cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/aasr-m8p
title: Probabilistic cytoarchitectonic map of Area hOc4v (LingG) (v3.4)

description: This dataset contains the distinct architectonic Area hOc4v (LingG) 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 hOc4v (LingG). The probability map of Area hOc4v (LingG) 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 hOc4v (LingG): Rottschy et al. (2018) [Data set, v3.2] [DOI: 10.25493/E0YJ-ADE](https://doi.org/10.25493%2FE0YJ-ADE) The most probable delineation of Area hOc4v (LingG) 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: 10.25493/1e12-161
title: Probabilistic cytoarchitectonic map of Area STS2 (STS) (v5.0)

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) Zachlod et al. (2019) [Data set, v3.1] [DOI: 10.25493/KHY9-J3Y](https://doi.org/10.25493%2FKHY9-J3Y) The most probable delineation of Area STS2 (STS) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D) 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/qfn4-bps
title: Atlas of muscarinic M2 receptor distributions in the rat brain

description: Transmitter receptors are key molecules of signal transmission. They are heterogeneously distributed throughout the brain. Therefore, it is necessary to analyze their spatial organization and the relationship between regional and laminar receptor densities with defined anatomical structures in order to understand the anatomical and molecular organization of the brain. Quantitative in vitro receptor autoradiography is a well-established technique to demonstrate receptor binding sites at a high spatial resolution. To compensate non-linear distortions, autoradiographs visualizing the regional and laminar distribution patterns of muscarinic cholinergic M2 receptors throughout the rat brain were 3D-reconstructed and the ensuing volume was aligned with an undistorted reference volume, the common rat reference template: the Waxholm Space atlas (Papp et al, 2014). Alignment of data to the Waxholm space was done in collaboration with SP5 (Jan Bjaalie).
DOI: 10.25493/anf9-eg3
title: Cortical activity features in transgenic mouse models of cognitive deficits (Fragile X Syndrome)

description: High-density electrocorticogram recordings were performed in the cerebral cortex of isoflurane anaesthetized adult wild-type and knock out mice to characterize the dynamics of emergent slow oscillatory activity. A new analysis pipeline has been refined and improved by enrichment with novel methods and functions accounting for the fine spatial and temporal resolution of the data. The combination of high-density electrode recordings with a detailed analytical tool allows the establishment of an unprecedented correlation of slow wave dynamics between cortical areas.
DOI: 10.25493/qyp4-5vq
title: P4 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 P4 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.
DOI: 10.25493/e5e8-1vv
title: Probabilistic cytoarchitectonic map of Area hOc3v (LingG) (v3.4)

description: This dataset contains the distinct architectonic Area hOc3v (LingG) 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 hOc3v (LingG). The probability map of Area hOc3v (LingG) 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 hOc3v (LingG): Rottschy et al. (2018) [Data set, v3.2] [DOI: 10.25493/3K39-DNC](https://doi.org/10.25493%2F3K39-DNC) The most probable delineation of Area hOc3v (LingG) 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/rkz6-b3z
title: Probability map of bundle rh_RAC-SF_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 Rostral Anterior Cingulate 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/pq2r-rtc
title: Probability map of bundle lh_LOF-RMF_1 (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 Lateral Orbitofrontal and Rostral Middle 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/sh37-979
title: Probabilistic cytoarchitectonic map of Area OP1 (POperc) (v9.4)

description: This dataset contains the distinct architectonic Area OP1 (POperc) in the MNI Colin 27 and MNI ICBM 152 reference spaces. As part of the Julich-Brain 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. 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 OP1 (POperc). The probability map of Area OP1 (POperc) 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 OP1 (POperc): Eickhoff et al. (2018) [Data set, v9.2] [DOI: 10.25493/HVH9-KBR](https://doi.org/10.25493%2FHVH9-KBR) Eickhoff et al. (2020) [Data set, v11.0] [DOI: 10.25493/9SP6-8HA](https://doi.org/10.25493%2F9SP6-8HA) The most probable delineation of Area OP1 (POperc) 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/vsk5-det
title: Probabilistic cytoarchitectonic map of Area hOc4d (Cuneus) (v2.4)

description: This dataset contains the distinct architectonic Area hOc4d (Cuneus) 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 hOc4d (Cuneus). The probability map of Area hOc4d (Cuneus) 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 hOc4d (Cuneus): Kujovic et al. (2018) [Data set, v2.2] [DOI: 10.25493/KQ4Y-Q4M](https://doi.org/10.25493%2FKQ4Y-Q4M) The most probable delineation of Area hOc4d (Cuneus) 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/h57g-zk5
title: Action potential dependent sIPSCs - from juvenile (P21-30) C57Bl6/J male mice

description: A general procedure to fit individual synaptic events recorded from voltage clamp experiments was tested with various sets of experimental data on GABAergic synapses; gephyrin and gephyrin-dependent pathways were chosen as a suitable example of a kinetic model of synaptic transmission.
DOI: 10.25493/3d29-nj7
title: Probabilistic cytoarchitectonic map of Area 1 (PostCG) (v8.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area 1 (PostCG) 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 1 (PostCG). The probability map of Area 1 (PostCG) 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 1 (PostCG): Geyer et al. (2019) [Data set, v8.4] [DOI: 10.25493/THB5-B64](https://doi.org/10.25493%2FTHB5-B64) The most probable delineation of Area 1 (PostCG) 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: 10.25493/4f4s-w5a
title: Density measurements of different receptors for CA2 (Hippocampus) [human, v2.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters of CA2 (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_ **GABA<sub>B</sub>** \| **_GABA_** \| _[<sup>3</sup>H]CGP54626_ **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/z9jx-wkb
title: Probabilistic cytoarchitectonic map of Area hOc4la (LOC) (v3.4)

description: This dataset contains the distinct architectonic Area hOc4la (LOC) 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 hOc4la (LOC). The probability map of Area hOc4la (LOC) 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 hOc4la (LOC): Malikovic et al. (2018) [Data set, v3.2] [DOI: 10.25493/FCQW-EZU](https://doi.org/10.25493%2FFCQW-EZU) 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/xjr8-qca
title: Wide-field calcium imaging of the right cortical hemisphere of GCaMP6f mice at different anesthesia levels

description: Slow waves (SWs) are spatio-temporal patterns of cortical activity that occur both during natural sleep and anesthesia and are preserved across species. Even though electrophysiological recordings have been largely used to characterize brain states, they are limited in spatial resolution and cannot target specific neuronal populations. Recently, large-scale optical imaging techniques coupled with functional indicators overcame these restrictions and will give new important insight on the SW phenomenon. Here we combined wide-field fluorescence microscopy and a transgenic mouse model expressing a calcium indicator (GCaMP6f) in excitatory neurons to study SW propagation over the mesoscale under different levels of anesthesia.
DOI: 10.25493/dw5a-ymd
title: Reference delineations of area HATA (Hippocampus) in individual sections of the BigBrain

description: This dataset contains cytoarchitectonic delineations of area HATA (Hippocampus) in the BigBrain dataset [Amunts et al. 2013]. The delineations were created after identification of the area in the histological, cell-stained sections based on the cytoarchitectonic criteria. Mappings of the HATA are available on every 15th to 60th section in both hemispheres. They were then transformed to the sections of the 3D reconstructed BigBrain space using the transformations used in Amunts et al. 2013, which were provided by Claude Lepage (McGill).
DOI: 10.25493/m2pm-92q
title: Probabilistic cytoarchitectonic map of Area PFop (IPL) (v9.4)

description: This dataset contains the distinct architectonic Area PFop (IPL) 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 PFop (IPL). The probability map of Area PFop (IPL) 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 PFop (IPL): Caspers et al. (2018) [Data set, v9.2] [DOI: 10.25493/XYBW-69Q](https://doi.org/10.25493%2FXYBW-69Q) The most probable delineation of Area PFop (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/ghbt-svn
title: Densities and 3D distribution of synapses using FIB/SEM imaging in the mouse hippocampus and neocortex

description: Synapses have been identified, segmented and quantified in the adult mouse hippocampus and neocortex using 3D electron microscopy (FIB-SEM). Three animals have been used for the hippocampus (ID2, ID4 and ID28) and three animals for the neocortex (ID5, ID24 and ID25). In the hippocampus we have studied three layers in the CA1 area (stratum oriens, stratum radiatum and stratum lacunosum-moleculare). In the neocortex we have studied the following layers of the somatosensory cortex (S1HL): L1, L2, L3, L4, L5a, L5b and L6. Data obtained include the total densities of synapses, the number of excitatory (asymmetric) and inhibitory (symmetric) synapses, their sizes (measured as the Feret’s diameter and synaptic apposition surfaces) and their spatial distribution.
DOI: 10.34810/data72/358
title: 01CO_1.MR.0004.0075.2016.02.04.15.44.11.109375.67136899.IMA