cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/3yj9-s6g
title: Probabilistic cytoarchitectonic map of Fastigial Nucleus (Cerebellum) (v6.2)

description: This dataset contains the distinct architectonic Fastigial Nucleus (Cerebellum) 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 Fastigial Nucleus (Cerebellum). The probability map of Fastigial Nucleus (Cerebellum) 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 Fastigial Nucleus (Cerebellum): Tellmann et al. (2018) [Data set, v6.0] [DOI: 10.25493/JNN1-V3Q](https://doi.org/10.25493%2FJNN1-V3Q) The most probable delineation of Fastigial Nucleus (Cerebellum) 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/qyfc-tqv
title: Probabilistic cytoarchitectonic map of Area OP9 (Frontal Operculum) (v5.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area OP9 (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 OP9 (Frontal Operculum). The probability map of Area OP9 (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 OP9 (Frontal Operculum): Saal et al. (2019) [Data set, v5.1] [DOI: 10.25493/3A30-5E4](https://doi.org/10.25493%2F3A30-5E4) The most probable delineation of Area OP9 (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. (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/rqkz-pcp
title: Maps of neurons stained with anti-Parvalbumin antibody

description: This dataset is about 3D-imaging at high resolution of parvalbumin expressing neurons of selected regions (area 17 of visual cortex) of human brain. The dataset contains the final fused volume in tiff format, the originally acquired 3D stacks, as well as stitching file generated by ZetaStitcher.
DOI: 10.25493/99hx-qb9
title: Density measurements of different receptors for Area ifs1 (IFS) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of 17 receptors for classical neurotransmitters in Area ifs1 (IFS) 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/kvx1-xek
title: Probabilistic cytoarchitectonic map of Area 6d2 (PreCG) (v5.0)

description: This dataset contains the distinct architectonic Area 6d2 (PreG) 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 6d2 (PreG). The probability map of Area 6d2 (PreG) 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 6d2 (PreCG): Sigl et al. (2019) [Data set, v4.0] [DOI: 10.25493/4MMD-HEB](https://doi.org/10.25493%2F4MMD-HEB) Sigl et al. (2019) [Data set, v4.1] [DOI: 10.25493/KXHS-N90](https://doi.org/10.25493%2FKXHS-N90) The most probable delineation of Area 6d2 (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) 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/n14d-jqt
title: Probabilistic cytoarchitectonic map of Area Fo2 (OFC) (v3.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area Fo2 (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 Fo2 (OFC). The probability map of Area Fo2 (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 Fo2 (OFC): Henssen et al. (2019) [Data set, v3.4] [DOI: 10.25493/3JB9-2V2](https://doi.org/10.25493%2F3JB9-2V2) The most probable delineation of Area Fo2 (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: 10.25493/5vpp-6by
title: Probabilistic cytoarchitectonic map of Area 33 (ACC) (v18.0)

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) Palomero-Gallagher et al. (2019) [Data set, v16.1] [DOI: 10.25493/X9QP-C6F](https://doi.org/10.25493%2FX9QP-C6F) 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) 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: 10.25493/6z1p-72j
title: Probabilistic cytoarchitectonic map of Entorhinal Cortex (v13.0)

description: This dataset contains the distinct architectonic Entorhinal Cortex 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 Entorhinal Cortex. The probability map of Entorhinal Cortex 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 Entorhinal Cortex: Amunts et al. (2018) [Data set, v11b.0] [DOI: 10.25493/373S-NVK](https://doi.org/10.25493%2F373S-NVK) Amunts et al. (2019) [Data set, v11.1] [DOI: 10.25493/KNXY-B1Z](https://doi.org/10.25493%2FKNXY-B1Z) The most probable delineation of Entorhinal Cortex 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: 10.25493/487v-4aq
title: Recordings of excitatory postsynaptic potentials from striatal neurons

description: Study and characterization of the synaptic properties of different striatal afferents. This dataset contains excitatory postsynaptic potentials recorded from striatal neurons after optogenetic activation of cortical and thalamic inputs.
DOI: 10.25493/kzna-y0s
title: AMPA receptor subunit distribution in the hippocampus of C57BL/J6 mice

description: This dataset contains tables and histograms showing the density of labeling, expressed as number of immunoparticles per µm2 for individual subunits of AMPA receptors (GluA1, GluA2, GluA3 and panAMPA (GluA1-3)) along different compartments of the somato-dendritic domains of hippocampal neurons. We used cutting-edge experimental immunolocalization approaches, the SDS freeze-fracture labelling (SDS-FRL) in the hippocampus of 8 week-old C57BL/J6 mice. The dataset also contains electron microscopic images of the labelling obtained using specific antibodies for the above mentioned receptor subunits of the different neuronal compartments. Altogether, the data shows the precise subcellular localization of the individual subunit-containing AMPA receptors providing a map along the 2-dimensional surface of hippocampal neurons.
DOI: 10.25493/1yqn-8na
title: Probabilistic cytoarchitectonic map of Area OP3 (POperc) (v12.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area OP3 (POperc) 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 OP3 (POperc). The probability map of Area OP3 (POperc) 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 OP3 (POperc): Eickhoff et al. (2018) [Data set, v9.2] [DOI: 10.25493/6PRT-2PS](https://doi.org/10.25493%2F6PRT-2PS) Eickhoff et al. (2019) [Data set, v9.4] [DOI: 10.25493/1Z8F-PX4](https://doi.org/10.25493%2F1Z8F-PX4) Eickhoff et al. (2020) [Data set, v11.0] [DOI: 10.25493/V715-BQU](https://doi.org/10.25493%2FV715-BQU) The most probable delineation of Area OP3 (POperc) 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: 10.34810/data72/342
title: 01CO_1.MR.0004.0149.2016.02.04.15.44.11.109375.67139866.IMA
DOI: 10.25493/adrk-vjp
title: Morphological reconstruction of striatal cholinergic interneuron

description: 3D reconstruction of the neuron morphology using Neurolucida (MBF Bioscience) coupled to Zeiss Axio Imager.A1.
DOI: 10.25493/5vdm-shh
title: Cortical recordings of the Fmr1KO mouse model of Fragile X syndrome during slow wave activity

description: Local field potentials were recorded in the cerebral cortex of a mouse model of Fragile X syndrome during slow wave activity under ketamine anesthesia. Different parameters of the recorded signal were compared to those of wild type littermates to assess functional biomarkers characterizing Fragile X syndrome. The approach was similar to that in previous publications of our group (Castano-Prat et al., 2017; Ruiz-Mejias et al., 2016).
DOI: 10.25493/g6cq-d4d
title: Brainwide distribution and variance of amyloid-beta deposits in tg-ArcSwe mice

description: Transgenic mice carrying the Arctic (E693G) and Swedish (KM670/6701NL) amyloid-b precursor protein (AβPP) develop amyloid-beta (Aβ) deposits in the brain that resemble Alzheimer’s disease neuropathology. The tg-ArcSwe atlas provides access to Aβx-40 immunolabeled histological section images from representative 12 month old tg-ArcSwe mice (Lord et al., Neurobiol. Aging 27:67-77, 2006), showing the morphology and spatial distribution of Aβx-40 plaque deposits across the brain. Analyses show that plaques are primarily distributed in the cerebral cortex, hippocampus, and thalamus. The average Aβ burden in the cortex and hippocampus is similar across animals, with coefficients of variance of 22% and 25%, respectively (Lillehaug et al., Neurobiol Aging 35:556-564, 2014).
DOI: 10.25493/49c3-wyt
title: Probability map of bundle lh_IP-SM_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 Supramarginal 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/nd6c-ysf
title: Probability map of bundle rh_LO-SP_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 Lateral Occipital and Superior Parietal 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/j5jr-yh0
title: Density measurements of different receptors for Area 4p (PreCG) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in Area 4p (PreCG) 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_ 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