cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/c0m6-nsy
title: Probabilistic cytoarchitectonic map of Area OP8 (Frontal Operculum) (v6.0)

description: This dataset contains the distinct architectonic Area OP8 (Frontal Operculum) in the MNI Colin 27 and MNI ICBM 152 reference spaces. As part of the Julich-Brain atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently, the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 and MNI ICBM 152 reference spaces where each voxel is assigned with the probability to belong to Area OP8 (Frontal Operculum). The probability map of Area OP8 (Frontal Operculum) is provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area OP8 (Frontal Operculum): Saal et al. (2019) [Data set, v5.0] [DOI: 10.25493/1NGE-YH3](https://doi.org/10.25493%2F1NGE-YH3) Saal et al. (2019) [Data set, v5.1] [DOI: 10.25493/NGF8-TA4](https://doi.org/10.25493%2FNGF8-TA4) The most probable delineation of Area OP8 (Frontal Operculum) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D) 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/fqe5-5qr
title: Probabilistic cytoarchitectonic map of Ventral Dentate Nucleus (Cerebellum) (v6.2)

description: This dataset contains the distinct architectonic Ventral Dentate Nucleus (Cerebellum) 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 Ventral Dentate Nucleus (Cerebellum). The probability map of Ventral Dentate Nucleus (Cerebellum) 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 Ventral Dentate Nucleus (Cerebellum): Tellmann et al. (2018) [Data set, v6.0] [DOI: 10.25493/89QC-M13](https://doi.org/10.25493%2F89QC-M13) The most probable delineation of Ventral Dentate Nucleus (Cerebellum) 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/dzwt-1t8
title: Cortical activity features in transgenic mouse models of cognitive deficits (Williams Beuren Syndrome)

description: High-density electrocorticogram recordings were performed in the cerebral cortex of ketamine-medetomidine 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. This dataset is used in the analytical workflow ["Slow Wave Analysis Pipeline (SWAP)"](https://wiki.ebrains.eu/bin/view/Collabs/slow-wave-analysis-pipeline).
DOI: 10.25493/kn7q-0mg
title: 3D reconstructions of pyramidal cells in mouse hippocampal CA1 region

description: We have produced 3D reconstructions of pyramidal cells in the mouse hippocampal CA1 region using Neurolucida software from 3D confocal stack of images. Data from 3D reconstructions will be useful to modeling and integration of anatomical data with functional studies in mouse hippocampus.
DOI: 10.25493/kg11-vqy
title: Probabilistic cytoarchitectonic map of Area PGa (IPL) (v11.0)

description: This dataset contains the distinct architectonic Area PGa (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 PGa (IPL). The probability map of Area PGa (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 PGa (IPL): Caspers et al. (2018) [Data set, v9.2] [DOI: 10.25493/T96P-05Y](https://doi.org/10.25493%2FT96P-05Y) Caspers et al. (2019) [Data set, v9.4] [DOI: 10.25493/V5HY-XTS](https://doi.org/10.25493%2FV5HY-XTS) The most probable delineation of Area PGa (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) 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: 10.25493/3nts-q0b
title: Electrophysiological recordings of striatal cholinergic interneuron

description: Membrane voltage traces recorded from somata of striatal cholinergic interneuron in response to square-pulse current injections.
DOI: 10.25493/89qc-m13
title: Probabilistic cytoarchitectonic map of Ventral Dentate Nucleus (Cerebellum) (v6.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Ventral Dentate Nucleus (Cerebellum) 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 Ventral Dentate Nucleus (Cerebellum). The probability map of Ventral Dentate Nucleus (Cerebellum) 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 Ventral Dentate Nucleus (Cerebellum): Tellmann et al. (2019) [Data set, v6.2] [DOI: 10.25493/FQE5-5QR](https://doi.org/10.25493%2FFQE5-5QR) The most probable delineation of Ventral Dentate Nucleus (Cerebellum) 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: 10.25493/3ma9-gyf
title: Right PMd subregions defined by their co-activation patterns

description: The right dorsal premotor cortex (PMd) of humans has been reported to be involved in a broad range of motor and cognitive functions. The basis of this behavioral heterogeneity was explored by performing a connectivity-based parcellation using meta-analytic approach applied to PMd coactivations. The connectivity-based parcellation (CBP) results of this study were compared to parcellations obtained through resting-state functional connectivity and probabilistic diffusion tractography. Functional connectivity profiles and behavioral decoding of the resulting PMd subregions allowed characterizing their respective behavior profile. These procedures divided the right PMd into five distinct subregions that formed a cognitive-motor gradient along a rostro-caudal axis: (i) a rostral subregion functionally connected with prefrontal cortex, which likely supports high-level cognitive processes, such as working memory (ii) a central subregion showing a mixed behavioral profile and functional connectivity to parietal regions of the dorsal attention network (iii) a caudal subregion closely integrated with the motor system (iv) a dorsal subregion, preferentially related to hand movements and connected to both cognitive and motor regions (v) a ventral subregion, whose functional profile fits the concept of an eye-movement related field. In conclusion, right PMd may be considered as a functional mosaic formed by five subregions.
DOI: 10.25493/favb-ndw
title: Probabilistic cytoarchitectonic map of DG (Hippocampus) (v13.0)

description: This dataset contains the distinct architectonic DG (Hippocampus) 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 DG (Hippocampus). The probability map of DG (Hippocampus) 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 DG (Hippocampus): Amunts et al. (2018) [Data set, v11b.0] [DOI: 10.25493/1KV8-B9G](https://doi.org/10.25493%2F1KV8-B9G) Amunts et al. (2019) [Data set, v11.1] [DOI: 10.25493/M8JP-XQT](https://doi.org/10.25493%2FM8JP-XQT) The most probable delineation of DG (Hippocampus) 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: 10.25493/9brg-m0t
title: Synapse maps - confocal

description: We have processed brain material from transgenic mice expressing the enhanced green fluorescent protein (EGFP) coupled with the synaptic protein PSD95 (EGFP95) in order to accomplish the automated counting process using 3D connected components of fluorescent puncta in stacks of confocal microscope images for the CA1 region of the hippocampal formation. Furthermore, we have developed a PSDs volume segmentation and quantification algorithm designed to be executed in a supercomputer in order to obtain results in multiple brain regions in a short time. We have currently uploaded 6 examples of the 3D automated counting process of the PSD95 protein using confocal microscopy, from the following CA1 regions: stratum oriens (SO), CA1 stratum radiatum (SR), CA1 stratum lacunosum moleculare (SLM). In addition, brain sections in the same regions and layers as those proccesed for confocal microscopy, have been processed for 3D electron microscopy using FIB/SEM in order to crossvalidate and interpret confocal data with ultrastructural data with on the number and size of PSD puncta in different brain regions. We have currently uploaded 1 example of the 3D counting process of PSD puncta using FIB/SEM from the CA1 SR region.
DOI: 10.25493/x835-sk7
title: Probabilistic cytoarchitectonic map of Area FG2 (FusG) (v1.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area FG2 (FusG) 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 FG2 (FusG). The probability map of Area FG2 (FusG) 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 FG2 (FusG): Caspers et al. (2019) [Data set, v1.4] [DOI: 10.25493/F2JH-KVV](https://doi.org/10.25493%2FF2JH-KVV) The most probable delineation of Area FG2 (FusG) 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/y7yv-6q6
title: Density measurements of different receptors for CA1 (Hippocampus) [human, v2.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters of CA1 (Hippocampus) obtained by means of quantitative _in vitro_ autoradiography. The receptor densities are visualized as _fingerprints_ (**fp**), which provide the mean density and standard deviation for each of the analyzed receptor types, averaged across samples. Overview of available measurements [ **receptor** \| **_neurotransmitter_** \| _labeling agent_ ]: **AMPA** \| **_glutamate_** \| _[<sup>3</sup>H]AMPA_ **kainate** \| **_glutamate_** \| _[<sup>3</sup>H]kainate_ **NMDA** \| **_glutamate_** \| _[<sup>3</sup>H]MK-801_ **GABA<sub>A</sub>** \| **_GABA_** \| _[<sup>3</sup>H]muscimol_ **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/mq0y-22e
title: Probabilistic cytoarchitectonic map of CA3 (Hippocampus) (v11b.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of CA3 (Hippocampus) 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 CA3 (Hippocampus). The probability map of CA3 (Hippocampus) 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 CA3 (Hippocampus): Amunts et al. (2020) [Data set, v11.1] [DOI: 10.25493/C546-GS0](https://doi.org/10.25493%2FC546-GS0) The most probable delineation of CA3 (Hippocampus) 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. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-64D)
DOI: 10.25493/8mrr-jhc
title: Probabilistic cytoarchitectonic map of Interposed Nucleus (Cerebellum) (v6.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Interposed Nucleus (Cerebellum) 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 Interposed Nucleus (Cerebellum). The probability map of Interposed Nucleus (Cerebellum) 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 Interposed Nucleus (Cerebellum): Tellmann et al. (2019) [Data set, v6.2] [DOI: 10.25493/3YJ9-S6G](https://doi.org/10.25493%2F3YJ9-S6G) The most probable delineation of Interposed Nucleus (Cerebellum) 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)