo4 (OFC) 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 Fo4 (OFC). The probability map of Area Fo4 (OFC) 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 Fo4 (OFC): Wojtasik et al. (2019) [Data set, v2.1] [DOI: 10.25493/29G0-66F](https://doi.org/10.25493%2F29G0-66F) The most probable delineation of Area Fo4 (OFC) 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) 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)