cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/m85e-vxr
title: Density measurements of different receptors for CA2 (Hippocampus) [rat, v1.0]

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_ 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: 10.25493/71yk-2je
title: 3D cyto- and receptor architectonic map of macaque lateral premotor area F7d

description: A multimodal analysis of the macaque motor cortex was carried out to create a 3D atlas integrating and reconciling discrepancies between previously published maps. This involved a quantitative and statistically testable approach to the analysis of cytoarchitecture in the macaque monkey frontal agranular cortex, in combination with an in vitro quantitative receptor autoradiographic analysis of the regional and laminar distribution patterns of 13 different neurotransmitter receptors. The borders of the identified areas were traced onto a reference macaque monkey brain in stereotaxic space. We provide here for the first time a map of lateral premotor area F7d of the macaque monkey brain integrating cyto- and receptor architectonic features in stereotaxic space. It constitutes a valuable resource for the analysis of functional experiments with non-human primates, and provides crucial data for modeling approaches with realistic synaptic dynamics.
DOI: 10.25493/w4h2-wtm
title: Probabilistic cytoarchitectonic map of Area hOc4lp (LOC) (v3.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area hOc4lp (LOC) in the individual, single subject template of the MNI Colin 27 reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to the reference space, where each voxel was assigned the probability to belong to Area hOc4lp (LOC). The probability map of Area hOc4lp (LOC) is provided in NifTi format for each hemisphere in the reference space. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and updated probability estimates for new brain structures may in some cases lead to measurable but negligible deviations of existing probability maps, as compared to earlier released datasets. Other available data versions of Area hOc4lp (LOC): Malikovic et al. (2019) [Data set, v3.4] [DOI: 10.25493/4B87-Q8X](https://doi.org/10.25493%2F4B87-Q8X) The most probable delineation of Area hOc4lp (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/n7y5-4x
title: Probability map of bundle rh_RMF-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 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/mxj6-6dh
title: Probabilistic cytoarchitectonic map of Area hOc1 (V1, 17, CalcS) (v2.4)

description: This dataset contains the distinct architectonic Area hOc1 (V1, 17, CalcS) 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 hOc1 (V1, 17, CalcS). The probability map of Area hOc1 (V1, 17, CalcS) 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 hOc1 (V1, 17, CalcS): Amunts et al. (2018) [Data set, v2.2] [DOI: 10.25493/8VRA-X28](https://doi.org/10.25493%2F8VRA-X28) 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/k243-13k
title: Graphical representation of rat cortical vasculature reconstructed from high resolution 3D SRXTM data.

description: The goal of the study is to produce the first high resolution, 3D reconstruction of the entire macrostructure and vascular system of the rat/mouse brain. To that end synchrotron radiation-based X-ray microscopy images were collected. Then the data is translated into 3D mesh and volumetric models (graph). This dataset contains the model of part of cortical vasculature of the rat brain as a graph structure (discrete mathematics). The data is a derived dataset using computational methods from the dataset “3D high resolution SRXTM image data of cortical vasculature of rat brain” ([doi: 10.25493/HCPQ-MY8](https://doi.org/10.25493/HCPQ-MY8)).
DOI: 10.25493/a5v4-hfh
title: Probabilistic cytoarchitectonic map of Area 5L (SPL) (v8.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area 5L (SPL) 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 5L (SPL). The probability map of Area 5L (SPL) 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 5L (SPL): Scheperjans et al. (2019) [Data set, v8.4] [DOI: 10.25493/C1FQ-2F](https://doi.org/10.25493%2FC1FQ-2F) The most probable delineation of Area 5L (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)
DOI: 10.25493/ja3w-b20
title: Probabilistic cytoarchitectonic map of Area hOc2 (V2, 18) (v4.0)

description: This dataset contains the distinct architectonic Area hOc2 (V2, 18) 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 hOc2 (V2, 18). The probability map of Area hOc2 (V2, 18) 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 hOc2 (V2, 18): Amunts et al. (2018) [Data set, v2.2] [DOI: 10.25493/FKMC-ZX7](https://doi.org/10.25493%2FFKMC-ZX7) Amunts et al. (2019) [Data set, v2.4] [DOI: 10.25493/QG9C-THD](https://doi.org/10.25493%2FQG9C-THD) The most probable delineation of Area hOc2 (V2, 18) 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/x66e-mzd
title: Probability map of bundle lh_PoCi-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 left hemisphere Posterior 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/dces-915
title: Regional and laminar distribution of silver stained cell bodies in the rat brain

description: Classical mapping studies relied on the analysis of histologically processed sections from formalin-fixed brains for the identification of borders between cortical areas or of the boundaries of subcortical nuclei. Differences in the regional and laminar distribution patterns of neurotransmitter receptors are known to complement and extend the insights into the structural organization of the brain, which are provided by cytoarchitectonic observations alone. Since the method of quantitative *in vitro* receptor autoradiography requires processing of unfixed, shock frozen brains, we here present a modification of a silver cell body histological staining which can be applied to cryosections neighbouring those destined to labelling of receptors. The histological staining resembles a Nissl staining, but yields a higher contrast between cell bodies and neuropil, and can also be used for quantitative approaches to cytoarchitectonic mapping. The present dataset provides high-resolution digitized images of coronal sections through the rat brain that were processed for the visualization of cell bodies, and which complement a series processed for the visualization of 19 different receptor binding sites from multiple neurotransmitter systems.
DOI: 10.25493/50aw-k2c
title: Structure of the axon initial segment of mouse striatal and cerebellar neurons

description: We have analyzed the structural organization of the axon initial segment (AIS) of the main populations of striatal and cerebellar neurons, using immunofluorescence, confocal microscopy and 3D reconstruction techniques (Imaris software). In the striatum we measured the length, mean diameter and surface area of the AIS of D1 and D2 projection neurons and of neuronal populations expressing Choline acetyltransferase (Chat), calretinin (CR), nitric oxide synthase (NOS) parvalbumin (PV) and neuropeptide-Y (NPY). In addition, the same AIS morphological parameters were measured in Stellate, Basket, Purkinje, Golgi and Granular neurons of the mouse cerebellum. Data will be useful to modeling and integration of anatomical data with functional studies in these brain regions.
DOI: 10.25493/ambk-r1t
title: Probabilistic cytoarchitectonic map of Area Ig3 (Insula) (v4.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area Ig3 (Insula) 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 toArea Ig3 (Insula). The probability map of Area Ig3 (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 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 Ig3 (Insula): Quabs et al. (2019) [Data set, v11.0] [DOI: 10.25493/2FTZ-MWQ](https://doi.org/10.25493/10.25493/2FTZ-MWQ) The most probable delineation of Area Ig3 (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: 10.34810/data72/341
title: 01CO_1.MR.0004.0104.2016.02.04.15.44.11.109375.67138062.IMA
DOI: 10.25493/e724-7kc
title: Probabilistic map of the left short cingulate fibres (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the probability map of the left short cingulate fibres (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/evvv-tcj
title: Interpolated 3D map of Area hIP5 (IPS) in the BigBrain

description: This dataset contains cytoarchitectonic maps of Area hIP5 (IPS) in the Big Brain dataset [Amunts et al. 2013]. The mappings were created using the semi-automatic method presented in Schleicher et al. 1999, based on coronal histological sections on 1 micron resolution. Mappings are available on approximately every 15-60th section of this region. They were then aligned to the corresponding sections of the 3D reconstructed Big Brain space, using the transformations used in Amunts et al. 2013, kindly provided by Claude Lepage (McGill University). From these delineations, a preliminary 3D map of Area hIP5 (IPS) has been created by simple interpolation of the coronal contours in the 3D anatomical space of the Big Brain. This map gives a first impression of the location of this area in the Big Brain, and can be viewed in the atlas viewer using the URL below. A full mapping of this area in every histological section using a Deep Learning approach is in progress. **Additional information:** The reference delineations used for this map are part of the work for the corresponding probabilistic map of Area hIP5 (IPS) of the JuBrain Cytoarchitectonic Atlas, published in: Richter et al. (2019) [Data set, v7.0] [DOI: 10.25493/WZNZ-DPQ](https://doi.org/10.25493%2FWZNZ-DPQ) Richter et al. (2019) [Data set, v7.1] [DOI: 10.25493/RNSM-Y4Y](https://doi.org/10.25493%2FRNSM-Y4Y) In addition, the dataset of the probabilistic cytoarchitectonic map of Area hIP5 (IPS) is part of the following research publication: Richter, M., Amunts, K., Mohlberg, H., Bludau, S., Eickhoff, S. B., Zilles, K., Caspers, S. (2018). Cytoarchitectonic segregation of human posterior intraparietal and adjacent parieto-occipital sulcus and its relation to visuomotor and cognitive functions. Cerebral Cortex, 29(3), 1305-1327 [DOI: 10.1093/cercor/bhy245](https://doi.org/10.1093%2Fcercor%2Fbhy245%20)
DOI: 10.25493/fr6p-6hw
title: Probabilistic cytoarchitectonic map of Area hIP2 (IPS) (v6.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area hIP2 (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 hIP2 (IPS). The probability map of Area hIP2 (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 hIP2 (IPS): Choi et al. (2019) [Data set, v6.1] [DOI: 10.25493/EJTM-NDY](https://doi.org/10.25493%2FEJTM-NDY) The most probable delineation of Area hIP2 (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/b85t-d88
title: Probabilistic cytoarchitectonic map of CA (Hippocampus) (v11.1)

description: This dataset contains the distinct architectonic CA (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 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 both reference spaces, where each voxel was assigned the probability to belong to CA (Hippocampus). The probability map of CA (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. The most probable delineation of CA (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, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR)
DOI: 10.25493/cvh7-1h7
title: Functional map of right 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, right 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.