cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/167t-527
title: Probabilistic cytoarchitectonic map of Area 6d1 (PreCG) (v5.0)

description: This dataset contains the distinct architectonic Area 6d1 (PreG) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to Area 6d1 (PreG). The probability map of Area 6d1 (PreG) are provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area 6d1 (PreCG): Sigl et al. (2019) [Data set, v4.0] [DOI: 10.25493/4JDW-STM](https://doi.org/10.25493%2F4JDW-STM) Sigl et al. (2019) [Data set, v4.1] [DOI: 10.25493/4WSQ-8FM](https://doi.org/10.25493%2F4WSQ-8FM) The most probable delineation of Area 6d1 (PreCG) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53)
DOI: 10.25493/tnez-hjq
title: Probability map of bundle rh_CMF-SF_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 Caudal Middle Frontal and Superior 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/j0fd-1m9
title: In vitro binding characterization of [³H] LY2119620 for the muscarinic cholinergic receptor type 2

description: Modern modelling and simulation approaches have been exploited to pinpoint and design more potent and specific ligands and allosteric modulators which can be used not only for research, but also for diagnostic and even therapeutic purposes. A crucial component of the ligand development program is the experimental validation of the accuracy and reliability of candidate compounds. We here present the data of the in vitro binding characterization of the novel ligand [<sup>3</sup>H] LY2119620, an allosteric modulator of the cholinergic muscarinic receptor type 2 (M<sub>2</sub>) obtained from the primary motor cortex (M1), the primary somatosensory cortex (S1), the hippocampus (Hip), the striatum (Str), and the cerebellum (Cb) of six adult rat brains. For each brain regions, the dataset comprises (i) the cross-subject average density and standard deviation of the M<sub>2</sub> receptor (in fmol/mg), as determined by labelling with [<sup>3</sup>H] LY2119620, and (ii) the corresponding dissociation constant (K<sub>D</sub>) of this labelling agent (in nM).
DOI: 10.25493/fzxb-m6s
title: Probabilistic cytoarchitectonic map of Area p32 (pACC) (v16.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area p32 (pACC) 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 p32 (pACC). The probability map of Area p32 (pACC) 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 p32 (pACC): 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)
DOI: 10.25493/s0dm-bk5
title: PCI-like measure in rodents

description: The aim of this task is to record evoked related potentials (ERPs) following a cortical perturbation, from a large number of cortical areas of rats exposed to general anaesthesia and during wakefulness. The recorded evoked cortical activity is used to quantify cortical complexity and to study its relation to (un)consciousness (Massimini et al. Science, 2005) in rodents. According to the Integrated Information Theory (IIT), any physical system generates consciousness as it is able to integrate information (Tononi BMC Neurosci , 2004; Tononi et al. Nat Rev Neurosci, 2016). In terms of brain functioning, this is reflected in the ability to support complex activity patterns that are widely distributed among interacting neuronal ensembles (integration) and differentiated in both time and space (information content). This ability of the brain can be quantified by the Perturbational Complexity Index (PCI; Casali et al. Sci. Transl. Med., 2013; Comolatti et al. Brain Stimul., 2019) and, in agreement with IIT, this index has proved able to reliably discriminate between conscious and unconscious brain states in humans (Casali et al. Sci. Transl. Med., 2013; Sarasso et al. Curr. Biol. 2015; Casarotto et al. Ann. Neurol., 2016). Since PCI quantifies the complexity of EEG activity following a cortical perturbation, it is objective and does not require any verbal interaction, allowing it to be applied also to non-human animal models. Multi-channel EEG activity is recorded by means of 16 epidural stainless-steel electrodes chronically implanted across the scalp of head-restrained rats, covering most of cortical areas. Cortical perturbation is given by brief current pulses through a bipolar tungsten electrode placed in the right secondary motor cortex. The resulting evoked potentials are recorded from each rat in 3 different days during wakefulness and during general anaesthesia induced by 3 different anaesthetics (propofol, sevoflurane, ketamine). Then, a new version of PCI (PCIST, Comolatti et al. Brain Stimul., 2019) is applied during the analysis, in order to quantify variations of cortical complexity in rodents across conditions (wakefulness and general anaesthesia).
DOI: 10.25493/7sep-p2v
title: Probabilistic cytoarchitectonic map of Ch 123 (Basal Forebrain) (v4.2)

description: This dataset contains the distinct architectonic Ch 123 (Basal Forebrain) 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 Ch 123 (Basal Forebrain). The probability map of Ch 123 (Basal Forebrain) 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 Ch 123 (Basal Forebrain): Zaborszky et al. (2018) [Data set, v4.0] [DOI: 10.25493/DE24-4FC](https://doi.org/10.25493%2FDE24-4FC) The most probable delineation of Ch 123 (Basal Forebrain) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0)
DOI: 10.25493/3fc2-r3r
title: 3-D point data representing the spatial distribution of corticopontine projections originating in the frontal 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 frontal 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 frontal corticostriatal, corticothalamic, corticocollicular, and corticopontine projections.
DOI: 10.25493/c5kw-dmh
title: Full hemisphere human immunohistochemistry for calcium binding proteins Parvalbumin, Calbindin and Calretinin - Color corrected images

description: This data set contains color-corrected images of the corresponding [raw data set](https://kg.ebrains.eu/search/live/minds/core/dataset/v1.0.0/4b8cc2b9-9839-4f41-a2f3-1e745bcc573a), generated by shifting, centering and aligning the pixel color distributions of the RGB channels, for all pixels of each included image.
DOI: 10.25493/7a09-wpp
title: Density measurements of different receptors for CA, stratum moleculare (Hippocampus) [rat, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters of CA, stratum moleculare (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: 10.25493/29fc-bce
title: Probabilistic cytoarchitectonic map of Area TE 1.1 (HESCHL) (v6.0)

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) 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) 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: 10.25493/62w8-ryf
title: Density measurements of different receptors for Area PGa (IPL) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in Area PGa (IPL) 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_ 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: 10.34810/data72/384
title: 01CO_1.MR.0004.0156.2016.02.04.15.44.11.109375.67140146.IMA
DOI: 10.25493/ak8j-wk8
title: GapMap Frontal to Occipital (v9.2)

description: This dataset contains the “GapMap Frontal to Occipital” 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 “GapMap Frontal to Occipital”. 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 “GapMap Frontal to Occipital” 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 “GapMap Frontal to Occipital” with progress in mapping. Other available data versions of Gap Map Frontal to Occipital: Amunts et al. (2020) [Data set, v9.0] [DOI: 10.25493/PBP4-HE6](https://doi.org/10.25493%2FPBP4-HE6) Amunts et al. (2020) [Data set, v9.1] [DOI: 10.25493/8EHQ-XYY](https://doi.org/10.25493%2F8EHQ-XYY) The most probable delineation of GapMap Frontal to Occipital 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)
DOI: 10.25493/dfyh-t01
title: Probability map of bundle rh_LOF-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 right hemisphere Lateral 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/gwpr-g6k
title: Probabilistic cytoarchitectonic map of IF (Amygdala) (v6.4)

description: This dataset contains the distinct architectonic Area IF (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 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 IF (Amygdala). The probability map of Area IF (Amygdala) 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. The most probable delineation of Area IF (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, v1.18] [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: 10.25493/80xh-gm8
title: Input architecture of cortical calbindin-D<sub>28K</sub> interneurons: dendritic synaptome

description: We have developed an immunohistochemistry-correlated electron microscopy (EM) method - the "mirror" technique - by which long series of EM ultrathin sections can be obtained of neurochemically identified interneurons (INs) with the preservation of high-fidelity ultrastructure. This approach allows quantitative assessment of morphometric parameters of synaptic inputs along the whole length of dendrites originating from the parent soma. Adjoining sections always have cut cell bodies which appear on the facing surface of both sections. Using the mirror-technique, in one of the sections the histochemical marker of the GABAergic subtype, calbindin was revealed. After identifying immuno-positive neurons which were cut on the surface the remaining part of the very same cell bodies were identified on the surface of the adjoining section. Here we provide quantitative data on the synaptic coverage of one dendrite and one segment belonging to one calbindin-D<sub>28K</sub> immuno-positive IN. The spatial distribution of asymmetric and symmetric synapses, surface area and volume of the presynaptic boutons, area extent of the active zones and morphometric parameters of synaptic vesicles were determined.