cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/468p-a99
title: GapMap Frontal to Temporal (v9.1)

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) 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)
DOI: 10.25493/wapw-81p
title: Neuronal density in the human cerebral cortex

description: This dataset contains the neuronal density in various cytoarchitectonic areas of the human cerebral cortex. Cell density was analyzed on cell-body-stained histological sections of 3 human postmortem brains that were supplied by Dr R. Alcaraz, Forensic Pathology Service, Basque Institute of Legal Medicine, Bilbao, Spain. Brain samples were obtained following the guidelines and approval by the Institutional Ethical Committee.
DOI: 10.25493/b9m6-5g8
title: Interpolated 3D map of Area TE 1.1 (HESCHL) in the BigBrain

description: This dataset contains cytoarchitectonic maps of Area TE 1.1 (HESCHL) 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 TE 1.1 (HESCHL) 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 TE 1.1 (HESCHL) of the JuBrain Cytoarchitectonic Atlas, published in: Amunts et al. (2018) [Data set] [DOI: 10.25493/55TY-YS8](https://doi.org/10.25493%2F55TY-YS8) Amunts et al. (2019) [Data set, v5.1] [DOI: 10.25493/4HA3-BBE](https://doi.org/10.25493%2F4HA3-BBE) In addition, the dataset of the probabilistic cytoarchitectonic map of Area TE 1.1 (HESCHL) is part of the following research publications: Rademacher, J., Morosan, P., Schormann, T., Schleicher, A., Werner, C., Freund, H.-J., & Zilles, K. (2001). Probabilistic Mapping and Volume Measurement of Human Primary Auditory Cortex. NeuroImage, 13(4), 669–683. [DOI: 10.1006/nimg.2000.0714](https://doi.org/10.1006%2Fnimg.2000.0714) Morosan, P., Rademacher, J., Schleicher, A., Amunts, K., Schormann, T., & Zilles, K. (2001). Human Primary Auditory Cortex: Cytoarchitectonic Subdivisions and Mapping into a Spatial Reference System. NeuroImage, 13(4), 684–701. [DOI: 10.1006/nimg.2000.0715](https://doi.org/10.1006%2Fnimg.2000.0715)
DOI: 10.34810/data72/377
title: 01CO_1.MR.0004.0036.2016.02.04.15.44.11.109375.67135334.IMA
DOI: 10.25493/6yhv-3bt
title: Density measurements of different receptors for CA1 (Hippocampus) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in CA1 (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/v9nj-tbq
title: Probabilistic cytoarchitectonic map of Area PGp (IPL) (v9.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area PGp (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 PGp (IPL). The probability map of Area PGp (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 PGp (IPL): Caspers et al. (2019) [Data set, v9.4] [DOI: 10.25493/FPFJ-ZCD](https://doi.org/10.25493%2FFPFJ-ZCD) The most probable delineation of Area PGp (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/qdd0-apd
title: Anterograde visualization of projections from medial orbitofrontal cortex in rat (case: F18)

description: High-resolution bright-field microscopy images (44) 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 medial orbitofrontal cortex (MOC). 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/jgm9-zet
title: Probabilistic cytoarchitectonic map of Area PFt (IPL) (v9.4)

description: This dataset contains the distinct architectonic Area PFt (IPL) 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 PFt (IPL). The probability map of Area PFt (IPL) 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 PFt (IPL): Caspers et al. (2018) [Data set, v9.2] [DOI: 10.25493/2PNK-M62](https://doi.org/10.25493%2F2PNK-M62) 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/f1tj-54w
title: Probabilistic cytoarchitectonic map of Area PF (IPL) (v9.4)

description: This dataset contains the distinct architectonic Area PF (IPL) 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 PF (IPL). The probability map of Area PF (IPL) 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 PF (IPL): Caspers et al. (2018) [Data set, v9.2] [DOI: 10.25493/H0GN-SA8](https://doi.org/10.25493%2FH0GN-SA8) The most probable delineation of Area PF (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/tb94-hrk
title: Probabilistic cytoarchitectonic map of Area PFm (IPL) (v9.4)

description: This dataset contains the distinct architectonic Area PFm (IPL) 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 PFm (IPL). The probability map of Area PFm (IPL) 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 PFm (IPL): Caspers et al. (2018) [Data set, v9.2] [DOI: 10.25493/AJGE-PNH](https://doi.org/10.25493%2FAJGE-PNH) The most probable delineation of Area PFm (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/afqr-50q
title: Probabilistic cytoarchitectonic map of Area hIP6 (IPS) (v7.1)

description: This dataset contains the distinct architectonic Area hIP6 (IPS) 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 hIP6 (IPS). The probability map of Area hIP6 (IPS) 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 hIP6 (IPS): Richter et al. (2019) [Data set, v7.0] [DOI: 10.25493/SVEY-ZBS](https://doi.org/10.25493%2FSVEY-ZBS) The most probable delineation of Area hIP6 (IPS) 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/yxtz-ab5
title: GapMap Temporal to Parietal (v9.2)

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) 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)
DOI: 10.25493/chjg-7qc
title: Whole cell patch-clamp recordings of cerebellar granule cells

description: This dataset provides a characterization of the intrinsic excitability and synaptic properties of the cerebellar granule cells. Whole-cell patch-clamp recordings were performed on acute parasagittal cerebellar slices obtained from juvenile Wistar rats (p18-p24). Passive granule cells parameters were extracted in voltage-clamp mode by analyzing current relaxation induced by step voltage changes (IV protocol). Granule cells intrinsic excitability was investigated in current-clamp mode by injecting 2 s current steps (CC step protocol). Synaptic transmission properties were investigated in current clamp mode by an electrical stimulation of the mossy fibers bundle (5 pulses at 50 Hz, EPSP protocol).
DOI: 10.25493/1vza-gp3
title: Probabilistic map of the right short cingulate fibres (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the probability map of the right short 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/k0x6-kkb
title: Interpolated 3D map of Area 6d1 (PreCG) in the BigBrain

description: This dataset contains cytoarchitectonic maps of Area 6d1 (PreCG) 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 6d1 (PreCG) 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 6d1 (PreCG) of the JuBrain Cytoarchitectonic Atlas, published in: Sigl et al. (2019) [Data set, v4.0] [DOI: 10.25493/4JDW-STM](https://doi.org/10.25493%2F4JDW-STM) Sigl et al. (2019) [Data set, v4.1] [DOI: 10.25493/4WSQ-8FM](https://doi.org/10.25493%2F4WSQ-8FM)
DOI: 10.25493/emnm-b7s
title: Blood flow simulations in realistic microvascular networks of the mouse cortex

description: The data set contains various time-averaged blood flow fields in realistic microvascular networks from the mouse somatosensory cortex. The underlying numerical model accounts for the key red blood cell (RBC) related flow phenomena for microvascular flow. Besides the baseline flow field we provide the time-averaged flow field for 120 cases where a single capillary has been dilated by 10%. All results are provided for the standard numerical model and for model variations where individual or multiple RBC related flow phenomena are deactivated. The data set allows the study of topological and functional characteristics of the time-averaged flow fields of the cortical microvasculature. Moreover, it can be used to gain insight on the impact of RBCs on microvascular flow and to broaden our understanding of the role of RBCs during baseline and up-regulation. Lastly, the data enables us to quantify the impact of single capillary alterations on the perfusion in surrounding vessels, which is relevant for the role of different vessels types for functional hyperemia.
DOI: 10.25493/mate-dk4
title: Distribution of neurons expressing tyrosine hydroxylase in the human cerebral cortex

description: This dataset contains the density of tyrosine hydroxylase (TH)-immunoreactive cortical interneurons in various cytoarchitectonic areas of the human cerebral cortex. Cell density was analyzed on cell-body-stained histological sections of 2 human postmortem brains that were supplied by Dr R. Alcaraz, Forensic Pathology Service, Basque Institute of Legal Medicine, Bilbao, Spain.
DOI: 10.25493/ezpf-y21
title: GapMap Temporal to Parietal (v9.1)

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) 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)