cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/dqne-mv3
title: GapMap Frontal I (v9.1)

description: This dataset contains the “Gap Map Frontal I” 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 I”. 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 I” 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 I” with progress in mapping. Other available data versions of Gap Map Frontal I: Amunts et al. (2020) [Data set, v9.0] [DOI: 10.25493/N6EZ-BRK](https://doi.org/10.25493%2FN6EZ-BRK) The most probable delineation of Gap Map Frontal I 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: 10.25493/69pp-n32
title: Characterization of striatal neurons in Parkinson’s disease patients and healthy individuals using scRNA-seq

description: The goal was to define the cellular subpopulation structure in the striatum of humans and the characterization of the functional and pathologic changes associated with nigro-striatal denervation and L-dopa treatment for striatal neurons and interneurons. Therefore, after isolation of NeunN-expressing nuclei from fresh-frozen striatal tissues (putamen and caudate nucleus), nuclei were used to generate single-cell transcriptomes using a commercial microwell-based assay. After library preparation and sequencing, the generated data were further processed and aligned against the reference genome. Gene expression matrixes are provided which can be further analyzed and overlaid with synaptic maps and organization, microanatomical and physiological data, and integrated for the generation of a human brain atlas.
DOI: 10.25493/v1h4-gf0
title: Probability map of bundle lh_CMF-Op_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 Pars Opercularis 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/qs7c-r31
title: Probabilistic cytoarchitectonic map of Area 7P (SPL) (v9.0)

description: This dataset contains the distinct architectonic Area 7P (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 7P (SPL). The probability map of Area 7P (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 7P (SPL): Scheperjans et al. (2018) [Data set, v8.2] [DOI: 10.25493/AHQS-ZR8](https://doi.org/10.25493%2FAHQS-ZR8) Scheperjans et al. (2019) [Data set, v8.4] [DOI: 10.25493/C3HS-8R7](https://doi.org/10.25493%2FC3HS-8R7) The most probable delineation of Area 7P (SPL) derived from the calculation of a maximum probability map of all currently released JuBrain 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/p8sd-jmh
title: Density measurements of different receptors for Area hOc1 (V1, 17, CalcS) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in Area hOc1 (V1, 17, CalcS) 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_ **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_ For exemplary samples, we also provide laminar _profiles_ (**pr**) and/or color-coded laminar _autoradiography_ images (**ar**). The density profiles provide, for a single tissue sample, an exemplary density distribution for a single receptor from the pial surface to the border between layer VI and the white matter. 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, 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: 10.25493/284b-qcd
title: Genetic variants in autistic and typically developing individuals

description: The main goal was to analyze the relationship between genotype and phenotype for individuals carrying synaptic mutations. Thus, we extracted from the PARIS cohort individuals (175 autistic individuals and 128 typically developing) all the impactful mutations that are synaptic or directly associated with autism.
DOI: 10.25493/jq8f-tnf
title: Sagittal and horizontal section images showing neuronal nuclei, calbindin and parvalbumin staining in the rat hippocampal region

description: Bright-field microscopy images of serial sagittal and horizontal (half hemisphere) brain sections showing neuronal nuclei (NeuN), calbindin and parvalbumin in an adult male Long Evans rat (case 902). The images show the detailed architecture of this complex region and enable identification of regional and subregional boundaries.
DOI: 10.25493/sggr-3gu
title: Interpolated 3D map of Area TE 1.2 (HESCHL) in the BigBrain

description: This dataset contains cytoarchitectonic maps of Area TE 1.2 (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 1.2 (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 1.2 (HESCHL) of the JuBrain Cytoarchitectonic Atlas, published in: Amunts et al. (2018) [Data set] [DOI: 10.25493/T1WA-MBT](https://doi.org/10.25493%2FT1WA-MBT) Amunts et al. (2019) [Data set, v5.1] [DOI: 10.25493/R382-617](https://doi.org/10.25493%2FR382-617) In addition, the dataset of the probabilistic cytoarchitectonic map of Area TE 1.2 (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: 10.25493/2nsz-pgw
title: Probabilistic cytoarchitectonic map of Area Fo1 (OFC) (v3.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area Fo1 (OFC) 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 Fo1 (OFC). The probability map of Area Fo1 (OFC) 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 Fo1 (OFC): Henssen et al. (2019) [Data set, v3.4] [DOI: 10.25493/H2N2-6J2](https://doi.org/10.25493%2FH2N2-6J2) The most probable delineation of Area Fo1 (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, v1.13] [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/dyxz-76u
title: Database of quantitative cellular and subcellular morphological properties from rat and mouse basal ganglia

description: Database of quantitative neuroanatomical data on the normal rat and mouse basal ganglia, derived from the literature or public repositories. The dataset consists of information related to the number and distribution of cells, synapses and dendritic spines, in addition to cell morphologies. Data are given in standard units of measurement, with standardized metadata. The dataset may be used to find, compare and select neuroanatomical information about basal ganglia regions, e.g. for use in computational models. The dataset includes: 1) .csv files for all tables in the database; 2) .csv and .xlsx files for selected data extracted from the database; 3) an empty version of the database (.accdb) and a spreadsheet (.xlsx) through which researchers can collect and / or contribute more information.
DOI: 10.34810/data72/346
title: 01CO_1.MR.0004.0215.2016.02.04.15.44.11.109375.67143536.IMA
DOI: 10.25493/tstk-akk
title: Multimodal Predictive Coding Network with WhiskEye and the NRP

description: Datasets gathered from the NeuroRobotics Platform (NRP) for use with the MultiPredNet Predictive Coding algorithm. The data is used to create useful representations of the local environment. These representations have their usefulness quantified via Representational Similarity Analysis, with Spearman Rank Correlation Coefficient used as the primary metric.
DOI: 10.25493/gp15-1ms
title: Brain-wide distribution of vesicular glutamate transporter type 3 (VGLUT3)

description: Vesicular glutamate transporters (VGLUT1-3) carry glutamate into synaptic vesicles. This dataset is a brain-wide collection of microscopic images showing the distribution of vesicular glutamate transporter 3 (VGLUT3) in the normal rodent brain, visualized by for light microscopy using immunohistochemistry using an antibody against VGLUT3.
DOI: 10.25493/xz0c-r9g
title: Neurotransmitter receptors and ion channels in the hippocampus 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 (AMPA receptors, NMDA receptors, GABAB receptors and mGlu5 receptors) and ion channel subtypes/subunits (GIRK channels, SK channels, Cav2.1 channels and Kv4 channels) along different compartments of the axo-somato-dendritic domains of hippocampal neurons. We used cutting-edge experimental immunolocalization approaches, the SDS freeze-fracture labelling (SDS-FRL) in the hippocampus 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 hippocampal neurons.
DOI: 10.25493/dy1s-edw
title: Probabilistic cytoarchitectonic map of Area PGp (IPL) (v11.0)

description: This dataset contains the distinct architectonic Area PGp (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 PGp (IPL). The probability map of Area PGp (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 PGp (IPL): Caspers et al. (2018) [Data set, v9.2] [DOI: 10.25493/V9NJ-TBQ](https://doi.org/10.25493%2FV9NJ-TBQ) 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) 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/ahqs-zr8
title: Probabilistic cytoarchitectonic map of Area 7P (SPL) (v8.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area 7P (SPL) in the individual, single subject template of the MNI Colin 27 reference space. As part of the JuBrain 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 the reference space, where each voxel was assigned the probability to belong to Area 7P (SPL). The probability map of Area 7P (SPL) is provided in NifTi format for each hemisphere in the reference space. The JuBrain 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 7P (SPL): Scheperjans et al. (2019) [Data set, v8.4] [DOI: 10.25493/C3HS-8R7](https://doi.org/10.25493%2FC3HS-8R7) The most probable delineation of Area 7P (SPL) derived from the calculation of a maximum probability map of all currently released JuBrain 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: 10.25493/4jdw-stm
title: Probabilistic cytoarchitectonic map of Area 6d1 (PreCG) (v4.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area 6d1 (PreCG) 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 6d1 (PreCG). The probability map of Area 6d1 (PreCG) 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 6d1 (PreCG): Sigl et al. (2019) [Data set, v4.1] [DOI: 10.25493/4WSQ-8FM](https://doi.org/10.25493%2F4WSQ-8FM) The most probable delineation of Area 6d1 (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)