cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/k96x-pj5
title: Probabilistic cytoarchitectonic map of Area hOc1 (V1, 17, CalcS) (v4.0)

description: This dataset contains the distinct architectonic Area hOc1 (V1, 17, CalcS) 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 hOc1 (V1, 17, CalcS). The probability map of Area hOc1 (V1, 17, CalcS) 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 hOc1 (V1, 17, CalcS): Amunts et al. (2018) [Data set, v2.2] [DOI: 10.25493/8VRA-X28](https://doi.org/10.25493%2F8VRA-X28) Amunts et al. (2019) [Data set, v2.4] [DOI: 10.25493/MXJ6-6DH](https://doi.org/10.25493%2FMXJ6-6DH) The most probable delineation of Area hOc1 (V1, 17, CalcS) 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/zm3m-ynj
title: Topography of the complete corticopontine projection

description: 3D reconstructed point coordinate data representing the spatial location of anterogradely labeled corticopontine axonal projections originating from frontal, parietal, temporal or occipital areas of the cerebral cortex. The data show that corticopontine projections from the entire cerebral cortex are topographically organized in the pontine nuclei, following a principal map that allows fairly accurate prediction of pontine target area for any site of origin in the cerebral cortex.
DOI: 10.25493/tzby-96w
title: Density measurements of different receptors for Area 3b (PostCG) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in Area 3b (PostCG) 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
DOI: 10.25493/m8pk-c82
title: Density measurements of different receptors for DG (Hippocampus) [human, v2.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters of 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_** \| _[³H]AMPA_ **kainate** \| **_glutamate_** \| _[³H]kainate_ **NMDA** \| **_glutamate_** \| _[³H]MK-801_ **GABA<sub>A</sub>** \| **_GABA_** \| _[³H]muscimol_ **GABA<sub>B</sub>** \| **_GABA_** \| _[³H]CGP54626_ **GABA<sub>A</sub>/BZ** \| **_GABA_** \| _[³H]flumazenil_ **muscarinic M<sub>1</sub>** \| **_acetylcholine_** \| _[³H]pirenzepine_ **muscarinic M<sub>2</sub>** \| **_acetylcholine_** \| _[³H]oxotremorine-M_ **muscarinic M<sub>3</sub>** \| **_acetylcholine_** \| _[³H]4-DAMP_ **nicotinic α<sub>4</sub>β<sub>2</sub>** \| **_acetylcholine_** \| _[³H]epibatidine_ **α<sub>1</sub>** \| **_noradrenalin/norepinephrine_** \| _[³H]prazosin_ **α<sub>2</sub>** \| **_noradrenalin/norepinephrine_** \| _[³H]UK-14,304_ **5-HT<sub>1A</sub>** \| **_serotonin_** \| _[³H]8-OH-DPAT_ **5-HT<sub>2</sub>** \| **_serotonin_** \| _[³H]ketanserin_ **D<sub>1</sub>** \| **_dopamine_** \| _[³H]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: 10.25493/7zc6-kw5
title: Probabilistic cytoarchitectonic map of Area 7M (SPL) (v9.0)

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) Scheperjans et al. (2019) [Data set, v8.4] [DOI: 10.25493/F25F-EKW](https://doi.org/10.25493%2FF25F-EKW) 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) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53)
DOI: 10.25493/3k39-dnc
title: Probabilistic cytoarchitectonic map of Area hOc3v (LingG) (v3.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area hOc3v (LingG) 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 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 hOc3v (LingG). The probability map of Area hOc3v (LingG) 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 hOc3v (LingG): Rottschy et al. (2019) [Data set, v3.4] [DOI: 10.25493/E5E8-1VV](https://doi.org/10.25493%2FE5E8-1VV) The most probable delineation of Area hOc3v (LingG) 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/g3mt-nr5
title: GapMap Frontal I (v10.0)

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) Amunts et al. (2020) [Data set, v9.1] [DOI: 10.25493/DQNE-MV3](https://doi.org/10.25493%2FDQNE-MV3) Amunts et al. (2020) [Data set, v9.2] [DOI: 10.25493/K6EV-C42](https://doi.org/10.25493%2FK6EV-C42) 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) 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/pe8e-bbq
title: Probability map of bundle lh_MT-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 Middle Temporal 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: 10.25493/76f3-zmz
title: Map of neurofilament in human brain cortex

description: This dataset contains images of neurofilament in human cortex obtained using serial two-photon microscopy. The individual 3D tiles acquired with the two-photon fluorescence microscope were fused together to produce the final image.
DOI: 10.25493/nrfb-7n5
title: Densities and 3D distributions of synapses using FIB/SEM imaging in the human Hippocampus (CA1) – Extension with additional subregions

description: For this dataset synapses have been identified, segmented and quantified in the deep part of the pyramidal layer, the stratum oriens and the stratum lacunosum-moleculare of the CA1 field of the human hippocampus with 3D electron microscopy (FIB-SEM). 5 human autopsies have been used to achieve a total of 15 valid images stacks (3 stacks per case) per investigated region. Data include the number and density per volume of asymmetric and symmetric synapses, their sizes and their 3D spatial position. The distribution of their post-synaptic targets has also been determined. This dataset contains additional data related to the dataset by Dominguez-Alvaro et al. (2020; doi: 10.25493/6HRE-F2Y). Experiments were performed identically, and the data from these two dataset can be used in combination.
DOI: 10.25493/aaep-jx9
title: Probability map of bundle lh_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 left 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/a2kp-fkd
title: EEG and Morris water maze data for Cyclin D2 KO mice and wildtype mice

description: This dataset is part of a project to study the interdependent functional connection between adult neurogenesis and hippocampus-dependent memory consolidation during NREM sleep. The data in this repository contains raw data of a three-day learning session in the Morris water maze task with 6 trials per day and raw data of 2 channel EEG, 1 channel EMG recordings (one baseline recording and three 24h recordings during each learning day) for each animal. Besides the raw sleep data in EDF format TXT scoring files for each recording are provided to classify the sleep or wake stage. Overall 20 animals were used: 10 Cyclin D2-/- mice as a model with suppressed adult hippocampal neurogenesis and 10 C57BL/6 mice as control group.
DOI: 10.25493/gzw1-7r3
title: Probabilistic cytoarchitectonic map of Area Fp2 (FPole) (v2.4)

description: This dataset contains the distinct architectonic Area Fp2 (FPole) 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 Fp2 (FPole). The probability map of Area Fp2 (FPole) 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 Fp2 (FPole): Bludau et al. (2018) [Data set, v2.2] [DOI: 10.25493/26WT-E3P](https://doi.org/10.25493%2F26WT-E3P) The most probable delineation of Area Fp2 (FPole) 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/fmgj-4n3
title: Pituitary homeobox 3 tetracycline-transactivator expression: coronal sections (case 3435)

description: Bright-field microscopy images of serial coronal brain sections showing Pituitary homeobox 3 (Pitx3) promoter expression in a bigenic Pitx3-tetracycline-transactivator (tTA) mouse brain (case 3435, adult male), using a driver-reporter construct in which the Pitx3-tTA promoter regulates the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, which is visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Our data show that the Pitx3-tTA promoter is spatially restricted to the substantia nigra, ventral tegmental area, and a few other regions.
DOI: 10.25493/3jb9-2v2
title: Probabilistic cytoarchitectonic map of Area Fo2 (OFC) (v3.4)

description: This dataset contains the distinct architectonic Area Fo2 (OFC) 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 Fo2 (OFC). The probability map of Area Fo2 (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 Fo2 (OFC): Henssen et al. (2018) [Data set, v3.2] [DOI: 110.25493/N14D-JQT](https://doi.org/10.25493%2FN14D-JQT) 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/xzmq-ahf
title: Probability map of bundle rh_LOF-RMF_1 (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 Orbitofrontal 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/w2ff-02g
title: P14 3D Edge-Aware Refined Atlas Derived from the Allen Developing Mouse Brain Atlas

description: 3D imaging necessitates corresponding 3D anatomical atlases, but generating such atlases is time consuming and challenging. Substantial efforts have generated such atlases including the Allen Developing Mouse Brain Atlas (ADMBA) but are typically limited by the process of serial 2D annotation for 3D reconstruction leading to artifacts. We have developed an automated, edge-aware atlas refinement method to curate existing atlases in a topologically consistent, 3D manner. Applying this approach to the P14 atlas in the ADMBA, we inferred labels for incompletely labeled areas, smoothed the labels in 3D, and realigned the labels to the underlying anatomical edges. As required, we also rotated images in 3D for midline symmetry.
DOI: 10.25493/4af6-wsd
title: Recordings of cerebellar neuronal firing induced by currents steps

description: Study and characterization of the intrinsic excitability properties of different cerebellar neuronal types.
DOI: 10.25493/wfcz-fsn
title: Franklin & Paxinos' “The Mouse Brain in Stereotaxic Coordinates” (4th edition) spatially registered to the Allen Mouse Common Coordinate Framework

description: The dataset contains location metadata defining the spatial registration of the 4th edition of “The Mouse Brain in Stereotaxic Coordinates” (Franklin & Paxinos, 2012) to the serial two-photon tomography (STPT) volume of the Allen Mouse Common Coordinate Framework (CCF, version 3; Oh et al., 2015). The stereotaxic atlas of Franklin & Paxinos contains revised delineations of 100 coronal, 32 sagittal and 30 horizontal diagrams from young adult C57BL/6J mice. The schematic diagram images were scanned from the book version and spatially registered to the Allen Mouse CCF. The dataset consists of an .xml file with spatial metadata that can be opened in the QuickNII tool (RRID:SCR_016854) to view overlay images of the two atlases. The atlas images are not shared due to copyright restrictions, but the user can use own images together with the .xml file provided here, following the procedure provided in the accompanying data descriptor file. The dataset can be used for direct comparison of anatomical landmarks and spatial correspondences of brain region delineated in the two atlases.