cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/8egg-zar
title: Whole-brain parcellation of the Julich-Brain Cytoarchitectonic Atlas (v1.18)

description: This dataset contains the whole-brain parcellation of the Julich-Brain Cytoarchitectonic Atlas ([Amunts and Zilles, 2015](https://www.sciencedirect.com/science/article/pii/S0896627315010727?via%3Dihub)) in the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. The parcellation is derived from the individual probability maps (PMs) of the cytoarchitectonic regions released in the Julich-Brain Cytoarchitectonic Atlas, that are further combined into a Maximum Probability Map (MPM). The MPM is calculated by considering for each voxel the probability of all cytoarchitectonic areas released in the atlas, and determining the most probable assignment ([Eickhoff 2005](https://www.sciencedirect.com/science/article/abs/pii/S105381190400792X?via%3Dihub)). Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.
DOI: 10.25493/vb20-459
title: Probability map of bundle rh_Op-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 Pars Opercularis 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: 10.25493/8cqn-y8s
title: Test of consciousness metrics in rodents

description: The aim of this task is to test and compare proposed measures of level of consciousness based on spontaneous EEG activity. These measures include metrics of signal diversity like Lempel Ziv complexity, amplitude coalition entropy, synchrony coalition entropy (Schartner et al., 2015) and metrics of integrated information, like Phi* and PhiG from the practical Phi toolbox published in https://figshare.com/articles/phi_toolbox_zip/3203326 Data report multichannel spontaneous EEG activity (16 epidural electrodes covering most of the cortex) from rats during wakefulness and during general anaesthesia induced by 3 different anaesthetics (propofol, sevoflurane, ketamine). The reported spontaneous data preceded an electrical stimulation to right secondary motor cortex used to induce evoked responses with the purpose to calculate the “Perturbational Complexity Index state transition”, another promising consciousness measure based on perturbed activation (Comolatti et al., 2019). This dataset (in conjunction with dataset at DOI: 10.25493/S0DM-BK5) allows comparison between proposed consciousness measures based on spontaneous activity and metric based on evoked activity from the same rats and conditions.
DOI: 10.34810/data72/352
title: 01CO_1.MR.0004.0231.2016.02.04.15.44.11.109375.67144177.IMA
DOI: 10.25493/eea7-68f
title: Probabilistic map of the posterior segment of the right arcuate fasciculus (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the probability map of the posterior segment of the right arcuate fasciculus (atlas of deep white matter fibre bundles, version 2018), in the MNI ICBM152 reference brain. This bundle was identified by fibre clustering, from the tractography datasets of 78 subjects in 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: 10.25493/9ry4-j8f
title: Probabilistic cytoarchitectonic map of Area 5M (SPL) (v9.0)

description: This dataset contains the distinct architectonic Area 5M (SPL) 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 5M (SPL). The probability map of Area 5M (SPL) 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 5M (SPL): Scheperjans et al. (2018) [Data set, v8.2] [DOI: 10.25493/CG41-Q6U](https://doi.org/10.25493%2FCG41-Q6U) Scheperjans et al. (2019) [Data set, v8.4] [DOI: 10.25493/Y12F-YMU](https://doi.org/10.25493%2FY12F-YMU) The most probable delineation of Area 5M (SPL) 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: 10.25493/9375-55v
title: Probabilistic cytoarchitectonic map of MF (Amygdala) (v6.4)

description: This dataset contains the distinct architectonic MF (Amygdala) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to MF (Amygdala). The probability map of MF (Amygdala) are provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. The most probable delineation of MF (Amygdala) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/80xp-bvh
title: Probability map of bundle rh_PrC-SP_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 Precentral and Superior Parietal 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/f6df-h8p
title: Probabilistic cytoarchitectonic map of Area STS1 (STS) (v3.1)

description: This dataset contains the distinct architectonic Area STS1 (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 STS1 (STS). The probability map of Area STS1 (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 STS1 (STS): Zachlod et al. (2019) [Data set, v3.0] [DOI: 10.25493/2G11-1WA](https://doi.org/10.25493%2F2G11-1WA) The most probable delineation of Area STS1 (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)
DOI: 10.25493/hhfq-86b
title: Probabilistic cytoarchitectonic map of Area Fo3 (OFC) (v5.0)

description: This dataset contains the distinct architectonic Area Fo3 (OFC) 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 Fo3 (OFC). The probability map of Area Fo3 (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. Other available data versions of Area Fo3 (OFC): Henssen et al. (2018) [Data set, v3.2] [DOI: 10.25493/9ZWB-EEZ](https://doi.org/10.25493%2F9ZWB-EEZ) Henssen et al. (2019) [Data set, v3.4] [DOI: 10.25493/E1YQ-65U](https://doi.org/10.25493%2FE1YQ-65U) The most probable delineation of Area Fo3 (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, v1.13] [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: 10.6084/m9.figshare.15015288
title: Puya raimondii

description: Genome, annotation
DOI: 10.25493/evqx-9x4
title: Probability map of bundle rh_CMF-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 Caudal Middle Frontal and Superior Frontal regions of the Desikan-Killiany atlas, in the MNI ICBM152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fibre shape (fibre clustering), from the tractography datasets of 78 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fibre belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibres going through.
DOI: 10.25493/89wk-3dm
title: Probability map of bundle rh_PoC-PrC_2 (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 Postcentral and Precentral 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/qkjh-f45
title: Probabilistic cytoarchitectonic map of Subiculum (Hippocampus) (v11.1)

description: This dataset contains the distinct architectonic Subiculum (Hippocampus) 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 Subiculum (Hippocampus). The probability map of Subiculum (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 Subiculum (Hippocampus): Amunts et al. (2018) [Data set, v11b.0] [DOI: 10.25493/57EJ-01Z](https://doi.org/10.25493%2F57EJ-01Z) The most probable delineation of Subiculum (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)
DOI: 10.25493/7tqf-9dm
title: 3-D point data representing the spatial distribution of corticopontine projections originating in the mouse visual cortex

description: The 3-D point coordinate data represent the spatial distribution of mouse brain subcortical projections from different parts of the visual cortex to the caudoputamen, thalamus, superior colliculus and pontine nuclei. The point data are derived from segmented images series programmatically downloaded from the Allen Brain Atlas Data Portal. The data are suited for 3-D visualization and comparison the spatial arrangement of visual corticostriatal, corticothalamic, corticocollicular, and corticopontine projections.
DOI: 10.25493/t3vh-k6p
title: Densities and 3D distributions of synapses using FIB/SEM imaging in the mouse neocortex (somatosensory cortex)

description: Synapses have been identified, segmented and quantified in the adult mouse somatosensory cortex with 3D electron microscopy (FIB-SEM). Three animals have been used (ID5, ID24 and ID25). The six layers of the somatosensory cortex have been studied. Data obtained include the number of asymmetric and symmetric synapses, their sizes and their spatial distribution.
DOI: 10.25493/x0ns-fp7
title: Density measurements of different receptors for CA3 (Hippocampus) [rat, v2.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters of CA3 (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_ **muscarinic M<sub>1</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]pirenzepine_ **muscarinic M<sub>2</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]oxotremorine-M_ **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_ 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: 10.25493/8vra-x28
title: Probabilistic cytoarchitectonic map of Area hOc1 (V1, 17, CalcS) (v2.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area hOc1 (V1, 17, CalcS) 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 hOc1 (V1, 17, CalcS). The probability map of Area hOc1 (V1, 17, CalcS) 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 hOc1 (V1, 17, CalcS): Amunts et al. (2019) [Data set, v2.4] [DOI: 0.25493/MXJ6-6DH](https://doi.org/10.25493%2FMXJ6-6DH) The most probable delineation of Area hOc1 (V1, 17, CalcS) 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/pmdh-fw1
title: Hippocampal image volume derived from Thy1-GFP-M transgenic mouse

description: Reconstruction of the hippocampus of a Thy1-GFP-M transgenic mouse, where a subset of pyramidal neurons and of interneurons are labeled. Data obtained with serial two-photon tomography.
DOI: 10.25493/bjr2-kyk
title: Reference delineations of area CA (Hippocampus) in individual sections of the BigBrain

description: This dataset contains cytoarchitectonic delineations of areas CA1, CA2 and CA3 (Hippocampus) in the BigBrain dataset [Amunts et al. 2013]. The delineations were created after identification of the areas in the histological, cell-stained sections based on the cytoarchitectonic criteria. Mappings of the hippocampal CA1, CA2 and CA3 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/jnn1-v3q
title: Probabilistic cytoarchitectonic map of Fastigial Nucleus (Cerebellum) (v6.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Fastigial 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 Fastigial Nucleus (Cerebellum). The probability map of Fastigial 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 Fastigial 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 Fastigial 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/ynq9-8bm
title: Probability map of bundle rh_CAC-PoCi_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 Caudal Anterior Cingulate and Posterior Cingulate 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.