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Empirical Bayes for Group (DCM) Studies: A Reproducibility Study

Overview of attention for article published in Frontiers in Human Neuroscience, December 2015
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  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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Title
Empirical Bayes for Group (DCM) Studies: A Reproducibility Study
Published in
Frontiers in Human Neuroscience, December 2015
DOI 10.3389/fnhum.2015.00670
Pubmed ID
Authors

Vladimir Litvak, Marta Garrido, Peter Zeidman, Karl Friston

Abstract

This technical note addresses some key reproducibility issues in the dynamic causal modelling of group studies of event related potentials. Specifically, we address the reproducibility of Bayesian model comparison (and inferences about model parameters) from three important perspectives namely: (i) reproducibility with independent data (obtained by averaging over odd and even trials); (ii) reproducibility over formally distinct models (namely, classic ERP and canonical microcircuit or CMC models); and (iii) reproducibility over inversion schemes (inversion of the grand average and estimation of group effects using empirical Bayes). Our hope was to illustrate the degree of reproducibility one can expect from DCM when analysing different data, under different models with different analyses.

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X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
Australia 1 1%
Unknown 73 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 33%
Researcher 10 13%
Student > Master 10 13%
Professor 4 5%
Student > Bachelor 3 4%
Other 11 15%
Unknown 12 16%
Readers by discipline Count As %
Neuroscience 23 31%
Psychology 10 13%
Engineering 7 9%
Agricultural and Biological Sciences 4 5%
Medicine and Dentistry 4 5%
Other 8 11%
Unknown 19 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 September 2023.
All research outputs
#7,013,982
of 24,417,958 outputs
Outputs from Frontiers in Human Neuroscience
#2,866
of 7,472 outputs
Outputs of similar age
#106,146
of 399,902 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#57
of 143 outputs
Altmetric has tracked 24,417,958 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 7,472 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.9. This one has gotten more attention than average, scoring higher than 61% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 399,902 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 143 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.