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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: David, Olivier; Lemaréchal, Jean-Didier; Jedynak, Maciej; Trebaul, Lena; +39 Authors

    F-TRACT atlas release - December 2021 ====================================== The F-TRACT atlas is provided as .csv (comma-separated values) files that can be read in any table editor. In addition, we provide a Matlab routine allowing to read the features of the atlas as Matlab variables. The atlas is provided for free use for research use only, with limited accuracy, which hopefully will improve with subsequent releases. Please cite David et al. (2013) Probabilistic functional tractography of the human cortex, NeuroImage, and Trebaul et al. (2018) Probabilistic functional tractography of the human cortex revisited, NeuroImage, Lemarechal et al. (2022) A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials, Brain, when using the F-TRACT atlas. - f-tract_v2112 : Connectivity probability as well as features describing fibers biophysical properties, estimated from CCEP data recorded in 780 patients, in the AAL, AICHA, Brodmann, Freesurfer, Hammers, HCP-MMP1, Lausanne2008 (resolutions 33, 60, 125, 250, 500) and MarsAtlas parcellation schemes. The CCEP features are: peak and onset latency (LatStart), amplitude, duration, integral, velocity estimated from the onset latency and the fibers distance between the parcels and axonal conduction delays. Synaptic excitatory and inhibitory delays are also provided for each parcel. All features have been estimated separately for patients younger than 15 y.o. (group "0-15") and patients older than 15 y.o. (group "15-100"). - Features maps : Images representing the connectivity probability and response features for all the regions in the Lausanne2008-60 parcellation.

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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/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
    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/
    ZENODO
    Dataset . 2021
    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/ ZENODOarrow_drop_down
      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/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
      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/
      ZENODO
      Dataset . 2021
      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: Voutsa, Venetia; Battaglia, Demian; Bracken, Louise J.; Brovelli, Andrea; +21 Authors

    The relationship of network structure and dynamics is one of the most extensively investigated problems in the theory of complex systems of the last years. Understanding this relationship is of relevance to a range of disciplines—from Neuroscience to Geomorphology. A major strategy of investigating this relationship is the quantitative comparison of a representation of network architecture (structural connectivity) with a (network) representation of the dynamics (functional connectivity). Here, we show that one can distinguish two classes of functional connectivity—one based on simultaneous activity (co-activity) of nodes, the other based on sequential activity of nodes. We delineate these two classes in different categories of dynamical processes—excitations, regular and chaotic oscillators—and provide examples for SC/FC correlations of both classes in each of these models. We expand the theoretical view of the SC/FC relationships, with conceptual instances of the SC and the two classes of FC for various application scenarios in Geomorphology, Ecology, Systems Biology, Neuroscience and Socio-Ecological Systems. Seeing the organisation of dynamical processes in a network either as governed by co-activity or by sequential activity allows us to bring some order in the myriad of observations relating structure and function of complex networks.

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    Image . 2021
    License: CC BY
    Data sources: Datacite
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    Image . 2021
    License: CC BY
    Data sources: Datacite
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    Image . 2021
    License: CC BY
    Data sources: Datacite
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    Image . 2021
    License: CC BY
    Data sources: Datacite
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      Image . 2021
      License: CC BY
      Data sources: Datacite
      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/
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      Image . 2021
      License: CC BY
      Data sources: Datacite
      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/
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      Image . 2021
      License: CC BY
      Data sources: Datacite
      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/
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      Image . 2021
      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: Agosti, Donat; Benichou, Laurence; Addink, Wouter; Arvanitidis, Christos; +15 Authors

    Overview table of recommendations

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    ZENODO
    Dataset . 2022
    License: CC 0
    Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC 0
      Data sources: ZENODO
  • 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: Thiyagaragan Achariyar; Baoman Li; Weiguo Peng; Verghese, Philip; +9 Authors

    Lenti-EGFP effectively transduced the choroid plexus. A) Lenti-EGFP unilateral injected into the lateral ventricle preferentially transduced the choroid plexus and ependymal layer but not brain parenchyma at 1 week. DAPI (blue); EGFP (green). B-E) Lenti-EGFP expression in the choroid plexus at 1 week. Images are DAPI (blue; B), AQP1 (red; C); EGFP (green; D) and merged images (E). Scale bar 50 μm. Intraventricular injection of 3 μL lenti-EGFP (1012 TU/ml). N = 5. (EPS 75360 kb)

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    Image . 2016
    License: CC BY
    Data sources: Datacite
    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/
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    Image . 2016
    License: CC BY
    Data sources: Datacite
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      Image . 2016
      License: CC BY
      Data sources: Datacite
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      Image . 2016
      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: 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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    https://doi.org/10.25493/3k39-...
    Dataset . 2018
    License: CC BY NC SA
    Data sources: Datacite; Sygma
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      https://doi.org/10.25493/3k39-...
      Dataset . 2018
      License: CC BY NC SA
      Data sources: Datacite; Sygma
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    Authors: Terock, J.; Frenzel, S.; Wittfeld, K.; Klinger-König, J.; +5 Authors

    Background: Alexithymia is a personality trait characterized by difficulties in identifying and describing emotions and associated with various psychiatric disorders. Neuroimaging studies found evidence for morphological and functional brain alterations in alexithymic subjects. However, the neurobiological mechanisms underlying alexithymia remain incompletely understood. Methods: We study the association of alexithymia with cortical correlation networks in a large community-dwelling sample of the Study of Health in Pomerania. Our analysis includes data of n = 2,199 individuals (49.4% females, age = 52.1 ± 13.6 years) which were divided into a low and high alexithymic group by a median split of the Toronto Alexithymia Scale. Cortical correlation networks were constructed based on the mean thicknesses of 68 regions, and differences in centralities were investigated. Results: We found a significantly increased centrality of the right paracentral lobule in the high alexithymia network after correction for multiple testing. Several other regions with motoric and sensory functions showed altered centrality on a nominally significant level. Conclusions: Finding increased centrality of the paracentral lobule, a brain area with sensory as well as motoric features and involvement in bowel and bladder voiding, may contribute to explain the association of alexithymia with functional somatic disorders and chronic pain syndromes.

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    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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    Authors: Amunts, K.; Mohlberg, H.; Bludau, S.; Pieperhoff, P.;

    This dataset contains the “GapMap Frontal to Temporal" 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 Temporal”. 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 Temporal” 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 Temporal” with progress in mapping. Other available data versions of Gap Frontal to Temporal: Amunts et al. (2020) [Data set, v9.0] [DOI: 10.25493/KRRN-E62](https://doi.org/10.25493%2FKRRN-E62) Amunts et al. (2020) [Data set, v9.1] [DOI: 10.25493/468P-A99](https://doi.org/10.25493%2F468P-A99) The most probable delineation of GapMap Frontal to Temporal 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) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9)

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    https://doi.org/10.25493/dds9-...
    Dataset . 2020
    License: CC BY NC SA
    Data sources: Datacite; Sygma
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      https://doi.org/10.25493/dds9-...
      Dataset . 2020
      License: CC BY NC SA
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    Authors: Egloff, Willi; Agosti, Donat; Patterson, David; Hoffmann, Anke; +3 Authors

    EU BON milestone report MS841

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    ZENODO
    Dataset . 2016
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2016
      License: CC BY
      Data sources: ZENODO
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    Authors: Eickhoff, S. B.; Schleicher, A.; Amunts, K.; Mohlberg, H.; +1 Authors

    This dataset contains the distinct architectonic Area OP1 (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 OP1 (POperc). The probability map of Area OP1 (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 OP1 (POperc): Eickhoff et al. (2018) [Data set, v9.2] [DOI: 10.25493/HVH9-KBR](https://doi.org/10.25493%2FHVH9-KBR) Eickhoff et al. (2020) [Data set, v11.0] [DOI: 10.25493/9SP6-8HA](https://doi.org/10.25493%2F9SP6-8HA) The most probable delineation of Area OP1 (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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    https://doi.org/10.25493/sh37-...
    Dataset . 2019
    License: CC BY NC SA
    Data sources: Datacite; Sygma
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      https://doi.org/10.25493/sh37-...
      Dataset . 2019
      License: CC BY NC SA
      Data sources: Datacite; Sygma
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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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    https://doi.org/10.25493/5kbv-...
    Dataset . 2019
    License: CC BY NC SA
    Data sources: Datacite; Sygma
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      https://doi.org/10.25493/5kbv-...
      Dataset . 2019
      License: CC BY NC SA
      Data sources: Datacite; Sygma
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768 Research products
  • 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: David, Olivier; Lemaréchal, Jean-Didier; Jedynak, Maciej; Trebaul, Lena; +39 Authors

    F-TRACT atlas release - December 2021 ====================================== The F-TRACT atlas is provided as .csv (comma-separated values) files that can be read in any table editor. In addition, we provide a Matlab routine allowing to read the features of the atlas as Matlab variables. The atlas is provided for free use for research use only, with limited accuracy, which hopefully will improve with subsequent releases. Please cite David et al. (2013) Probabilistic functional tractography of the human cortex, NeuroImage, and Trebaul et al. (2018) Probabilistic functional tractography of the human cortex revisited, NeuroImage, Lemarechal et al. (2022) A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials, Brain, when using the F-TRACT atlas. - f-tract_v2112 : Connectivity probability as well as features describing fibers biophysical properties, estimated from CCEP data recorded in 780 patients, in the AAL, AICHA, Brodmann, Freesurfer, Hammers, HCP-MMP1, Lausanne2008 (resolutions 33, 60, 125, 250, 500) and MarsAtlas parcellation schemes. The CCEP features are: peak and onset latency (LatStart), amplitude, duration, integral, velocity estimated from the onset latency and the fibers distance between the parcels and axonal conduction delays. Synaptic excitatory and inhibitory delays are also provided for each parcel. All features have been estimated separately for patients younger than 15 y.o. (group "0-15") and patients older than 15 y.o. (group "15-100"). - Features maps : Images representing the connectivity probability and response features for all the regions in the Lausanne2008-60 parcellation.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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    Authors: Voutsa, Venetia; Battaglia, Demian; Bracken, Louise J.; Brovelli, Andrea; +21 Authors

    The relationship of network structure and dynamics is one of the most extensively investigated problems in the theory of complex systems of the last years. Understanding this relationship is of relevance to a range of disciplines—from Neuroscience to Geomorphology. A major strategy of investigating this relationship is the quantitative comparison of a representation of network architecture (structural connectivity) with a (network) representation of the dynamics (functional connectivity). Here, we show that one can distinguish two classes of functional connectivity—one based on simultaneous activity (co-activity) of nodes, the other based on sequential activity of nodes. We delineate these two classes in different categories of dynamical processes—excitations, regular and chaotic oscillators—and provide examples for SC/FC correlations of both classes in each of these models. We expand the theoretical view of the SC/FC relationships, with conceptual instances of the SC and the two classes of FC for various application scenarios in Geomorphology, Ecology, Systems Biology, Neuroscience and Socio-Ecological Systems. Seeing the organisation of dynamical processes in a network either as governed by co-activity or by sequential activity allows us to bring some order in the myriad of observations relating structure and function of complex networks.

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    Image . 2021
    License: CC BY
    Data sources: Datacite
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    Image . 2021
    License: CC BY
    Data sources: Datacite
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    Image . 2021
    License: CC BY
    Data sources: Datacite
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    Image . 2021
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    Data sources: Datacite
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      Image . 2021
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    Authors: Agosti, Donat; Benichou, Laurence; Addink, Wouter; Arvanitidis, Christos; +15 Authors

    Overview table of recommendations

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    ZENODO
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      ZENODO
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    Authors: Thiyagaragan Achariyar; Baoman Li; Weiguo Peng; Verghese, Philip; +9 Authors

    Lenti-EGFP effectively transduced the choroid plexus. A) Lenti-EGFP unilateral injected into the lateral ventricle preferentially transduced the choroid plexus and ependymal layer but not brain parenchyma at 1 week. DAPI (blue); EGFP (green). B-E) Lenti-EGFP expression in the choroid plexus at 1 week. Images are DAPI (blue; B), AQP1 (red; C); EGFP (green; D) and merged images (E). Scale bar 50 μm. Intraventricular injection of 3 μL lenti-EGFP (1012 TU/ml). N = 5. (EPS 75360 kb)

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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 obtai