cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/amxy-fk1
title: Density measurements of different receptors for CA1 (Hippocampus) [rat, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters of CA1 (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_** \| _[<sup>3</sup>H]AMPA_ **kainate** \| **_glutamate_** \| _[<sup>3</sup>H]kainate_ **NMDA** \| **_glutamate_** \| _[<sup>3</sup>H]MK-801_ **GABA<sub>A</sub>** \| **_GABA_** \| _[<sup>3</sup>H]muscimol_ 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: 10.25493/24vd-64k
title: Spatial distribution of pontine neurons projecting to crus IIa of the cerebellar cortex

description: 3D reconstructed point coordinate data representing the spatial location of retrogradely labelled rat pontine neurons projecting to different parts of crus I and crus IIa in the cerebellar hemisphere. The dual tracing data show that pontine neuronal cell bodies projecting to crus IIa are located centrally in the pontine nuclei, in regions receiving corticopontine projections from the S1 upper lip area, while neuronal cell bodies projecting to crus I are located more external parts of the pontine nuclei.
DOI: 10.34810/data72/373
title: 01CO_1.MR.0004.0111.2016.02.04.15.44.11.109375.67138342.IMA
DOI: 10.25493/wn7h-h6n
title: Probability map of bundle rh_PoC-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 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/khy9-j3y
title: Probabilistic cytoarchitectonic map of Area STS2 (STS) (v3.1)

description: This dataset contains the distinct architectonic Area STS2 (STS) 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 STS2 (STS). The probability map of Area STS2 (STS) 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 STS2 (STS): Zachlod et al. (2019) [Data set, v3.0] [DOI: 10.25493/Y5QZ-KV](https://doi.org/10.25493%2FY5QZ-KV) 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/nsx2-3j4
title: Morphological reconstruction of the direct-pathway striatal projection neuron expressing D1 dopamine receptor

description: Digital 3D reconstruction of the neuron morphology publicly available at NeuroMorpho.Org is further corrected to assure uniform and standardized representation in computer simulations. In particular, the neurites cut at the slice border are repaired using intact branches inside the slice. Local axon collaterals are added using other cell's reconstruction. Original reconstruction: http://neuromorpho.org/dableFiles/kellendonk/CNG%20version/WT-P270-20.CNG.swc
DOI: 10.25493/2e1t-47f
title: Probabilistic cytoarchitectonic map of Area 6mp (SMA, mesial SFG) (v9.1)

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) 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)
DOI: 10.25493/pbyc-r3b
title: Supporting data for a study towards a bio-inspired real-time neuromorphic cerebellum

description: This work presents the first simulation of a large-scale, bio-inspired cerebellum model performed on neuromorphic hardware. A network containing 97k neurons and 4.2M synapses is simulated on the SpiNNaker neuromorphic system. The same model is also simulated using NEST, a popular spiking neural network simulator using generic computational resources and double precision floating point arithmetic, as a basis for validation. Individual cell and network-level spiking activity is validated against NEST and SpiNNaker is shown to produce results in agreement with NEST. The goal after validation is understanding how to accelerate the simulation speed of the network on the SpiNNaker system. Through detailed communication profiling, peak network activity is identified as one of the main challenges for simulation speed-up. Understanding activity propagation through the network will be used to inform future development of a bespoke software solution for optimal execution of cerebellum models on the chosen many-core digital neuromorphic platform. The large ratio of granule cells to other cell types in the network results in high levels of activity converging onto few cells, with those cells having relatively larger time costs associated with the processing of communication. See also the associated GitHub repository.
DOI: 10.25493/he0s-1at
title: Probability map of bundle lh_IT-MT_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 Temporal and Middle 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: 10.25493/2ekd-2zv
title: Probability map of bundle rh_LOF-MOF_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 Orbitofrontal and Middle Orbitofrontal 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/e9ef-6sb
title: Dose-dependent effects of ketamine on spontaneous and evoked EEG activity in rats

description: This dataset contains recordings of spontaneous epidural EEG from a large number of cortical areas and evoked related potentials (ERPs) following local electrical cortical stimulation in 6 rats during deep ketamine anesthesia (keta2). These data can be used in combination with the EBRAINS dataset [PCI-like measure in rodents](https://doi.org/10.25493/S0DM-BK5), where the same rats were recorded during wakefulness (wake) and light ketamine anesthesia (keta1), to identify dose dependent effects of ketamine in cortical activity. For subject j07, the recording from keta1 condition is reported in this dataset. A grid of 16 epidural stainless-steel electrodes were chronically implanted across the scalp to record multi-channel EEG activity in head-restrained rats, while cortical stimulation was given by brief current pulses by a bipolar tungsten electrode placed in the right secondary motor cortex. Spontaneous EEG and ERPs were recorded from the 6 rats during unresponsive behaviour induced by the constant intravenous administration of ketamine.
DOI: 10.25493/h54a-peb
title: GapMap Frontal II (v9.1)

description: This dataset contains the “Gap Map Frontal II” 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 II”. 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 II” 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 II” with progress in mapping. Other available data versions of Gap Map Frontal II: Amunts et al. (2020) [Data set, v9.0] [DOI: 10.25493/XCKH-NF9](https://doi.org/10.25493%2FXCKH-NF9) The most probable delineation of Gap Map Frontal II derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-64D)
DOI: 10.25493/by7n-ba1
title: Probability map of bundle rh_Or-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 Pars Orbitalis 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: 10.25493/55ty-ys8
title: Probabilistic cytoarchitectonic map of Area TE 1.1 (HESCHL) (v5.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area TE 1.1 (HESCHL) 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 TE 1.1 (HESCHL). The probability map of Area TE 1.1 (HESCHL) 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 TE 1.1 (HESCHL): Morosan et al. (2019) [Data set, v5.1] [DOI: 10.25493/4HA3-BBE](https://doi.org/10.25493%2F4HA3-BBE) 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: 10.25493/5mws-qd3
title: Anterograde visualization of projections from ventral orbitofrontal cortex in rat (case: F11)

description: High-resolution bright-field microscopy images (44) of serial coronal brain sections showing anterogradely labeled axons in orbitofrontal cortex (OFC) in a female adult Sprague Dawley rat. The anterograde tracer Phaseolus vulgaris-leucoagglutinin (PHA-L) was injected into the ventral orbitofrontal cortex (VOC). Brain sections are 300 µm apart and 50 µm thick. The dataset contains a complete series throughout the anterior-posterior extent of the brain, contributing to visualization of a topographically organized connectivity from OFC throughout the brain, including the published data on a topographically organized projection to the parahippocampal areas.
DOI: 10.25493/v5bh-p7p
title: Maximum probability map of deep white matter fibre bundles (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the maximum probability map of deep white matter fibre bundles (atlas of deep white matter fibre bundles, version 2018), in the MNI ICBM152 reference brain. These bundles were identified by fibre clustering, from the tractography datasets of 78 subjects in Neurospin’s ARCHI database. The maximum probability corresponds to the voxels with the highest number of putative fibres going through.
DOI: 10.25493/bcsj-be9
title: 3-D point data representing the spatial distribution of corticopontine projections originating in the somatosensory cortex of transgenic mice

description: The 3-D point coordinate data represent the spatial distribution of mouse brain subcortical projections from different parts of the somatosensory cortex to the caudoputamen, thalamus, superior colliculus and pontine nuclei. The point data are derived from segmented images series programmatically downloaded from the Allen Brain Atlas Data Portal. The data are suited for 3-D visualization and comparison the spatial arrangement of somatosensory corticostriatal, corticothalamic, corticocollicular, and corticopontine projections.
DOI: 10.25493/ngf8-ta4
title: Probabilistic cytoarchitectonic map of Area OP8 (Frontal Operculum) (v5.1)

description: This dataset contains the distinct architectonic Area OP8 (Frontal Operculum) in the MNI Colin 27 and MNI ICBM 152 reference spaces. As part of the Julich-Brain 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 OP8 (Frontal Operculum). The probability map of Area OP8 (Frontal Operculum) 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 OP8 (Frontal Operculum): Saal et al. (2019) [Data set, v5.0] [DOI: 10.25493/1NGE-YH3](https://doi.org/10.25493%2F1NGE-YH3) The most probable delineation of Area OP8 (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/zq9t-n4t
title: The contribution of NMDA and AMPA receptors to persistent firing in the dorsolateral prefrontal cortex in working memory

description: In this dataset we compared the contribution of NMDA and AMPA receptors to persistent firing in the dorsolateral prefrontal cortex (dlPFC) of male macaque monkeys performing a delayed saccade to a memorized spatial location. We used iontophoresis to eject small amounts of glutamate receptor antagonists (APV for blocking NMDA-Rs, CNQX for blocking AMPA-Rs), aiming to perturb but not abolish neuronal activity. We found that both AMPA-Rs and NMDA-Rs contributed to persistent activity. Blockers of the NMDA-Rs decreased persistent firing associated with the memory of the neuron’s preferred spatial location but had comparatively little effect on the representation of the anti-preferred location. They therefore decreased the information conveyed by persistent firing about the memorized location. In contrast, AMPA-R blockers decreased activity elicited by the memory of both the preferred and anti-preferred location, with a smaller effect on the information conveyed by persistent activity. Our results provide new insights into the contribution of AMPA-Rs and NMDA-Rs to persistent activity during working memory tasks.
DOI: 10.25493/v18n-nha
title: Density measurements of different receptors for CA3 (Hippocampus) [rat, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters of CA3 (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_** \| _[<sup>3</sup>H]AMPA_ **kainate** \| **_glutamate_** \| _[<sup>3</sup>H]kainate_ **NMDA** \| **_glutamate_** \| _[<sup>3</sup>H]MK-801_ **GABA<sub>A</sub>** \| **_GABA_** \| _[<sup>3</sup>H]muscimol_ 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/h0gn-sa8
title: Probabilistic cytoarchitectonic map of Area PF (IPL) (v9.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area PF (IPL) 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 PF (IPL). The probability map of Area PF (IPL) 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 PF (IPL): Caspers et al. (2019) [Data set, v9.4] [DOI: 10.25493/F1TJ-54W](https://doi.org/10.25493%2FF1TJ-54W) The most probable delineation of Area PF (IPL) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/99vd-1ds
title: Probability map of bundle rh_PrC-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 Precentral 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: 10.34810/data72/374
title: 01CO_1.MR.0004.0238.2016.02.04.15.44.11.109375.67144458.IMA