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339 Research products

  • Neuroinformatics
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Johnson, Joseph F.; Belyk, Michel; Schwartze, Michael; Pinheiro, Ana P.; +1 Authors

    Self-voice attribution can become difficult when voice characteristics are ambiguous, but functional magnetic resonance imaging (fMRI) investigations of such ambiguity are sparse. We utilized voice-morphing (self-other) to manipulate (un-) certainty in self-voice attribution in a button-press paradigm. This allowed investigating how levels of self-voice certainty alter brain activation in brain regions monitoring voice identity and unexpected changes in voice playback quality. FMRI results confirmed a self-voice suppression effect in the right anterior superior temporal gyrus (aSTG) when self-voice attribution was unambiguous. Although the right inferior frontal gyrus (IFG) was more active during a self-generated compared to a passively heard voice, the putative role of this region in detecting unexpected self-voice changes during the action was demonstrated only when hearing the voice of another speaker and not when attribution was uncertain. Further research on the link between right aSTG and IFG is required and may establish a threshold monitoring voice identity in action. The current results have implications for a better understanding of the altered experience of self-voice feedback in auditory verbal hallucinations.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao DataverseNLarrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    DataverseNL
    Dataset . 2022
    Data sources: DataverseNL
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    NARCIS; DataverseNL
    Dataset . 2022
    Data sources: Datacite; NARCIS
    DataverseNL
    Dataset . 2022
    Data sources: B2FIND
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao DataverseNLarrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      DataverseNL
      Dataset . 2022
      Data sources: DataverseNL
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      NARCIS; DataverseNL
      Dataset . 2022
      Data sources: Datacite; NARCIS
      DataverseNL
      Dataset . 2022
      Data sources: B2FIND
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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: Senden, Mario; Reuter, Niels; van den Heuvel, Martijn P.; Goebel, Rainer; +2 Authors

    Higher cognition may require the globally coordinated integration of specialized brain regions into functional networks. A collection of structural cortical hubs—referred to as the rich club—has been hypothesized to support task-specific functional integration. In the present paper, we use a whole-cortex model to estimate directed interactions between 68 cortical regions from functional magnetic resonance imaging activity for four different tasks (reflecting different cognitive domains) and resting state. We analyze the state-dependent input and output effective connectivity (EC) of the structural rich club and relate these to whole-cortex dynamics and network reconfigurations. We find that the cortical rich club exhibits an increase in outgoing EC during task performance as compared with rest while incoming connectivity remains constant. Increased outgoing connectivity targets a sparse set of peripheral regions with specific regions strongly overlapping between tasks. At the same time, community detection analyses reveal massive reorganizations of interactions among peripheral regions, including those serving as target of increased rich club output. This suggests that while peripheral regions may play a role in several tasks, their concrete interplay might nonetheless be task-specific. Furthermore, we observe that whole-cortex dynamics are faster during task as compared with rest. The decoupling effects usually accompanying faster dynamics appear to be counteracted by the increased rich club outgoing EC. Together our findings speak to a gating mechanism of the rich club that supports fast-paced information exchange among relevant peripheral regions in a task-specific and goal-directed fashion, while constantly listening to the whole network.,DATA_TASK_3DMOV_HP_CSF_WDBriefly, data comes from five functional runs consisting of a resting-state measurement (eyes closed), four individual task measurements including a visual n-back (n=2) task (Kirchner, 1958), the Eriksen flanker task (Eriksen & Eriksen, 1974), a mental rotation task (Shepard & Metzler, 1971), and a verbal odd-man-out task (Flowers & Robertson, 1985). All runs comprise 192 data points with tasks being continuously performed during this period. For the n-back and flanker task, stimuli were presented at a rate of 0.5 Hz; for the mental rotation and odd-man out tasks they were presented at a rate of 0.25 Hz. Task sequence was counterbalanced across participants with the exception that the resting state functional run was always acquired first to prevent carry-over effects (Grigg & Grady, 2010). The data were acquired using a 3 Tesla Siemens Prisma Fit (upgraded Tim Trio) scanner and a 64-channel head coil. Initial preprocessing was performed using BrainVoyager QX (v2.6; Brain Innovation, Maastricht, the Netherlands). This includes slice scan time correction, 3D-motion correction, high-pass filtering with a frequency cutoff of .01 Hz, and registration of functional and anatomical images. Subsequently, using MATLAB (2013a, The MathWorks,Natick, MA), signals were cleaned by performing wavelet despiking (Patel & Bullmore, 2015) and regressing out a global noise signal given by the first principal component of signals observed within the cerebrospinal fluid of the ventricles. Next, voxels were uniquely assigned to one of the 68 cortical ROIs specified by the DK atlas and an average BOLD time-series was computed for each region as the mean time-series over all voxels of that region., CC0 1.0

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    DANS-EASY
    Dataset . 2017
    Data sources: B2FIND
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    DRYAD; NARCIS
    Dataset . 2018
    License: CC 0
    Data sources: Datacite; NARCIS
    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 . 2018
    License: CC 0
    Data sources: ZENODO
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      DANS-EASY
      Dataset . 2017
      Data sources: B2FIND
      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/
      DRYAD; NARCIS
      Dataset . 2018
      License: CC 0
      Data sources: Datacite; NARCIS
      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 . 2018
      License: CC 0
      Data sources: 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: Schmidt, Ruben; LaFleur, Karl; Reus, Marcel De; Berg, Leonard Van Den; +1 Authors

    Figure S8. Global synchrony progression in the macaque. The order parameters r and r link for the macaque model network progressed in a manner similar to the human network (Figure 2), displaying a modular and a whole brain synchrony state separated by a critical regime.

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    Image . 2015
    License: CC BY
    Data sources: Datacite
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    Image . 2015
    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/
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      Image . 2015
      License: CC BY
      Data sources: Datacite
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      Image . 2015
      License: CC BY
      Data sources: Datacite
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  • Authors: NA10 Collaboration;

    PLAB=194 GEV/C. DATA FROM NA10 COLLABORATION AT CERN. MASS OF MUON PAIRS & gt; 4.07 GEV. UPDATE (04 DEC 2018): Added Tables 11-19 (194 GeV) and Tables 20-30 (286 GeV) containing re-analysed data by the NA10 experimenters using a better estimate of Fermi motion effects. These re-analysed data were obtained by private communication and previously published in W.J. Stirling and M.R. Whalley, "A compilation of Drell-Yan cross sections", J.Phys. G19 (1993) D1-D102 (https://doi.org/10.1088/0954-3899/19/D/001); numbers taken from http://hepdata.cedar.ac.uk/review/dy/na10.shtml . Thanks to Ivan Novikov for pointing out the omission from HEPData of these re-analysed data. The cross section ${\rm d}^2\sigma/{\rm d}\sqrt{\tau}{\rm d}x$ integrated over each $\sqrt{\tau}$-$x_F$ cell as a function of $x_F$ for $\sqrt{\tau}$ = 0.21-0.24. The $\Upsilon$ region has been excluded. The integrated luminosity is $L = (8.58 \pm 0.53)\times 10^{37}$ [cm$^2$/W nucleus]$^{-1}$. Note that these data have been re-analysed by the NA10 experimenters using a better estimate of Fermi motion effects (see Tables 11-19 of this record).

    HEPDataarrow_drop_down
    HEPData
    Dataset . 2018
    Data sources: Datacite
    HEPData
    Dataset . 1970
    Data sources: Datacite
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      HEPDataarrow_drop_down
      HEPData
      Dataset . 2018
      Data sources: Datacite
      HEPData
      Dataset . 1970
      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: D’Amato, Alfonsina; Mannelli, Lorenzo Di Cesare; Lucarini, Elena; Man, Angela L.; +15 Authors

    Additional file 11. Metabolome-proteome correlation (Pearson) .

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    figshare
    Dataset . 2020
    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/
    figshare
    Dataset . 2020
    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/ figsharearrow_drop_down
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      figshare
      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: L3 Collaboration;

    CERN-LEP. Comprehensive study of hadronic event shapes and distributions in E+ E- interactions from collision energies from 91 to 209 GeV. These data update and supersede many of the L3 results published previously.. This section contains the 2,3,4 and 5 jet fractions for the JADE, Durham(KT) and Cambridge algorithms as a function of their respective jet resolution parameters (YCUT). CERN-LEP. Comprehensive study of hadronic event shapes and distributions in E+ E- interactions from collision energies from 91 to 209 GeV. These data update and supersede many of the L3 results published previously.. This section contains the distributions of the event shape variables THRUST, Heavy Jet Mass (RHO), Total and Wide Jet Broadening (BT and BW) and C- and D-Parameters (C-PARAM and D-PARAM) plus their first and second moments (MEAN and DISPERSION). CERN-LEP. Comprehensive study of hadronic event shapes and distributions in E+ E- interactions from collision energies from 91 to 209 GeV. These data update and supersede many of the L3 results published previously.. This section contains the charged particle multiplicity distributions and the LN(1/X) distributions plus their mean values and dispersions. Jet fractions using the JADE algorithm as a function of the jet resolution parameter YCUT at c.m. energy 161.3 GeV.

    HEPDataarrow_drop_down
    HEPData
    Dataset . 2004
    Data sources: Datacite
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      HEPDataarrow_drop_down
      HEPData
      Dataset . 2004
      Data sources: Datacite
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  • Authors: L3 Collaboration;

    CERN-LEP. Measurement of distributions of event shape variables in hadronicevents produced in E+ E- interactions at c.m. energies from 30 to 189 GeV. Fits to the moments of the event shape distributions are used to deduce values for t he strong coupling constant ALPHA-S at each of the energies. The measurements demonstrate the running of ALPHA-S as expected in QCD with a value at the Z0 mass of 0.1215 +- 0.0012 (EXP) +- 0.0061 (THEORY). Numerical values of the two event shape distributions supplied by D. Duchesneau. Distribution for THRUST at c.m. energy 189 GeV.

    HEPDataarrow_drop_down
    HEPData
    Dataset . 2000
    Data sources: Datacite
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      HEPData
      Dataset . 2000
      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: van der Burgh, Hannelore;

    Objective: To understand the progressive nature of amyotrophic lateral sclerosis (ALS) by investigating differential brain patterns of gray and white matter involvement in clinically or genetically defined subgroups of patients using cross-sectional, longitudinal and multimodal MRI. Methods: We assessed cortical thickness, subcortical volumes and white matter connectivity from T1-weighted and diffusion-weighted MRI in 292 ALS patients (follow-up: n=150) and 156 controls (follow-up: n=72). Linear mixed-effects models were used to assess changes in structural brain measurements over time in patients compared to controls. Results: Patients with a C9orf72 mutation (n=24) showed widespread gray and white matter involvement at baseline, and extensive loss of white matter integrity in the connectome over time. In C9orf72-negative patients, we detected cortical thinning of motor and frontotemporal regions, and loss of white matter integrity of connections linked to the motor cortex. Spinal-onset patients displayed widespread white matter involvement at baseline and gray matter atrophy over time, whereas bulbar-onset patients started out with prominent gray matter involvement. Patients with unaffected cognition or behavior displayed predominantly motor system involvement, while widespread cerebral changes, including frontotemporal regions with progressive white matter involvement over time, were associated with impaired behavior or cognition. Progressive loss of gray and white matter integrity typically occurred in patients with shorter disease durations (<13 months), independent of progression rate. Conclusions: Heterogeneity of phenotype and C9orf72 genotype relates to distinct patterns of cerebral degeneration. We demonstrate that imaging studies have the potential to monitor disease progression and early intervention may be required to limit cerebral degeneration. Supplemental Data corresponding to research paper 'A multimodal longitudinal study of structural brain involvement in ALS' It contains appendices, supplemental figures and supplemental tables for this paper.

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    ZENODO
    Dataset . 2020
    License: CC 0
    Data sources: ZENODO
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      ZENODO
      Dataset . 2020
      License: CC 0
      Data sources: ZENODO
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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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: D’Amato, Alfonsina; Mannelli, Lorenzo Di Cesare; Lucarini, Elena; Man, Angela L.; +15 Authors

    Additional file 14. GFAP western blotting. Polyacrylamide (10%) gel stained with blue Coomassie with a representative image of molecular weight marker with relevant kDa (a). GAPDH visualized bands and merged with nitrocellulose membrane (b). GFAP visualized bands and merged with nitrocellulose membrane (c). In (d) a representative histogram shows levels of analysed protein both in FMT-Y and FMT-A treated and control animals. Lane 1 (positive control, DITNC1 astrocyte-derived cell line); lane 2 (negative control, BV-2 microglial cell line); lane 3 (aged mouse hippocampal proteins); lane 4 (adult mouse hippocampal protein); lane 5 (MT-aged hippocampal proteins); lane 6 (MT-adult hippocampal proteins). GFAP protein was detected approximately at 55 kDa (right blots). GAPDH (37 kDa) was used as housekeeping (left panel). Molecular weight (mw) used was SHARPMASS VII.

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    Image . 2020
    License: CC BY
    Data sources: Datacite
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    Image . 2020
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    Data sources: Datacite
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  • Authors: L3 Collaboration;

    CERN-LEP. Comprehensive study of hadronic event shapes and distributions in E+ E- interactions from collision energies from 91 to 209 GeV. These data update and supersede many of the L3 results published previously.. This section contains the 2,3,4 and 5 jet fractions for the JADE, Durham(KT) and Cambridge algorithms as a function of their respective jet resolution parameters (YCUT). CERN-LEP. Comprehensive study of hadronic event shapes and distributions in E+ E- interactions from collision energies from 91 to 209 GeV. These data update and supersede many of the L3 results published previously.. This section contains the distributions of the event shape variables THRUST, Heavy Jet Mass (RHO), Total and Wide Jet Broadening (BT and BW) and C- and D-Parameters (C-PARAM and D-PARAM) plus their first and second moments (MEAN and DISPERSION). CERN-LEP. Comprehensive study of hadronic event shapes and distributions in E+ E- interactions from collision energies from 91 to 209 GeV. These data update and supersede many of the L3 results published previously.. This section contains the charged particle multiplicity distributions and the LN(1/X) distributions plus their mean values and dispersions. Differential distributions for the C-Parameter.

    HEPDataarrow_drop_down
    HEPData
    Dataset . 2004
    Data sources: Datacite
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      HEPData
      Dataset . 2004
      Data sources: Datacite
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339 Research products
  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Johnson, Joseph F.; Belyk, Michel; Schwartze, Michael; Pinheiro, Ana P.; +1 Authors

    Self-voice attribution can become difficult when voice characteristics are ambiguous, but functional magnetic resonance imaging (fMRI) investigations of such ambiguity are sparse. We utilized voice-morphing (self-other) to manipulate (un-) certainty in self-voice attribution in a button-press paradigm. This allowed investigating how levels of self-voice certainty alter brain activation in brain regions monitoring voice identity and unexpected changes in voice playback quality. FMRI results confirmed a self-voice suppression effect in the right anterior superior temporal gyrus (aSTG) when self-voice attribution was unambiguous. Although the right inferior frontal gyrus (IFG) was more active during a self-generated compared to a passively heard voice, the putative role of this region in detecting unexpected self-voice changes during the action was demonstrated only when hearing the voice of another speaker and not when attribution was uncertain. Further research on the link between right aSTG and IFG is required and may establish a threshold monitoring voice identity in action. The current results have implications for a better understanding of the altered experience of self-voice feedback in auditory verbal hallucinations.

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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    DataverseNL
    Dataset . 2022
    Data sources: DataverseNL
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    NARCIS; DataverseNL
    Dataset . 2022
    Data sources: Datacite; NARCIS
    DataverseNL
    Dataset . 2022
    Data sources: B2FIND
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao DataverseNLarrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      DataverseNL
      Dataset . 2022
      Data sources: DataverseNL
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      NARCIS; DataverseNL
      Dataset . 2022
      Data sources: Datacite; NARCIS
      DataverseNL
      Dataset . 2022
      Data sources: B2FIND
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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: Senden, Mario; Reuter, Niels; van den Heuvel, Martijn P.; Goebel, Rainer; +2 Authors

    Higher cognition may require the globally coordinated integration of specialized brain regions into functional networks. A collection of structural cortical hubs—referred to as the rich club—has been hypothesized to support task-specific functional integration. In the present paper, we use a whole-cortex model to estimate directed interactions between 68 cortical regions from functional magnetic resonance imaging activity for four different tasks (reflecting different cognitive domains) and resting state. We analyze the state-dependent input and output effective connectivity (EC) of the structural rich club and relate these to whole-cortex dynamics and network reconfigurations. We find that the cortical rich club exhibits an increase in outgoing EC during task performance as compared with rest while incoming connectivity remains constant. Increased outgoing connectivity targets a sparse set of peripheral regions with specific regions strongly overlapping between tasks. At the same time, community detection analyses reveal massive reorganizations of interactions among peripheral regions, including those serving as target of increased rich club output. This suggests that while peripheral regions may play a role in several tasks, their concrete interplay might nonetheless be task-specific. Furthermore, we observe that whole-cortex dynamics are faster during task as compared with rest. The decoupling effects usually accompanying faster dynamics appear to be counteracted by the increased rich club outgoing EC. Together our findings speak to a gating mechanism of the rich club that supports fast-paced information exchange among relevant peripheral regions in a task-specific and goal-directed fashion, while constantly listening to the whole network.,DATA_TASK_3DMOV_HP_CSF_WDBriefly, data comes from five functional runs consisting of a resting-state measurement (eyes closed), four individual task measurements including a visual n-back (n=2) task (Kirchner, 1958), the Eriksen flanker task (Eriksen & Eriksen, 1974), a mental rotation task (Shepard & Metzler, 1971), and a verbal odd-man-out task (Flowers & Robertson, 1985). All runs comprise 192 data points with tasks being continuously performed during this period. For the n-back and flanker task, stimuli were presented at a rate of 0.5 Hz; for the mental rotation and odd-man out tasks they were presented at a rate of 0.25 Hz. Task sequence was counterbalanced across participants with the exception that the resting state functional run was always acquired first to prevent carry-over effects (Grigg & Grady, 2010). The data were acquired using a 3 Tesla Siemens Prisma Fit (upgraded Tim Trio) scanner and a 64-channel head coil. Initial preprocessing was performed using BrainVoyager QX (v2.6; Brain Innovation, Maastricht, the Netherlands). This includes slice scan time correction, 3D-motion correction, high-pass filtering with a frequency cutoff of .01 Hz, and registration of functional and anatomical images. Subsequently, using MATLAB (2013a, The MathWorks,Natick, MA), signals were cleaned by performing wavelet despiking (Patel & Bullmore, 2015) and regressing out a global noise signal given by the first principal component of signals observed within the cerebrospinal fluid of the ventricles. Next, voxels were uniquely assigned to one of the 68 cortical ROIs specified by the DK atlas and an average BOLD time-series was computed for each region as the mean time-series over all voxels of that region., CC0 1.0

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    DANS-EASY
    Dataset . 2017
    Data sources: B2FIND
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    DRYAD; NARCIS
    Dataset . 2018
    License: CC 0
    Data sources: Datacite; NARCIS
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    ZENODO
    Dataset . 2018
    License: CC 0
    Data sources: ZENODO
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      DANS-EASY
      Dataset . 2017
      Data sources: B2FIND
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      DRYAD; NARCIS
      Dataset . 2018
      License: CC 0
      Data sources: Datacite; NARCIS
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      ZENODO
      Dataset . 2018
      License: CC 0
      Data sources: 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: Schmidt, Ruben; LaFleur, Karl; Reus, Marcel De; Berg, Leonard Van Den; +1 Authors

    Figure S8. Global synchrony progression in the macaque. The order parameters r and r link for the macaque model network progressed in a manner similar to the human network (Figure 2), displaying a modular and a whole brain synchrony state separated by a critical regime.

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    Image . 2015
    License: CC BY
    Data sources: Datacite
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    Image . 2015
    License: CC BY
    Data sources: Datacite
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      Image . 2015
      License: CC BY
      Data sources: Datacite
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  • Authors: NA10 Collaboration;

    PLAB=194 GEV/C. DATA FROM NA10 COLLABORATION AT CERN. MASS OF MUON PAIRS & gt; 4.07 GEV. UPDATE (04 DEC 2018): Added Tables 11-19 (194 GeV) and Tables 20-30 (286 GeV) containing re-analysed data by the NA10 experimenters using a better estimate of Fermi motion effects. These re-analysed data were obtained by private communication and previously published in W.J. Stirling and M.R. Whalley, "A compilation of Drell-Yan cross sections", J.Phys. G19 (1993) D1-D102 (https://doi.org/10.1088/0954-3899/19/D/001); numbers taken from http://hepdata.cedar.ac.uk/review/dy/na10.shtml . Thanks to Ivan Novikov for pointing out the omission from HEPData of these re-analysed data. The cross section ${\rm d}^2\sigma/{\rm d}\sqrt{\tau}{\rm d}x$ integrated over each $\sqrt{\tau}$-$x_F$ cell as a function of $x_F$ for $\sqrt{\tau}$ = 0.21-0.24. The $\Upsilon$ region has been excluded. The integrated luminosity is $L = (8.58 \pm 0.53)\times 10^{37}$ [cm$^2$/W nucleus]$^{-1}$. Note that these data have been re-analysed by the NA10 experimenters using a better estimate of Fermi motion effects (see Tables 11-19 of this record).

    HEPDataarrow_drop_down
    HEPData
    Dataset . 2018
    Data sources: Datacite
    HEPData
    Dataset . 1970
    Data sources: Datacite
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