cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/t96p-05y
title: Probabilistic cytoarchitectonic map of Area PGa (IPL) (v9.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area PGa (IPL) 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 PGa (IPL). The probability map of Area PGa (IPL) 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 PGa (IPL): 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)
DOI: 10.25493/2rav-wsz
title: Interpolated 3D map of Area hIP6 (IPS) in the BigBrain

description: This dataset contains cytoarchitectonic maps of Area hIP6 (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 hIP6 (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 hIP6 (IPS) of the JuBrain Cytoarchitectonic Atlas, published in: Richter et al. (2019) [Data set, v7.0] [DOI: 10.25493/SVEY-ZBS](https://doi.org/10.25493%2FSVEY-ZBS) Richter et al. (2019) [Data set, v7.1] [DOI: 10.25493/AFQR-50Q](https://doi.org/10.25493%2FAFQR-50Q) In addition, the dataset of the probabilistic cytoarchitectonic map of Area hIP6 (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: 10.25493/8ehq-xyy
title: GapMap Frontal to Occipital (v9.1)

description: This dataset contains the “GapMap Frontal to Occipital” in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. In order to provide whole-brain coverage for the cortex within the Julich-Brain Atlas, yet uncharted parts of the frontal cortex have been combined to the brain region “GapMap Frontal to Occipital”. The distributions were modeled so that probabilistic gap maps were computed in analogy to other maps of the Julich-Brain Atlas. The probabilistic map of “GapMap Frontal to Occipital” 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. New maps are continuously replacing parts of “GapMap Frontal to Occipital” with progress in mapping. Other available data versions of Gap Map Frontal to Occipital: Amunts et al. (2020) [Data set, v9.0] [DOI: 10.25493/PBP4-HE6](https://doi.org/10.25493%2FPBP4-HE6) The most probable delineation of GapMap Frontal to Occipital 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/ht6m-vf
title: Neural Correlates of Multisensory Detection Behavior in Primary and Higher-Order Visual Cortex

description: The brain acts on the environment by gathering multisensory information. The information from multiple senses is combined effectively in the mammalian brain to form one unified perceptual experience. This process of multisensory integration has been linked to multimodal activity in sensory areas of the neocortex, but the precise mechanisms and relevant areas are still not fully elucidated. In this work, we imaged neuronal activity in the primary visual cortex (V1) and Anterolateral visual cortex (AL) of the mouse during a visual, auditory and audiovisual detection task. Animals were trained to respond by licking a lickspout if a stimulus was perceived, and withhold licking if there was no stimulus. This paradigm provides us with the chance to see enhanced responses in multisensory conditions. The task information is saved in MATLAB files holding relevant information such as animals response information (such as correct/incorrect, response time) and stimulus information (such as modality, contrast or loudness). Two-photon data were collected and processed; individual cells’ dF/F traces have been extracted and are presented in the same MATLAB files (see Usage notes for more details about the data layout). With these data, we found that AL neurons are more sensitive to weak stimuli compared to V1. Both V1 and AL cells showed modulation of visual responses in the multisensory condition, meaning the addition of an auditory stimulus to the visual one changed the cell responses. For multisensory stimuli, area AL showed stronger responses when the animal reported to have seen a stimulus, versus when it did not. Results indicate area AL correlated more with multisensory detection than V1.
DOI: 10.25493/7xn4-czq
title: Density measurements of different receptors for CA2 (Hippocampus) [rat, v2.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters of CA2 (hippocampus) obtained by means of quantitative _in vitro_ autoradiography. The receptor densities are visualized as _fingerprints_ (**fp**), which provide the mean density and standard deviation for each of the analyzed receptor types, averaged across samples. Overview of available measurements [ **receptor** \| **_neurotransmitter_** \| _labeling agent_ ]: **AMPA** \| **_glutamate_** \| _[<sup>3</sup>H]AMPA_ **kainate** \| **_glutamate_** \| _[<sup>3</sup>H]kainate_ **NMDA** \| **_glutamate_** \| _[<sup>3</sup>H]MK-801_ **GABA<sub>A</sub>** \| **_GABA_** \| _[<sup>3</sup>H]muscimol_ **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/s1hg-n64
title: Probability map of bundle rh_CMF-PrC_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 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/k87f-js5
title: Probability map of bundle rh_IT-MT_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 Inferior Temporal and Middle Temporal 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: 10.34810/data72/382
title: 01CO_1.MR.0004.0193.2016.02.04.15.44.11.109375.67141630.IMA
DOI: 10.25493/hpx1-1ct
title: Probabilistic cytoarchitectonic map of Area 6ma (preSMA, mesial SFG) (v9.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area 6ma (preSMA, mesial SFG) 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 6ma (preSMA, mesial SFG). The probability map of Area 6ma (preSMA, mesial SFG) 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 6ma (preSMA, mesial SFG): Ruan et al. (2019) [Data set, v9.1] [DOI: 10.25493/WVNR-SPT](https://doi.org/10.25493%2FWVNR-SPT) The most probable delineation of Area 6ma (preSMA, mesial SFG) 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: 10.5281/zenodo.5115251
title: Silber_et_al_EMC²_gmd-2021-194_code_and_data

description: This Repository includes the code for The Earth Model Column Collaboratory (EMC²) v1.1 described in gmd-2021-194: "The Earth Model Column Collaboratory (EMC²) v1.1: An Open-Source Ground-Based Lidar and Radar Instrument Simulator and Subcolumn Generator for Large-Scale Models" by Silber et al. The Repository also includes the ModelE3 SCM and DHARMA LES model output data and EMC²-processed files used to produce the manuscript's plots together with a Jupyter Notebook for running EMC² and producing these plots. The most recent updates of EMC² can be found at: https://github.com/columncolab/EMC2
DOI: 10.25493/xkyg-v5n
title: Anterograde visualization of projections from lateral orbitofrontal cortex in rat (case: F38)

description: High-resolution bright-field microscopy images (35) 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 lateral orbitofrontal cortex (LOC). 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/e3qx-88a
title: Probabilistic cytoarchitectonic map of Area OP4 (POperc) (v12.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area OP4 (POperc) 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 OP4 (POperc). The probability map of Area OP4 (POperc) 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 OP4 (POperc): Eickhoff et al. (2018) [Data set, v9.2] [DOI: 10.25493/51S0-K7W](https://doi.org/10.25493%2F51S0-K7W) Eickhoff et al. (2019) [Data set, v9.4] [DOI: 10.25493/BVT0-H3U](https://doi.org/10.25493%2FBVT0-H3U) Eickhoff et al. (2020) [Data set, v11.0] [DOI: 10.25493/99GW-N85](https://doi.org/10.25493%2F99GW-N85) The most probable delineation of Area OP4 (POperc) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D) 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/w2w2-at1
title: Density measurements of different receptors for Area ifj2 (IFS,PreCS) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of 17 receptors for classical neurotransmitters in Area ifj2 (IFS,PreCS) 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. For exemplary samples, we also provide color-coded laminar autoradiography images (ar). The autoradiography images show an exemplary density distribution of a single receptor for one laminar cross-section in a single tissue sample. Information on the used tissue samples and corresponding subjects for the receptor fingerprints and autoradiographs as well as a list of analyzed receptors accompanies the provided dataset.
DOI: 10.25493/v5hy-xts
title: Probabilistic cytoarchitectonic map of Area PGa (IPL) (v9.4)

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) 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)
DOI: 10.25493/e0yj-ade
title: Probabilistic cytoarchitectonic map of Area hOc4v (LingG) (v3.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area hOc4v (LingG) 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 hOc4v (LingG). The probability map of Area hOc4v (LingG) 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 hOc4v (LingG): Rottschy et al. (2019) [Data set, v3.4] [DOI: 10.25493/AASR-M8P](https://doi.org/10.25493%2FAASR-M8P) The most probable delineation of Area hOc4v (LingG) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/njyw-9r
title: Probabilistic cytoarchitectonic map of Area p24ab (pACC) (v18.0)

description: This dataset contains the distinct architectonic areas p24a and p24b (combined to a single probability map named Area p24ab (pACC)) 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 p24ab (pACC). The probability map of Area p24ab (pACC) 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 p24ab (pACC): Palomero-Gallagher et al. (2019) [Data set, v16.0] [DOI: 10.25493/80YK-SN0](https://doi.org/10.25493%2F80YK-SN0) Palomero-Gallagher et al. (2019) [Data set, v16.1] [DOI:10.25493/DHXC-2KN](https://doi.org/10.25493%2FDHXC-2KN) The most probable delineation of Area p24ab (pACC) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0)
DOI: 10.25493/7hfq-98y
title: Probabilistic map of the left temporal cingulate fibres (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the probability map of the left temporal 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.