cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/vhfh-n2
title: Probabilistic cytoarchitectonic map of HATA (Hippocampus) (v11b.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of HATA (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 HATA (Hippocampus). The probability map of HATA (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 HATA (Hippocampus): Amunts et al. (2019) [Data set, v11.1] [DOI: 10.25493/M1XP-VSQ](https://doi.org/10.25493%2FM1XP-VSQ) The most probable delineation of HATA (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. (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/pvpp-p3q
title: Probabilistic cytoarchitectonic map of Area 4a (PreCG) (v9.4)

description: This dataset contains the distinct architectonic Area 4a (PreCG) 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 4a (PreCG). The probability map of Area 4a (PreCG) 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 4a (PreCG): Geyer et al. (2018) [Data set, v9.2] [DOI: 10.25493/5GXC-Z2U](https://doi.org/10.25493%2F5GXC-Z2U) The most probable delineation of Area 4a (PreCG) 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/x9qp-c6f
title: Probabilistic cytoarchitectonic map of Area 33 (ACC) (v16.1)

description: This dataset contains the distinct architectonic Area 33 (ACC) 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 33 (ACC). The probability map of Area 33 (ACC) 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 33 (ACC): Palomero-Gallagher et al. (2018) [Data set, v16.0] [DOI: 10.25493/S6PK-Z7E](https://doi.org/10.25493%2FS6PK-Z7E) The most probable delineation of Area 33 (ACC) 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)
DOI: 10.25493/gcaj-vdg
title: Anterograde visualization of projections from ventral orbitofrontal cortex in rat (case: F5)

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 biotinylated dextran amine (BDA) was injected into the ventral orbitofrontal cortex (VOC). 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/j3xm-1f8
title: 3D reconstructions of pyramidal neurons from the mouse cortex

description: We have produced 3D reconstructions of pyramidal neurons from the mouse somatosensory cortex across layers II-VI using Neurolucida software from 3D confocal stack of images. Data from 3D reconstructions will be useful to modeling and integration of anatomical data with functional studies in mouse brain.
DOI: 10.25493/4zfh-rpu
title: Density measurements of different receptors for DG (Hippocampus) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in DG (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_ 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/yhzn-vmv
title: Probabilistic cytoarchitectonic map of Area hOc4lp (LOC) (v5.0)

description: This dataset contains the distinct architectonic Area hOc4lp (LOC) 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 hOc4lp (LOC). The probability map of Area hOc4lp (LOC) 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 hOc4lp (LOC): Malikovic et al. (2018) [Data set, v3.2] [DOI: 10.25493/W4H2-WTM](https://doi.org/10.25493%2FW4H2-WTM) Malikovic et al. (2019) [Data set, v3.4] [DOI: 10.25493/4B87-Q8X](https://doi.org/10.25493%2F4B87-Q8X) The most probable delineation of Area hOc4lp (LOC) 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/fkah-nxt
title: DiFuMo atlas (128 dimensions)

description: DiFuMo (Dictionary of Functional Modes) 128-dimensional atlas presents a parcellation of the brain into 128 elementary structures using sparse dictionary learning. The atlas contains two main components: (1) an image of the 128 spatial components, sampled in MNI space, and (2) anatomical names for these 128 regions. It is meant to serve as an atlas to extract functional signals, eg to create image-derived phenotypes. These modes are optimized to represent well raw BOLD time series, over a with range of experimental conditions.
DOI: 10.25493/4101-1zg
title: Probabilistic cytoarchitectonic map of Area hOc6 (POS) (v7.1)

description: This dataset contains the distinct architectonic Area hOc6 (POS) 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 hOc6 (POS). The probability map of Area hOc6 (POS) 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 hOc6 (POS): Richter et al. (2019) [Data set, v7.0] [DOI: 10.25493/ZKR8-P13](https://doi.org/10.25493%2FZKR8-P13) The most probable delineation of Area hOc6 (POS) 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)
DOI: 10.25493/fmdd-smu
title: Interpolated 3D map of Area 6mp (SMA, mesial SFG) in the BigBrain

description: This dataset contains cytoarchitectonic maps of Area 6mp (SMA, mesial SFG) in the Big Brain dataset [Amunts et al. 2013]. The mappings were created using the semi-automatic method presented in Schleicher et al. 1999, based on coronal histological sections on 1 micron resolution. Mappings are available on approximately every 15-60th section of this region. They were then aligned to the corresponding sections of the 3D reconstructed Big Brain space, using the transformations used in Amunts et al. 2013, kindly provided by Claude Lepage (McGill University). From these delineations, a preliminary 3D map of Area 6mp (SMA, mesial SFG) has been created by simple interpolation of the coronal contours in the 3D anatomical space of the Big Brain. This map gives a first impression of the location of this area in the Big Brain, and can be viewed in the atlas viewer using the URL below. A full mapping of this area in every histological section using a Deep Learning approach is in progress. **Additional information:** The reference delineations used for this map are part of the work for the corresponding probabilistic map of Area 6mp (SMA, mesial SFG) of the JuBrain Cytoarchitectonic Atlas, published in: Ruan et al. (2019) [Data set, v9.0] [DOI: 10.25493/CZTA-P58](https://doi.org/10.25493%2FCZTA-P58) Ruan et al. (2019) [Data set, v9.1] [DOI: 10.25493/2E1T-47F](https://doi.org/10.25493%2F2E1T-47F) In addition, the dataset of the probabilistic cytoarchitectonic map of Area 6mp (SMA, mesial SFG) is part of the following research publication: Ruan, J., Bludau, S., Palomero-Gallagher, N., Caspers, S., Mohlberg, H., Eickhoff, S. B., Seitz, R. J., Amunts, A. (2018) Cytoarchitecture, probability maps, and functions of the human supplementary and pre-supplementary motor areas, 223(9):4169-4186. [DOI: 10.1007/s00429-018-1738-6](https://doi.org/10.1007%2Fs00429-018-1738-6)
DOI: 10.25493/1ssx-606
title: Three-dimensional topography of corticopontine projections from the rat sensorimotor cortex (S1, S2, M1)

description: 3D reconstructed point coordinate data representing the spatial relationships of rat corticopontine projections originating from the primary somatosensory cortex (S1), the secondary somatosensory cortex (S2), and the primary motor cortex (M1). The corticopontine projections were anterogradely labeled following dual tracer injections into S1 and S2 or M1. The data demonstrate that whisker-related projections from S1 and M1 are largely segregated, but at some locations adjoining and partly overlapping. Furthermore, S1 contributes significantly more corticopontine projections than M1. Projections from corresponding representations in S1 and S2 terminate in similar parts of the pontine nuclei and display considerable amounts of spatial overlap.
DOI: 10.25493/v6jm-nha
title: Density measurements of different receptors for Area ifj1 (IFS,PreCS) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of 17 receptors for classical neurotransmitters in Area ifj1 (IFS,PreCS) 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. For exemplary samples, we also provide color-coded laminar autoradiography images (ar). The autoradiography images show an exemplary density distribution of a single receptor for one laminar cross-section in a single tissue sample. Information on the used tissue samples and corresponding subjects for the receptor fingerprints and autoradiographs as well as a list of analyzed receptors accompanies the provided dataset.
DOI: 10.25493/yby9-qa
title: Metadata Schema of Common Data Elements for Traumatic Brain Injury (v5)

description: Data in the Medical Informatics Platform (MIP) resides in hospital servers and never leaves the hospital. Analyses and experiments are executed with respect to that principle while preserving patients’ anonymity making it infeasible for their identity to be inferred. Hospitals importing their data to the Medical Informatics Platform join a federation so as to run analyses on data from other hospital nodes as well. Each federation in the MIP refers to a specific Medical Condition. We present the metadata for the federation of hospitals that studies traumatic brain injury. A previous data version of Metadata Schema of CDE for TBI can be found here: Karozos et al. (2019) [Data set, v4 ] [DOI: 10.25493/2XWW-K4Y](https://kg.ebrains.eu/search/instances/Dataset/75b01958-b59a-42a1-9e1d-326532beabee)
DOI: 10.25493/xghe-8d9
title: Probability map of bundle rh_Tr-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 Triangularis 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: 10.25493/2pnk-m62
title: Probabilistic cytoarchitectonic map of Area PFt (IPL) (v9.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area PFt (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 PFt (IPL). The probability map of Area PFt (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 PFt (IPL): Caspers et al. (2019) [Data set, v9.4] [DOI: 10.25493/JGM9-ZET](https://doi.org/10.25493%2FJGM9-ZET) The most probable delineation of Area PFt (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: 10.25493/97ba-87y
title: Density measurements of different receptors for Area 9 [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in Area 9 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_ **mGluR2/3** \| **_glutamate_** \| _[<sup>3</sup>H]LY341495_ **GABA<sub>A</sub>** \| **_GABA_** \| _[<sup>3</sup>H]muscimol_ **GABA<sub>B</sub>** \| **_GABA_** \| _[<sup>3</sup>H]CGP54626_ **GABA<sub>A</sub>/BZ** \| **_GABA_** \| _[<sup>3</sup>H]flumazenil_ **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 for the receptor fingerprints, profiles and autoradiographs as well as a list of 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/c07s-0a0
title: Probability map of bundle lh_IP-MT_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 Inferior Parietal and Middle Temporal 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.