cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/ka52-jaj
title: Density measurements of different receptors for dorsal part of Area V3 [macaque, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in the dorsal part of Area V3 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_ **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_ **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, 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/fcqw-ezu
title: Probabilistic cytoarchitectonic map of Area hOc4la (LOC) (v3.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area hOc4la (LOC) 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 hOc4la (LOC). The probability map of Area hOc4la (LOC) 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 hOc4la (LOC): Malikovic et al. (2019) [Data set, v3.4] [DOI: 10.25493/Z9JX-WKB](https://doi.org/10.25493%2FZ9JX-WKB) The most probable delineation of Area hOc4la (LOC) 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/w55n-xyx
title: Probability map of bundle rh_MT-SM_0 (atlas of superficial white matter fibre bundles, version 2018)

description: This data contains the probability map of a short fibre bundle connecting the right hemisphere Middle Temporal and Supramarginal regions of the Desikan-Killiany atlas, in the MNI ICBM152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fibre shape (fibre clustering), from the tractography datasets of 78 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fibre belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibres going through.
DOI: 10.25493/36yt-g89
title: Interpolated 3D map of Area STS2 (STS) in the BigBrain

description: This dataset contains cytoarchitectonic maps of Area STS2 (STS) 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 STS2 (STS) 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 STS2 (STS) of the JuBrain Cytoarchitectonic Atlas, published in: Zachlod et al. (2019) [Data set, v3.0] [DOI: 10.25493/Y5QZ-KV](https://doi.org/10.25493%2FY5QZ-KV) Zachlod et al. (2019) [Data set, v3.1] [DOI: 10.25493/KHY9-J3Y](https://doi.org/10.25493%2FKHY9-J3Y)
DOI: 10.25493/1bt9-yyd
title: Franklin & Paxinos' “The Mouse Brain in Stereotaxic Coordinates” (3rd edition) spatially registered to the Allen Mouse Common Coordinate Framework

description: The dataset contains location metadata defining the spatial registration of the 3rd edition of “The Mouse Brain in Stereotaxic Coordinates” (Franklin & Paxinos, 2008) to the serial two-photon tomography (STPT) volume of the Allen Mouse Common Coordinate Framework (CCF, version 3; Oh et al., 2015). The stereotaxic atlas of Franklin & Paxinos contains 30 new horizontal diagrams, and revised delineations of 100 coronal and 32 sagittal diagrams from young adult C57BL/6J mice. The schematic diagram images were spatially registered to the Allen Mouse CCF. The dataset consists of an .xml file with spatial metadata that can be opened in the QuickNII tool (RRID:SCR_016854) to view overlay images of the two atlases. The atlas images are not shared due to copyright restrictions, but the user can use own images together with the .xml file provided here, following the procedure provided in the accompanying data descriptor file. The dataset can be used for direct comparison of anatomical landmarks and spatial correspondences of brain region delineated in the two atlases.
DOI: 10.25493/kpb6-2ax
title: Probabilistic cytoarchitectonic map of Area Id1 (Insula) (v11.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area Id1 (Insula) 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 Id1 (Insula). The probability map of Area Id1 (Insula) 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 Id1 (Insula): Kurth et al. (2019) [Data set, v13.1] [DOI: 10.25493/F37H-8WB](https://doi.org/10.25493%2FF37H-8WB) The most probable delineation of Area Id1 (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, v1.13] [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/z6ss-wh8
title: Hierarchical nonspatial map learning in humans

description: Humans performed a computerized task. They learned to navigate a nonspatial maze by pressing buttons to move from one object to another. Stimuli were arranged in a hierarchy so that a root object led to 2 objects (top level) which led to 4 objects (middle level) which led to 8 objects (bottom or target level). The maze was learned by trial and error on 1-step trajectories and evaluated (without overt feedback) on full paths from root to target. We compared different curricula for learning the maze, which involved training on breadth-first, depth-first and random curricula.
DOI: 10.25493/4etw-9xb
title: Density measurements of different receptors for dorsal part of Area hOc3 (Cuneus) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in the dorsal part of Area hOc3 (Cuneus) 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_ **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: 10.25493/29g0-66f
title: Probabilistic cytoarchitectonic map of Area Fo4 (OFC) (v2.1)

description: This dataset contains the distinct architectonic Area Fo4 (OFC) 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 Fo4 (OFC). The probability map of Area Fo4 (OFC) are provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. The most probable delineation of Area Fo4 (OFC) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.18] [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/h38t-5c2
title: Physiological data recording of interneurons from human temporal neocortex

description: This dataset includes electrophysiological recordings from interneurons in layer 1 or layer 2/3 from human temporal cortex. Electrophysiological cell properties are determined by current injections into the cell. These data can be used to classify the interneurons into subtypes.
DOI: 10.25493/5nx4-hxr
title: AICHA: An atlas of intrinsic connectivity of homotopic areas (Version 2)

description: A functional brain ROIs atlas based on resting-state fMRI data acquired in 281 individuals. AICHA ROIs cover the whole cerebrum, each having 1) homogeneity of its constituting voxels intrinsic activity, and 2) a unique homotopic contralateral counterpart with which it has maximal intrinsic connectivity. **For methodological details, see:** Joliot, M., Jobard, G., Naveau, M., Delcroix, N., Petit, L., Zago, L., Crivello, F., Mellet, E., Mazoyer, B., Tzourio-Mazoyer, N. (2015). AICHA: An atlas of intrinsic connectivity of homotopic areas. Journal of Neuroscience Methods, 254:46-59 DOI: [10.1016/j.jneumeth.2015.07.013](https://doi.org/10.1016%2Fj.jneumeth.2015.07.013)
DOI: 10.25493/97td-3yh
title: Probabilistic cytoarchitectonic map of Area 45 (IFG) (v9.0)

description: This dataset contains the distinct architectonic Area 45 (IFG) 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 45 (IFG). The probability map of Area 45 (IFG) 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 45 (IFG): Amunts et al. (2018) [Data set, v7.2] [DOI: 10.25493/J2KZ-AZW](https://doi.org/10.25493%2FJ2KZ-AZW) Amunts et al. (2019) [Data set, v7.4] [DOI: 10.25493/MR1V-BJ3](https://doi.org/10.25493%2FMR1V-BJ3) The most probable delineation of Area 45 (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) 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: 10.25493/12x0-nby
title: Ventral midbrain electrical microstimulation induces perceptual learning in primates

description: Practice improves perception and enhances neural representations of trained visual stimuli, a phenomenon known as visual perceptual learning (VPL). While attention to task-relevant stimuli plays an important role in such learning, Pavlovian stimulus-reinforcer associations are sufficient to drive VPL, even subconsciously. It has been proposed that reinforcement facilitates perceptual learning through the activation of neuromodulatory centers, but this has not been directly confirmed in primates. Here, we paired task-irrelevant visual stimuli with microstimulation of a dopaminergic center, the ventral tegmental area (VTA), in macaques. Pairing VTA microstimulation with a task-irrelevant visual stimulus increased fMRI activity and improved classification of fMRI activity patterns selectively for the microstimulation-paired stimulus. Moreover, pairing VTA microstimulation with a task-irrelevant visual stimulus improved the subject’s capacity to discriminate that stimulus. This is the first causal demonstration of the role of neuromodulatory centers in VPL in primates.
DOI: 10.25493/dqj7-kc8
title: Density measurements of different receptors for Area 7A (SPL) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in Area 7A (SPL) 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/q0hk-506
title: All-atom Molecular Dynamics Simulation of Human M2 muscarinic acetylcholine receptor in complex with an agonist and antagonist

description: The provided data concern two 500 ns long full atomistic molecular dynamics simulations of the human M2 muscarinic acetylcholine receptor in complex with one of its agonists and antagonist. In both of them the receptor is embedded in bi-layer phospholipid membrane whose composition mimics the neuronal one. The simulation box is filled with water molecules and the amount of Sodium and Chloride ions needed to neutralize the system and setting the ion concentration to a physiological condition. The simulation has been performed at 300K after 50 ns equilibration. The provided data can be employed to investigate the dynamics of pharmacological relevant trans-membrane receptor under physiological conditions.
DOI: 10.34810/data72/351
title: 01CO_1.MR.0004.0121.2016.02.04.15.44.11.109375.67138743.IMA