cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/q3pa-5gb
title: Interpolated 3D map of Area hIP8 (IPS) in the BigBrain

description: This dataset contains cytoarchitectonic maps of Area hIP8 (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 hIP8 (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 hIP8 (IPS) of the JuBrain Cytoarchitectonic Atlas, published in: Richter et al. (2019) [Data set, v7.0] [DOI: 10.25493/JBV5-J8J](https://doi.org/10.25493%2FJBV5-J8J) Richter et al. (2019) [Data set, v7.1] [DOI: 10.25493/YYT8-FT8](https://doi.org/10.25493%2FYYT8-FT8) In addition, the dataset of the probabilistic cytoarchitectonic map of Area hIP8 (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/qn1f-waf
title: Probabilistic cytoarchitectonic map of Area hIP7 (IPS) (v7.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area hIP7 (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 hIP7 (IPS). The probability map of Area hIP7 (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 hIP7 (IPS): Richter et al. (2019) [Data set, v7.1] [DOI: 10.25493/WRCY-8Z1](https://doi.org/10.25493%2FWRCY-8Z1) The most probable delineation of Area hIP7 (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, v1.18] [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/j6te-qwd
title: Regional and laminar distribution of receptors for serotonin in the rat brain

description: The present dataset provides the quantitative regional and laminar distribution of key molecules in signal transfer, namely the serotonin receptors 5-HT<sub>1A</sub> and 5-HT<sub>2</sub> in five selected rostrocaudal levels of the rat brain. The receptors were visualized by means of quantitative *in vitro* receptor autoradiography and the selective tritiated ligands 8-OH-DPAT and ketanserin. The high spatial resolution of this method enables quantification of receptor densities in anatomically identifiable cortical structures as detailed as the hippocampal regions and layers, or subcortical structures such as amygdalar nuclei.
DOI: 10.25493/h93n-8gq
title: 3D cyto- and receptor architectonic map of macaque area PF of the inferior parietal lobe

description: A multimodal analysis of the macaque inferior parietal lobule 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 inferior parietal lobe, in combination with an in vitro quantitative receptor autoradiographic analysis of the regional and laminar distribution patterns of 15 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 area PF 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/t2m3-2st
title: Probabilistic cytoarchitectonic map of Area OP7 (Frontal Operculum) (v2.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area OP7 (Frontal Operculum) 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 OP7 (Frontal Operculum). The probability map of Area OP7 (Frontal Operculum) 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. The most probable delineation of Area OP7 (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. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-64D)
DOI: 10.25493/td8x-vs9
title: Probabilistic cytoarchitectonic map of HC-Presubiculum (Hippocampus) (v13.0)

description: This dataset contains the distinct architectonic HC-Presubiculum (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 HC-Presubiculum (Hippocampus). The probability map of HC-Presubiculum (Hippocampus) 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 Entorhinal Cortex derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53)
DOI: 10.25493/e1yq-65u
title: Probabilistic cytoarchitectonic map of Area Fo3 (OFC) (v3.4)

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) 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)
DOI: 10.25493/1ggf-y82
title: Characterization of spatial distribution of activity in cerebellar cortical slices

description: This project is focused on the characterization of the spatial distribution of activity in cerebellar cortical slices, with particular attention to the granular layer and the Purkinje cell layer. The excitatory input of the mossy fiber activated both granule and Golgi cells in the granular layer. While Golgi cells are inhibitory interneurons that inhibit granule cells through a feedback and a feedforward loops, granule cell axons contact Purkinje cells through the ascending part and the parallel fibers. In this project, high-density multi-electrode array recordings (HD-MEA) are used to characterize the distribution of activity in the cerebellar cortex following five-pulses electrical activation of mossy fibers at 6Hz, in acute slices of the cerebellar vermis.
DOI: 10.25493/5m61-gpe
title: 3-D point data representing the spatial distribution of corticopontine projections originating in the mouse frontal cortex

description: The 3-D point coordinate data represent the spatial distribution of mouse brain subcortical projections from different parts of the frontal cortex (e.g. secondary motor 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 frontal corticostriatal, corticothalamic, corticocollicular, and corticopontine projections.
DOI: 10.34810/data72/356
title: 01CO_1.MR.0004.0108.2016.02.04.15.44.11.109375.67138222.IMA
DOI: 10.25493/51am-wn4
title: Probabilistic cytoarchitectonic map of Area 25 (sACC) (v16.1)

description: This dataset contains the distinct architectonic Area 25 (sACC) 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 25 (sACC). The probability map of Area 25 (sACC) 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 25 (sACC): Palomero-Gallagher et al. (2018) [Data set, v16.0] [DOI: 10.25493/NMPJ-EU](https://doi.org/10.25493%2FNMPJ-EU) The most probable delineation of Area 25 (sACC) 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/xk1b-v89
title: Mechanisms underlying cortical complexity

description: We used electrophysiology in vivo and in ferret cortical slices in vitro to investigate the mechanisms underlying cortical complexity and its association with different brain states in wild type and transgenic murine models of cognitive deficits. For this dataset, ferret cortical slices in vitro were used to investigated cellular and network mechanisms underlying cortical complexity by means of electrical, pharmacological or optical tools in collaboration with T.3.2.2<sup>[1]</sup>, T3.4.1<sup>[1]</sup> and T3.4.4<sup>[1]</sup>. <sup>[1]</sup> HBP funded projects with task ID T3.2.2 “Multiscale mapping of cortical activity, reactivity and effec-tive connectivity across states in humans” (SGA2) <sup>[2]</sup> HBP funded projects with task ID T3.4.1 “Measures and mechanisms of consciousness in rodents” (SGA2) <sup>[3]</sup> HBP funded projects with task ID T3.4.4 “Brain structure-function dynamical interactions in patho-logical, pharmacological and physiological modulation of consciousness” (SGA2)
DOI: 10.25493/ekv9-29d
title: Probabilistic cytoarchitectonic map of Area Ig1 (Insula) (v13.1)

description: This dataset contains the distinct architectonic Area Ig1 (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 to Area Ig1 (Insula). The probability map of Area Ig1 (Insula) is provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area Ig1 (Insula): Kurth et al. (2018) [Data set, v11.0] [DOI: 10.25493/H2H6-0SA](https://doi.org/10.25493%2FH2H6-0SA) The most probable delineation of Area Ig1 (Insula) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/d58j-vdc
title: Probabilistic map of the direct segment of the right arcuate fasciculus (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the probability map of the direct 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/q542-wsq
title: Anterograde visualization of projections from ventral orbitofrontal cortex in rat (case: F6)

description: High-resolution bright-field microscopy images (45) of serial coronal brain sections showing anterogradely labeled axons in orbitofrontal cortex (OFC) in a female adult Sprague Dawley rat. The anterograde tracer biotinylated dextran amine (BDA) was injected into the ventral orbitofrontal cortex (VOC). Brain sections are 300 µm apart and 50 µm thick. The dataset contains a complete series throughout the anterior-posterior extent of the brain, contributing to visualization of a topographically organized connectivity from OFC throughout the brain, including the published data on a topographically organized projection to the parahippocampal areas.
DOI: 10.25493/34q4-h62
title: Probabilistic cytoarchitectonic map of Area Fo6 (OFC) (v2.1)

description: This dataset contains the distinct architectonic Area Fo6 (OFC) 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 Fo6 (OFC). The probability map of Area Fo6 (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. The most probable delineation of Area Fo6 (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, v1.18] [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)
DOI: 10.25493/yahm-8nt
title: DiFuMo atlas (1024 dimensions)

description: DiFuMo (Dictionary of Functional Modes) 1024-dimensional atlas presents a parcellation of the brain into 1024 elementary structures using sparse dictionary learning. The atlas contains two main components: (1) an image of the 1024 spatial components, sampled in MNI space, and (2) anatomical names for these 1024 regions. It is meant to serve as an atlas to extract functional signals, eg to create image-derived phenotypes. These modes are optimized to represent well raw BOLD time series, over a with range of experimental conditions.
DOI: 10.25493/zkr8-p13
title: Probabilistic cytoarchitectonic map of Area hOc6 (POS) (v7.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area hOc6 (POS) 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 hOc6 (POS). The probability map of Area hOc6 (POS) 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 hOc6 (POS): Richter et al. (2019) [Data set, v7.1] [DOI: 10.25493/4101-1ZG](https://doi.org/10.25493%2F4101-1ZG) The most probable delineation of Area hOc6 (POS) 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/x4zt-are
title: E18.5 3D Edge-Aware Refined Atlas Derived from the Allen Developing Mouse Brain Atlas

description: 3D imaging necessitates corresponding 3D anatomical atlases, but generating such atlases is time consuming and challenging. Substantial efforts have generated such atlases including the Allen Developing Mouse Brain Atlas (ADMBA) but are typically limited by the process of serial 2D annotation for 3D reconstruction leading to artifacts. We have developed an automated, edge-aware atlas refinement method to curate existing atlases in a topologically consistent, 3D manner. Applying this approach to the E18.5 atlas in the ADMBA, we inferred labels for incompletely labeled areas, smoothed the labels in 3D, and realigned the labels to the underlying anatomical edges. As required, we also rotated images in 3D for midline symmetry and resized a stretch of compressed sagittal label planes.