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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Zaborszky, L.; Hoemke, L.; Mohlberg, H.; Schleicher, A.; +2 Authors

    This dataset contains the distinct probabilistic cytoarchitectonic map of Ch 123 (Basal Forebrain) 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 Ch 123 (Basal Forebrain). The probability map of Ch 123 (Basal Forebrain) 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 Ch 123 (Basal Forebrain): Zaborszky et al. (2019) [Data set, v4.2] [DOI: 10.25493/7SEP-P2V](https://doi.org/10.25493%2F7SEP-P2V) The most probable delineation of Ch 123 (Basal Forebrain) 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)

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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    EBRAINS
    Dataset . 2018
    License: CC BY NC SA
    Data sources: Sygma; Datacite
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      EBRAINS
      Dataset . 2018
      License: CC BY NC SA
      Data sources: Sygma; Datacite
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    Authors: Eickhoff, S. B.; Schleicher, A.; Amunts, K.; Mohlberg, H.; +1 Authors

    This dataset contains the distinct architectonic Area OP2 (POperc) in the MNI Colin 27 and MNI ICBM 152 reference spaces. As part of the Julich-Brain 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. 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 OP2 (POperc). The probability map of Area OP2 (POperc) 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 OP2 (POperc): Eickhoff et al. (2018) [Data set, v9.2] [DOI: 10.25493/F8W5-HNB](https://doi.org/10.25493%2FF8W5-HNB) Eickhoff et al. (2020) [Data set, v11.0] [DOI: 10.25493/SDW0-YEZ](https://doi.org/10.25493%2FSDW0-YEZ) The most probable delineation of Area OP2 (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)

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    EBRAINS
    Dataset . 2019
    License: CC BY NC SA
    Data sources: Sygma; Datacite
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      EBRAINS
      Dataset . 2019
      License: CC BY NC SA
      Data sources: Sygma; Datacite
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    Authors: Rottschy, C.; Eickhoff, S. B.; Schleicher, A.; Mohlberg, H.; +3 Authors

    This dataset contains the distinct probabilistic cytoarchitectonic map of Area hOc3v (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 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 hOc3v (LingG). The probability map of Area hOc3v (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 hOc3v (LingG): Rottschy et al. (2019) [Data set, v3.4] [DOI: 10.25493/E5E8-1VV](https://doi.org/10.25493%2FE5E8-1VV) The most probable delineation of Area hOc3v (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)

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    EBRAINS
    Dataset . 2018
    License: CC BY NC SA
    Data sources: Sygma; Datacite
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      EBRAINS
      Dataset . 2018
      License: CC BY NC SA
      Data sources: Sygma; Datacite
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    Authors: Armendariz, Marcelo; Ban, Hiroshi; Welchman, Andrew E; Vanduffel, Wim;

    Electrophysiological evidence suggested primarily the involvement of area MT in depth cue integration in macaques, as opposed to human imaging data pinpointing area V3B/KO. To clarify this conundrum, we decoded monkey fMRI responses evoked by stimuli signaling near or far depths defined by binocular disparity, relative motion and their combination, and we compared results with those from an identical experiment previously performed in humans.Responses in macaque area MT are more discriminable when two cues concurrently signal depth, and information provided by one cue is diagnostic of depth indicated by the other. This suggests that monkey area MT computes fusion of disparity and motion depth signals, exactly as shown for human area V3B/KO. Hence, these data reconcile previously reported discrepancies between depth processing in human and monkey by showing the involvement of the dorsal stream in depth cue integration using the same technique, despite the engagement of different regions. data describing fig 1-8 and sfig 1-12data.zip

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    DANS-EASY
    Dataset . 2019
    Data sources: B2FIND
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    ZENODO; DRYAD
    Dataset . 2019
    License: CC 0
    Data sources: Datacite; ZENODO
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      DANS-EASY
      Dataset . 2019
      Data sources: B2FIND
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      ZENODO; DRYAD
      Dataset . 2019
      License: CC 0
      Data sources: Datacite; ZENODO
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    Authors: Palomero-Gallagher, N.; Zilles, K.;

    This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters of CA, stratum cellulare (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_** \| _[3H]AMPA_ **kainate** \| **_glutamate_** \| _[3H]kainate_ **NMDA** \| **_glutamate_** \| _[3H]MK-801_ **GABAA** \| **_GABA_** \| _[3H]muscimol_ **muscarinic M1** \| **_acetylcholine_** \| _[3H]pirenzepine_ **muscarinic M2** \| **_acetylcholine_** \| _[3H]oxotremorine-M_ **α1** \| **_noradrenalin/norepinephrine_** \| _[3H]prazosin_ **α2** \| **_noradrenalin/norepinephrine_** \| _[3H]UK-14,304_ **5-HT1A** \| **_serotonin_** \| _[3H]8-OH-DPAT_ **5-HT2** \| **_serotonin_** \| _[3H]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

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    EBRAINS
    Dataset . 2020
    License: CC BY NC SA
    Data sources: Sygma; Datacite
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    EBRAINS
    Dataset . 2020
    License: CC BY NC SA
    Data sources: Sygma; Datacite
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      EBRAINS
      Dataset . 2020
      License: CC BY NC SA
      Data sources: Sygma; Datacite
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      EBRAINS
      Dataset . 2020
      License: CC BY NC SA
      Data sources: Sygma; Datacite
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    Authors: Costantini, I.; Mazzamuto, G.; Pesce, L.; Scardigli, M.; +1 Authors

    The aim of this work is to study the 3D organization of some population of interneurons within a sample of interest, previously analyzed for physiological information, using two photon fluorescence microscopy (TPFM). We exploit the high axial and radial resolution of TPFM optical sectioning (0.44 x 0.44 x 2 μm³) in combination with a protocol for tissue clearing and labeling to perform the 3D reconstruction of 300 um thick brain sections. We clear the sections with the SWITCH/TDE clearing method and label the samples with three antibodies to co-stain three different populations of inhibitory interneurons: PV (Parvalbumin), SST(Somatostatin), and VIP (Vaso Intestinal Peptide). A previous data version of “Molecular characterization of the interneurons in human temporal neocortex by two photon fluorescence microscopy” can be found here: Costantini et al. (2020) [Data set, v1.0] [DOI: 10.25493/V9GW-4JG]( https://doi.org/10.25493%2FV9GW-4JG)

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    EBRAINS
    Dataset . 2021
    License: CC BY NC SA
    Data sources: Datacite; Sygma
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      EBRAINS
      Dataset . 2021
      License: CC BY NC SA
      Data sources: Datacite; Sygma
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    Authors: Ballardini, Giulia; Ponassi, Valentina; Galofaro, Elisa; Carlini, Giorgio; +4 Authors

    Additional file 1. Analysis of the movement and force strategies applied to solve the task. We analyzed the strategies used by the subjects for accomplishing the tasks, to verify if they can provide further explanations of the results presented in the manuscript. In Experiment 1 we found that the loading conditions influenced the kinematic strategy during the position matching task. In Experiment 2 the strategy adopted for bimanual force exertion was not influenced by symmetric/asymmetric arm configurations, but by handedness or hand preference effects. Figure S1. Example of speed and force profile.

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    figshare
    Dataset . 2019
    License: CC BY
    Data sources: Datacite
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    Dataset . 2019
    License: CC BY
    Data sources: Datacite
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      Dataset . 2019
      License: CC BY
      Data sources: Datacite
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      figshare
      Dataset . 2019
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ruan, J.; Bludau, S.; Palomero-Gallagher, N.; Caspers, S.; +4 Authors

    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)

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    EBRAINS
    Dataset . 2019
    License: CC BY NC SA
    Data sources: Datacite; Sygma
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      EBRAINS
      Dataset . 2019
      License: CC BY NC SA
      Data sources: Datacite; Sygma
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    Authors: Ruland (Née Bradler), S. H.; Schiffer, C.; Dickscheid, T.; Mohlberg, H.; +1 Authors

    This dataset contains cytoarchitectonic maps of Area ifj1 (IFS/PreCS) in the BigBrain. The mappings were created using cytoarchitectonic criteria applied on digitized histological sections of 1 ��m resolution, cut in coronal plane. Areal borders have been detected by an oberserver-independent border definition (Schleicher 2000). Mappings are available on sections of the BigBrain and have been transformed to the 3D reconstructed BigBrain space using the transformations used in Amunts et al. 2013. From these delineations, a preliminary 3D map of Area ifj1 (IFS/PreCS) 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.

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    EBRAINS
    Dataset . 2022
    License: CC BY NC SA
    Data sources: Sygma; Datacite
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      EBRAINS
      Dataset . 2022
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    Authors: Kampis, Dora; Parise, Eugenio; Csibra, Gergely; Kovács, Ágnes;

    A major feat of social beings is to encode what their conspecifics see, know or believe. While various nonhuman animals show precursors of these abilities, humans perform uniquely sophisticated inferences about other people’s mental states. However, it is still unclear how these possibly human-specific capacities develop and whether preverbal infants, similarly to adults form representations of other agents’ mental states, specifically metarepresentations. We explored the neuro-cognitive bases of 8-month-olds’ ability to encode the world from another person’s perspective, using gamma-band EEG activity over the temporal lobes, an established neural signature for sustained object representation after occlusion. We observed such gamma-band activity when an object was occluded from the infants’ perspective, as well as when it was occluded only from the other person (Experiment 1), and also when subsequently the object disappeared but the person falsely believed the object to be present (Experiment 2). These findings suggest that the cognitive systems involved in representing the world from infants’ own perspective are also recruited for encoding others’ beliefs. Such results point to an early developing, powerful apparatus suitable to deal with multiple concurrent representations; and suggest that infants can have a metarepresentational understanding of other minds even before the onset of language. EEG data 8mo infants kampis_parise_csibra_kovacsEEG Data of manuscript Kampis, Parise, Csibra & Kovács. Values depict averaged activation between 25-35 Hz on the indicated channels and conditions.

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    DANS-EASY
    Dataset . 2015
    Data sources: B2FIND
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    ZENODO; DRYAD
    Dataset . 2016
    License: CC 0
    Data sources: Datacite; ZENODO
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      DANS-EASY
      Dataset . 2015
      Data sources: B2FIND
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      ZENODO; DRYAD
      Dataset . 2016
      License: CC 0
      Data sources: Datacite; ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Zaborszky, L.; Hoemke, L.; Mohlberg, H.; Schleicher, A.; +2 Authors

    This dataset contains the distinct probabilistic cytoarchitectonic map of Ch 123 (Basal Forebrain) 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 Ch 123 (Basal Forebrain). The probability map of Ch 123 (Basal Forebrain) 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 Ch 123 (Basal Forebrain): Zaborszky et al. (2019) [Data set, v4.2] [DOI: 10.25493/7SEP-P2V](https://doi.org/10.25493%2F7SEP-P2V) The most probable delineation of Ch 123 (Basal Forebrain) 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)

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    EBRAINS
    Dataset . 2018
    License: CC BY NC SA
    Data sources: Sygma; Datacite
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      EBRAINS
      Dataset . 2018
      License: CC BY NC SA
      Data sources: Sygma; Datacite
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    Authors: Eickhoff, S. B.; Schleicher, A.; Amunts, K.; Mohlberg, H.; +1 Authors

    This dataset contains the distinct architectonic Area OP2 (POperc) in the MNI Colin 27 and MNI ICBM 152 reference spaces. As part of the Julich-Brain 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. 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 OP2 (POperc). The probability map of Area OP2 (POperc) 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 OP2 (POperc): Eickhoff et al. (2018) [Data set, v9.2] [DOI: 10.25493/F8W5-HNB](https://doi.org/10.25493%2FF8W5-HNB) Eickhoff et al. (2020) [Data set, v11.0] [DOI: 10.25493/SDW0-YEZ](https://doi.org/10.25493%2FSDW0-YEZ) The most probable delineation of Area OP2 (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)

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    EBRAINS
    Dataset . 2019
    License: CC BY NC SA
    Data sources: Sygma; Datacite
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      EBRAINS
      Dataset . 2019
      License: CC BY NC SA
      Data sources: Sygma; Datacite
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    Authors: Rottschy, C.; Eickhoff, S. B.; Schleicher, A.; Mohlberg, H.; +3 Authors

    This dataset contains the distinct probabilistic cytoarchitectonic map of Area hOc3v (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 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 hOc3v (LingG). The probability map of Area hOc3v (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 hOc3v (LingG): Rottschy et al. (2019) [Data set, v3.4] [DOI: 10.25493/E5E8-1VV](https://doi.org/10.25493%2FE5E8-1VV) The most probable delineation of Area hOc3v (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)

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    EBRAINS
    Dataset . 2018
    License: CC BY NC SA
    Data sources: Sygma; Datacite
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      EBRAINS
      Dataset . 2018
      License: CC BY NC SA
      Data sources: Sygma; Datacite
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    Authors: Armendariz, Marcelo; Ban, Hiroshi; Welchman, Andrew E; Vanduffel, Wim;

    Electrophysiological evidence suggested primarily the involvement of area MT in depth cue integration in macaques, as opposed to human imaging data pinpointing area V3B/KO. To clarify this conundrum, we decoded monkey fMRI responses evoked by stimuli signaling near or far depths defined by binocular disparity, relative motion and their combination, and we compared results with those from an identical experiment previously performed in humans.Responses in macaque area MT are more discriminable when two cues concurrently signal depth, and information provided by one cue is diagnostic of depth indicated by the other. This suggests that monkey area MT computes fusion of disparity and motion depth signals, exactly as shown for human area V3B/KO. Hence, these data reconcile previously reported discrepancies between depth processing in human and monkey by showing the involvement of the dorsal stream in depth cue integration using the same technique, despite the engagement of different regions. data describing fig 1-8 and sfig 1-12data.zip

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    DANS-EASY
    Dataset . 2019
    Data sources: B2FIND
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    ZENODO; DRYAD
    Dataset . 2019
    License: CC 0
    Data sources: Datacite; ZENODO
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      DANS-EASY
      Dataset . 2019
      Data sources: B2FIND
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      ZENODO; DRYAD
      Dataset . 2019
      License: CC 0
      Data sources: Datacite; ZENODO
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    Authors: Palomero-Gallagher, N.; Zilles, K.;

    This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters of CA, stratum cellulare (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_** \| _[3H]AMPA_ **kainate** \| **_glutamate_** \| _[3H]kainate_ **NMDA** \| **_glutamate_** \| _[3H]MK-801_ **GABAA** \| **_GABA_** \| _[3H]muscimol_ **muscarinic M1** \| **_acetylcholine_** \| _[3H]pirenzepine_ **muscarinic M2** \| **_acetylcholine_** \| _[3H]oxotremorine-M_ **α1** \| **_noradrenalin/norepinephrine_** \| _[3H]prazosin_ **α2** \| **_noradrenalin/norepinephrine_** \| _[3H]UK-14,304_ **5-HT1A** \| **_serotonin_** \| _[3H]8-OH-DPAT_ **5-HT2** \| **_serotonin_** \| _[3H]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

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    EBRAINS
    Dataset . 2020
    License: CC BY NC SA
    Data sources: Sygma; Datacite
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    EBRAINS
    Dataset . 2020
    License: CC BY NC SA
    Data sources: Sygma; Datacite
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      EBRAINS
      Dataset . 2020
      License: CC BY NC SA
      Data sources: Sygma; Datacite
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      EBRAINS
      Dataset . 2020
      License: CC BY NC SA
      Data sources: Sygma; Datacite