cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/c279-428
title: Density measurements of different receptors for Area TE 2.1 (STG) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in Area TE 2.1 (STG) 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 **The receptor density data of this area were also published in:** Morosan P, Rademacher J, Palomero-Gallagher N, and Zilles K (2005) Anatomical organization of the human auditory cortex: Cytoarchitecture and transmitter receptors. In Heil P, Scheich H, Bundinger E, and König R (Eds), The Auditory Cortex - Towards a Synthesis of Human and Animal Research. (pp. 27-50) [Mahwah, New Jersey, USA: Lawrence Erlbaum Associates](https://www.taylorfrancis.com/books/e/9781135613365/chapters/10.4324%2F9781410613066-9)
DOI: 10.25493/jtzs-1tr
title: Probabilistic cytoarchitectonic map of Area 6d3 (SFS) (v5.0)

description: This dataset contains the distinct architectonic Area 6d3 (SFS) 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 6d3 (SFS). The probability map of Area 6d3 (SFS) 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 6d3 (SFS): Sigl et al. (2019) [Data set, v4.0] [DOI: 10.25493/40J8-TAK](https://doi.org/10.25493%2F40J8-TAK) Sigl et al. (2019) [Data set, v4.1] [DOI: 10.25493/NVJ5-JJ](https://doi.org/10.25493%2FNVJ5-JJ) The most probable delineation of Area 6d3 (SFS) 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: 10.25493/9m00-cjn
title: GapMap Frontal II (v10.0)

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) Amunts et al. (2020) [Data set, v9.1] [DOI: 10.25493/H54A-PEB](https://doi.org/10.25493%2FH54A-PEB) Amunts et al. (2020) [Data set, v9.2] [DOI: 10.25493/Q0MY-250](https://doi.org/10.25493%2FQ0MY-250) 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) 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/h9x7-6ks
title: Functional map of left human hippocampus

description: The hippocampus' organization is very well known from the cytoarchitecture perspective but its functional integrity is less understood in humans. In this study, left hippocampal differentiation along the longitudinal axis was defined based on task-based functional connectivity, resting-state functional connectivity, and structural covariance connectivity. The resulting hippocampal parcels were then characterized based on two multimodal functional connectivity and a quantitative analysis of associated behavioral functions. Analyzing the parcels across purely functional large-scale connectivity revealed an organization of the hippocampus that mirrored the tripartite functional differentiation with a head, body and tail subdivision. At higher subdivision levels, the hippocampus was additionally divided into robust 5 and 7 subregions along the longitudinal dimension. In contrast, the organization of the hippocampus based on structural data mirrored a hybrid like differentiation with a head parcel and two additional medial and lateral subregions in the medial-lateral dimension reminiscent of the cornu ammonis and subiculum subfields. Behavioral profiling of the functional organization indicated a behavioral gradient from anterior to posterior representing emotion-cognition and self-centric vs. world centric information processing.
DOI: 10.25493/j9t6-tx9
title: Probabilistic cytoarchitectonic map of Area hIP3 (IPS) (v8.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area hIP3 (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 hIP3 (IPS). The probability map of Area hIP3 (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 hIP3 (IPS): Scheperjans et al. (2019) [Data set, v8.4] [DOI: 10.25493/P8X0-V1G](https://doi.org/10.25493%2FP8X0-V1G) The most probable delineation of Area hIP3 (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, v1.13] [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: 10.25493/3a30-5e4
title: Probabilistic cytoarchitectonic map of Area OP9 (Frontal Operculum) (v5.1)

description: This dataset contains the distinct architectonic Area OP9 (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 OP9 (Frontal Operculum). The probability map of Area OP9 (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 OP9 (Frontal Operculum): Saal et al. (2019) [Data set, v5.0] [DOI: 10.25493/QYFC-TQV](https://doi.org/10.25493%2FQYFC-TQV) The most probable delineation of Area OP9 (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: 10.25493/1418-fkq
title: Probability map of bundle rh_IP-IT_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 Inferior Parietal and Inferior 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/f37h-8wb
title: Probabilistic cytoarchitectonic map of Area Id1 (Insula) (v13.1)

description: This dataset contains the distinct architectonic Area Id1 (Insula) 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 Id1 (Insula). The probability map of Area Id1 (Insula) 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 Id1 (Insula): Kurth et al. (2018) [Data set, v11.0] [DOI: 10.25493/KPB6-2AX](https://doi.org/10.25493%2FKPB6-2AX) 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: 10.25493/5qdp-arh
title: Density measurements of different receptors for Area PFcm (IPL) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in Area PFcm (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_** \| _[³H]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, 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/yp9k-ymw
title: E13.5 3D Edge-Aware Refined Atlas Derived from the Allen Developing Mouse Brain Atlas

description: 3D imaging necessitates corresponding 3D anatomical atlases, but generating such atlases is time consuming and challenging. Substantial efforts have generated such atlases including the Allen Developing Mouse Brain Atlas (ADMBA) but are typically limited by the process of serial 2D annotation for 3D reconstruction leading to artifacts. We have developed an automated, edge-aware atlas refinement method to curate existing atlases in a topologically consistent, 3D manner. Applying this approach to the E13.5 atlas in the ADMBA, we inferred labels for incompletely labeled areas, smoothed the labels in 3D, and realigned the labels to the underlying anatomical edges. As required, we also rotated images in 3D for midline symmetry and used the completed labels to strip regions outside of the central nervous system.
DOI: 10.25493/gqez-3nu
title: Probability map of bundle lh_Fu-LO_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 Fusiform and Lateral Occipital 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/data72/363
title: 01CO_1.MR.0004.0200.2016.02.04.15.44.11.109375.67141910.IMA
DOI: 10.25493/1j85-qsk
title: Distribution of dopamine 2 receptor positive neurons in the adolescent female mouse brain

description: High-resolution bright-field microscopy images of coronal brain sections showing dopamine 2 receptor positive neurons across the adolescent (35 days old) mouse brain. The dataset consists of an image series covering the rostral part of the brain from Drd2-EGFP mice. For each brain, every fourth section was processed by diaminobenzidine (DAB) immunohistochemistry using a polyclonal anti-GFP (RRID:AB_300798) antibody. The publication related to the dataset furthermore includes stereological counts of positive neurons in the prelimbic, infralimbic and insula cortex, as well as in dorsal and ventral striatum.
DOI: 10.25493/3dbt-0zh
title: Probability map of bundle lh_PoC-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 left hemisphere Postcentral 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: 10.25493/8b5f-gz2
title: Physiological effects of cholinergic modulation on mouse hippocampal neurons

description: As part of a detailed study of the effects of subcortical neuromodulation on hippocampal circuits, these experiments quantified the changes in neuronal properties due to the application of the cholinergic agonist carbachol in the acute hippocampal slice preparation. Whole-cell patch-clamp recordings were used to measure the response of hippocampal pyramidal cells and interneurons to step current injections of various amplitudes, both under control conditions and in the presence of carbachol in the bath. The data can be used to quantify the effects of cholinergic input on the physiological properties of hippocampal neurons, and are essential for building mechanistic models of the effects of subcortical neuromodulation on hippocampal circuit dynamics.
DOI: 10.25493/r6qb-pnr
title: Probabilistic cytoarchitectonic map of Area Id6 (Insula) (v4.0)

description: This dataset contains the distinct architectonic Area Id6 (Insula) 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 Id6 (Insula). The probability map of Area Id6 (Insula) are provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area Id6 (Insula): Quabs et al. (2019) [Data set, v3.1] [DOI: 10.25493/54HZ-KFQ](https://doi.org/10.25493%2F54HZ-KFQ) The most probable delineation of Area Id6 (Insula) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493/8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-64D) 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/cqd9-f38
title: Maps of GLUR2 receptors in human cortex

description: This dataset contains the image of spatial distribution of GLUR2 subunit in AMPA receptor in human cortex using serial two photons microscopy. The individual 3D tiles acquired with the two photon fluorescence microscope were fused together to produce the final image.
DOI: 10.25493/j8kp-fb
title: Probability map of bundle lh_IC-PrCu_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 Isthmus Cingulate and Precuneus 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.