cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/1way-m3e
title: Probabilistic cytoarchitectonic map of Area TE 3 (STG) (v6.0)

description: This dataset contains the distinct architectonic Area TE 3 (STG) 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 TE 3 (STG). The probability map of Area TE 3 (STG) 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 TE 3 (STG): Morosan et al. (2018) [Data set, v5.0] [DOI: 10.25493/V09X-3EW](https://doi.org/10.25493%2FV09X-3EW) Morosan et al. (2019) [Data set, v5.1] [DOI: 10.25493/BN5J-JT8](https://doi.org/10.25493%2FBN5J-JT8) The most probable delineation of Area TE 3 (STG) 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: 10.34810/data72/365
title: 01CO_1.MR.0004.0071.2016.02.04.15.44.11.109375.67136738.IMA
DOI: 10.25493/yyt8-ft8
title: Probabilistic cytoarchitectonic map of Area hIP8 (IPS) (v7.1)

description: This dataset contains the distinct architectonic Area hIP8 (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 hIP8 (IPS). The probability map of Area hIP8 (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 hIP8 (IPS): Richter et al. (2019) [Data set, v7.0] [DOI: 10.25493/JBV5-J8J](https://doi.org/10.25493%2FJBV5-J8J) The most probable delineation of Area hIP8 (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/bhrh-s49
title: Neurotransmitter receptors and ion channels in the somatosensory 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 (GABAB receptors and mGlu5 receptors) and ion channel subtypes/subunits (GIRK2 channels, SK2 channels and Cav2.1 channels) along different compartments of the axo-somato-dendritic domains of pyramidal cells in a given cortical layer. We used cutting-edge experimental immunolocalization approaches, the SDS freeze-fracture labelling (SDS-FRL) in the somatosensory cortex 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 cortical pyramidal cells.
DOI: 10.25493/qx5q-n9d
title: Probabilistic cytoarchitectonic map of Area 7PC (SPL) (v9.0)

description: This dataset contains the distinct architectonic Area 7PC (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 7PC (SPL). The probability map of Area 7PC (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 7PC (SPL): Scheperjans et al. (2018) [Data set, v8.2] [DOI: 10.25493/27J7-2YX](https://doi.org/10.25493%2F27J7-2YX) Scheperjans et al. (2019) [Data set, v8.4] [DOI: 10.25493/Z45N-1T](https://doi.org/10.25493%2FZ45N-1T) The most probable delineation of Area 7PC (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, v2.2] [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: 10.25493/6d3w-rc
title: Laminar probe recordings in the mouse primary somatosensory and visual areas of awake and anesthetized mice

description: The goal of this experiment is to study how the activity and the connectivity between different neuronal subpopulations in the neocortex (in terms of area, layer and type of interneuron) varies as a function of brain state and sensory stimuli. The data includes electrophysiological recordings from nine mice in the mouse primary somatosensory and visual areas. These datasets comprise recordings during anesthesia (isoflurane) and awake states. In each of these states, recordings were performed under darkness and light conditions, followed by recordings with three different patterns of optogenetic stimulations. Three genetic mouse lines were used: VIP-Cre (vasoactive intestinal peptide) SST-Cre (somatostatin) and PV-Cre (parvalbumin). Viral injections were made to induce the expression of channelrhodopsin (ChR2) in the three different interneuronal subpopulations. Optogenetics was used to both identify individual neuronal subpopulations (opto-tagging) and trigger their activity to infer their role in modulating other neuronal subtypes, as a function of brain state and sensory stimulation. Recordings were performed in primary visual cortex (V1) and primary somatosensory cortex (S1HL, hindlimb area).
DOI: 10.25493/ecz3-9r0
title: Probabilistic cytoarchitectonic map of Area hOc4v (LingG) (v5.0)

description: This dataset contains the distinct architectonic Area hOc4v (LingG) 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 hOc4v (LingG). The probability map of Area hOc4v (LingG) 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 hOc4v (LingG): Rottschy et al. (2018) [Data set, v3.2] [DOI: 10.25493/E0YJ-ADE](https://doi.org/10.25493%2FE0YJ-ADE) Rottschy et al. (2018) [Data set, v3.4] [DOI: 10.25493/AASR-M8P](https://doi.org/10.25493%2FAASR-M8P) The most probable delineation of Area hOc4v (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) 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/n127-hws
title: Interpolated 3D map of Area TE 3 (STG) in the BigBrain

description: This dataset contains cytoarchitectonic maps of Area TE 3 (STG) 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 3 (STG) 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 3 (STG) of the JuBrain Cytoarchitectonic Atlas, published in: Geyer et al. (2018) [Data set] [DOI: 10.25493/V09X-3EW](https://doi.org/10.25493%2FV09X-3EW) Geyer et al. (2019) [Data set, v5.1] [DOI: 10.25493/BN5J-JT8](https://doi.org/10.25493%2FBN5J-JT8) In addition, the dataset of the probabilistic cytoarchitectonic map of Area TE 3 (STG) is part of the following research publications: Schleicher, A., Amunts, K., Geyer, S., Morosan, P., & Zilles, K. (1999). Observer-Independent Method for Microstructural Parcellation of Cerebral Cortex: A Quantitative Approach to Cytoarchitectonics. NeuroImage, 9(1), 165–177. [DOI: 10.1006/nimg.1998.0385](https://doi.org/10.1006%2Fnimg.1998.0385) Morosan, P., Schleicher, A., Amunts, K., & Zilles, K. (2005). Multimodal architectonic mapping of human superior temporal gyrus. Anatomy and Embryology, 210(5-6), 401–406. [DOI: 10.1007/s00429-005-0029-1](https://doi.org/10.1007%2Fs00429-005-0029-1)
DOI: 10.25493/jrky-264
title: Reference delineations of area TrS (Transsubiculum) in individual sections of the BigBrain

description: This dataset contains cytoarchitectonic delineations of area TrS (Transsubiculum) in the BigBrain dataset [Amunts et al. 2013]. The delineations were created after identification of the area in the histological, cell-stained sections based on the cytoarchitectonic criteria. Mappings of the TrS are available on every 15th to 60th section in both hemispheres. They were then transformed to the sections of the 3D reconstructed BigBrain space using the transformations used in Amunts et al. 2013, which were provided by Claude Lepage (McGill).
DOI: 10.25493/t88f-2p
title: Single cell transcriptomes from mouse hippocampus and cerebellum generated on the Fluidigm C1 using Smart-seq2

description: This dataset report single cell transcriptomes from mouse hippocampus and cerebellum at P40 generated on the Fluidigm C1 modified running a script to implement Smart-seq2
DOI: 10.25493/g6tj-268
title: Spontaneous Direct Current (DC) modulated activity in in vitro ferret slices

description: Transcranial direct current stimulation (tDCS) is a technique extensively used in the clinical realm to modulate cortical activity. tDCS has been suggested, among other applications, to induce suppression of epileptiform activity. However, the understanding of the mechanisms underlying tDCS neuromodulation is still limited. The goal of the present study was to explore whether DC stimulation has the potential to modulate cortical activity in vitro. We applied exogenous constant electric fields (EF) with different polarities and intensities to in vitro ferret cortical slices expressing slow oscillatory activity. The present model might be useful to better understand the neuromodulation mechanisms of tDCS in the clinical realm. The dataset contains data for three different subjects. Two of these subjects have two sessions (2 different slices) and the other just one slice.
DOI: 10.25493/kq4y-q4m
title: Probabilistic cytoarchitectonic map of Area hOc4d (Cuneus) (v2.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area hOc4d (Cuneus) 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 hOc4d (Cuneus). The probability map of Area hOc4d (Cuneus) 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 hOc4d (Cuneus): Kujovic et al. (2019) [Data set, v2.4] [DOI: 10.25493/VSK5-DET](https://doi.org/10.25493%2FVSK5-DET) The most probable delineation of Area hOc4d (Cuneus) 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/ae2s-kt6
title: Probabilistic cytoarchitectonic map of Area Id3 (Insula) (v7.1)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area Id3 (Insula) 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 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 both reference spaces, where each voxel was assigned the probability to belong to Area Id3 (Insula). The probability map of Area Id3 (Insula) 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 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. The most probable delineation of Area Id3 (Insula) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493/8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-64D)
DOI: 10.25493/j64x-vr6
title: Topography of pontocerebellar neurons projecting to posterior lobe of the rat cerebellum

description: 3D reconstructed point coordinate data representing the spatial location of the somata of pontine neurons projecting to face, forelimb, and hindlimb receiving parts of the paramedian lobe and cupula pyramidis of the rat cerebellum. The data show that somata of pontocerebellar neurons targeting to the face, forelimb and hindlimb zones of the paramedian lobule, respectively, are located in progressively more caudal parts of the pontine nuclei with little of no overlap.
DOI: 10.25493/qtc1-yxj
title: Probability map of bundle rh_PoC-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 right hemisphere Postcentral 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/msvp-h7r
title: 3D cyto- and receptor architectonic map of macaque lateral premotor area F5s

description: A multimodal analysis of the macaque motor cortex was carried out to create a 3D atlas integrating and reconciling discrepancies between previously published maps. This involved a quantitative and statistically testable approach to the analysis of cytoarchitecture in the macaque monkey frontal agranular cortex, in combination with an in vitro quantitative receptor autoradiographic analysis of the regional and laminar distribution patterns of 13 different neurotransmitter receptors. The borders of the identified areas were traced onto a reference macaque monkey brain in stereotaxic space. We provide here for the first time a map of lateral premotor area F5s of the macaque monkey brain integrating cyto- and receptor architectonic features in stereotaxic space. It constitutes a valuable resource for the analysis of functional experiments with non-human primates, and provides crucial data for modeling approaches with realistic synaptic dynamics.
DOI: 10.25493/ngqw-fqg
title: 3D cyto- and receptor architectonic map of macaque pre-supplementary motor cortex, area F6

description: A multimodal analysis of the macaque motor cortex was carried out to create a 3D atlas integrating and reconciling discrepancies between previously published maps. This involved a quantitative and statistically testable approach to the analysis of cytoarchitecture in the macaque monkey frontal agranular cortex, in combination with an in vitro quantitative receptor autoradiographic analysis of the regional and laminar distribution patterns of 13 different neurotransmitter receptors. The borders of the identified areas were traced onto a reference macaque monkey brain in stereotaxic space. We provide here for the first time a map of the pre-supplementary motor cortex (pre-SMA; area F6) of the macaque monkey brain integrating cyto- and receptor architectonic features in stereotaxic space. It constitutes a valuable resource for the analysis of functional experiments with non-human primates, and provides crucial data for modeling approaches with realistic synaptic dynamics.
DOI: 10.25493/g07q-k87
title: Recordings of excitatory postsynaptic potentials from cerebellar neurons

description: Study and characterization of the synaptic properties of different cerebellar neuronal types.