cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/7t39-yke
title: Probabilistic cytoarchitectonic map of Area 44 (IFG) (v7.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area 44 (IFG) 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 44 (IFG). The probability map of Area 44 (IFG) 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 44 (IFG): Amunts et al. (2019) [Data set, v7.4] [DOI: 10.25493/F9P8-ZVW](https://doi.org/10.25493%2FF9P8-ZVW) The most probable delineation of Area 44 (IFG) 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/qn6k-chn
title: Density measurements of different receptors for Area FG1 (FusG) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in Area FG1 (FusG) 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/wd31-sea
title: Probabilistic cytoarchitectonic map of SF (Amygdala) (v6.4)

description: This dataset contains the distinct architectonic SF (Amygdala) 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 SF (Amygdala). The probability map of SF (Amygdala) 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 SF (Amygdala): Kedo et al. (2018) [Data set, v6.1] [DOI: 10.25493/BRNZ-SXW](https://doi.org/10.25493%2FBRNZ-SXW) The most probable delineation of SF (Amygdala) 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/j7n6-nfr
title: Probability map of bundle lh_MOF-ST_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 Orbitofrontal and Superior Temporal 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/n87d-f6w
title: Probabilistic cytoarchitectonic map of Area 6mp (SMA, mesial SFG) (v10.0)

description: This dataset contains the distinct architectonic Area 6mp (SMA, mesial SFG) in the MNI Colin 27 and MNI ICBM 152 reference spaces. 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. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 and MNI ICBM 152 reference spaces where each voxel is assigned with the probability to belong to Area 6mp (SMA, mesial SFG). The probability map of Area 6mp (SMA, mesial SFG) 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 6mp (SMA, mesial SFG): Ruan et al. (2019) [Data set, v9.0] [DOI: 10.25493/CZTA-P58](https://doi.org/10.25493%2FCZTA-P58) Ruan et al. (2019) [Data set, v9.1] [DOI: 10.25493/2E1T-47F](https://doi.org/10.25493%2F2E1T-47F) The most probable delineation of Area 6mp (SMA, mesial SFG) 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/y5qz-kv
title: Probabilistic cytoarchitectonic map of Area STS2 (STS) (v3.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area STS2 (STS) 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 STS2 (STS). The probability map of Area STS2 (STS) 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 STS2 (STS): Zachlod et al. (2019) [Data set, v3.1] [DOI: 10.25493/KHY9-J3Y](https://doi.org/10.25493%2FKHY9-J3Y) The most probable delineation of Area STS2 (STS) 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/mdrw-swq
title: Arc expression in resting state

description: Localization of activated cells (expressing immedate early gene Arc) in half mouse brain, normal caging.
DOI: 10.25493/1asn-j8u
title: Probabilistic cytoarchitectonic map of Area p32 (pACC) (v18.0)

description: This dataset contains the distinct architectonic Area p32 (pACC) 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 p32 (pACC). The probability map of Area p32 (pACC) 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 p32 (pACC): Palomero-Gallagher et al. (2019) [Data set, v16.0] [DOI: 10.25493/FZXB-M6S](https://doi.org/10.25493%2FFZXB-M6S) Palomero-Gallagher et al. (2019) [Data set, v16.1] [DOI: 10.25493/3JX0-7E5](https://doi.org/10.25493%2F3JX0-7E5) The most probable delineation of Area p32 (pACC) 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) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0)
DOI: 10.25493/zm3j-6c5
title: Molecular characterization of the interneurons in human temporal neocortex by two photon fluorescence microscopy (v1.1)

description: The aim of this work is to study the 3D organization of some population of interneurons within a sample of interest, previously analyzed for physiological information, using two photon fluorescence microscopy (TPFM). We exploit the high axial and radial resolution of TPFM optical sectioning (0.44 x 0.44 x 2 μm³) in combination with a protocol for tissue clearing and labeling to perform the 3D reconstruction of 300 um thick brain sections. We clear the sections with the SWITCH/TDE clearing method and label the samples with three antibodies to co-stain three different populations of inhibitory interneurons: PV (Parvalbumin), SST(Somatostatin), and VIP (Vaso Intestinal Peptide). A previous data version of “Molecular characterization of the interneurons in human temporal neocortex by two photon fluorescence microscopy” can be found here: Costantini et al. (2020) [Data set, v1.0] [DOI: 10.25493/V9GW-4JG]( https://doi.org/10.25493%2FV9GW-4JG)
DOI: 10.25493/6864-qvg
title: Longitudinal imaging of motor-evoked cortical activity before and after stroke

description: We recorded time series of calcium activity of cortical pyramidal neurons with wide-field fluorescence microscope. Increase in GCaMP6f fluorescence was indicative of an increase in neuronal activity. The mice were head restrained during the recording session. The field of view of the microscope allows to record cortical activity in the right hemisphere and in a portion of the left one while the mouse is performing a passive extension-active retraction of the left forelimb in a robotic device. The forces applied and the position of the forelimb are also recorded simultaneously. The experiments are divided in two parts: robot and control group. The robot group, consisting of 5 mice (GCaMP22, GCaMP23, GCaMP24, GCaMP25, GCaMP26), performed 4-5 days of motor training on robotic platform in healthy conditions (pre-stroke) and 20 days of motor rehabilitation starting 5 days after stroke (post-stroke). The control group, consisting of 3 mice (GCaMP27, GCaMP28, GCaMP29), performed 20 days of motor training in healthy condition.
DOI: 10.25493/aws5-mzg
title: 3D reconstruction and measurement of individual thalamocortical projection neuron axons of somatosensory and visual thalamic nuclei

description: This dataset describes quantitatively the somatodendritic and axonal structure of individual thalamocortical projection neurons in the thalamic somatic and visual sensory nuclei. The cells in the dataset were labeled using state-of the art viral vectors able to drive massive levels of expression of marker proteins in a neuron after a single transfection event, leaving the rest of the brain unlabeled. This allows the unambiguous visualization /measurement of the entire axonal tree, no matter how large or complex. They are the first visualization of these cells ever reported in mice. Experiments were conducted in vivo in wildtype C57BL76 adult animals and have a low (&lt;5%) success rate. Accurate labeling analysis is extremely labor-intensive, especially for large and widespread-axon cells. The present dataset may thus be viewed as a collection of “archetypal” reference specimens of these neuron populations. The accurate 3D data and measurement of axonal arborizations of thalamocortical neurons provide in this dataset will be re-usable by any scientist developing biologically-based models of local and long range brain circuitry.
DOI: 10.25493/w7th-n07
title: Probability map of bundle rh_PrC-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 right hemisphere Precentral 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/dfb3-spj
title: Probabilistic cytoarchitectonic map of Area hIP4 (IPS) (v7.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area hIP4 (IPS) 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 hIP4 (IPS). The probability map of Area hIP4 (IPS) 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 hIP4 (IPS): Richter et al. (2019) [Data set, v7.1] [DOI: 10.25493/TSEN-QSY](https://doi.org/10.25493%2FTSEN-QSY) The most probable delineation of Area hIP4 (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)
DOI: 10.25493/4ha3-bbe
title: Probabilistic cytoarchitectonic map of Area TE 1.1 (HESCHL) (v5.1)

description: This dataset contains the distinct architectonic Area TE 1.1 (HESCHL) 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 1.1 (HESCHL). The probability map of Area TE 1.1 (HESCHL) 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 1.1 (HESCHL): Morosan et al. (2018) [Data set, v5.0] [DOI: 10.25493/55TY-YS8](https://doi.org/10.25493%2F55TY-YS8) The most probable delineation of Area TE 1.1 (HESCHL) 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: 10.25493/57ej-01z
title: Probabilistic cytoarchitectonic map of Subiculum (Hippocampus) (v11b.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Subiculum (Hippocampus) 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 Subiculum (Hippocampus). The probability map of Subiculum (Hippocampus) 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 Subiculum (Hippocampus): Amunts et al. (2019) [Data set, v11.1] [DOI: 10.25493/QKJH-F45](https://doi.org/10.25493%2FQKJH-F45) The most probable delineation of Subiculum (Hippocampus) 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/jnfa-hdp
title: Whole cell patch-clamp recordings of cerebellar Golgi cells

description: The Golgi cells, together with granule cells and mossy fibers, form a neuronal microcircuit regulating information transfer at the cerebellum input stage. In order to further investigate the Golgi cells properties and their excitatory synapses, whole-cell patch-clamp recordings were performed on acute parasagittal cerebellar slices obtained from juvenile GlyT2-GFP mice (p16-p21). Passive Golgi cells parameters were extracted in voltage-clamp mode by analyzing current relaxation induced by step voltage changes (IV protocol). Excitatory synaptic transmission properties were investigated by electrical stimulation of the mossy fibers bundle (5 pulses at 50 Hz, EPSC protocol, voltage-clamp mode.