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Construct validation of a DCM for resting state fMRI.

Razi A, Kahan J, Rees G, Friston KJ - Neuroimage (2014)

Bottom Line: Dynamic causal modelling (DCM) is a framework that allows for the identification of the causal (directed) connections among neuronal systems--known as effective connectivity.We also simulated group differences and compared the ability of spectral and stochastic DCMs to identify these differences.We show that spectral DCM was not only more accurate but also more sensitive to group differences.

View Article: PubMed Central - PubMed

Affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK; Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan. Electronic address: a.razi@ucl.ac.uk.

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This figure plots the distribution of the posterior expectations of the two schemes over subjects for the strongest connection from left IPC to right IPC (see Fig. 10). The posterior expectations were ranked in descending order. The upper left panel shows the posterior expectations (light grey bars) for the spectral DCM with superimposed confidence interval (pink bars). A similar plot for stochastic DCM is shown in the lower left panel. We also show scatter plot of the posterior expectations over subjects for the two schemes.
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f0055: This figure plots the distribution of the posterior expectations of the two schemes over subjects for the strongest connection from left IPC to right IPC (see Fig. 10). The posterior expectations were ranked in descending order. The upper left panel shows the posterior expectations (light grey bars) for the spectral DCM with superimposed confidence interval (pink bars). A similar plot for stochastic DCM is shown in the lower left panel. We also show scatter plot of the posterior expectations over subjects for the two schemes.

Mentions: Finally, to assess the comparative reproducibility of spectral and stochastic DCM estimates we examined the distribution of connection strengths (and their respective confidence intervals) for the most significant connection (from left to right IPC) over subjects — ranked from the highest to the lowest posterior expectation. The two distributions for spectral (upper left) and stochastic estimators (lower left) are shown in Fig. 11. It can be seen that there is a remarkable consistency over subjects for both estimators — except that one participant's connection was in the opposite direction for spectral DCM. Furthermore, again we see that stochastic estimators are shrunk towards the prior expectations (of zero) relative to the spectral estimators. We also included a scatter plot of the two distributions (right panel) over participants which showed high correlations over participants' estimates.


Construct validation of a DCM for resting state fMRI.

Razi A, Kahan J, Rees G, Friston KJ - Neuroimage (2014)

This figure plots the distribution of the posterior expectations of the two schemes over subjects for the strongest connection from left IPC to right IPC (see Fig. 10). The posterior expectations were ranked in descending order. The upper left panel shows the posterior expectations (light grey bars) for the spectral DCM with superimposed confidence interval (pink bars). A similar plot for stochastic DCM is shown in the lower left panel. We also show scatter plot of the posterior expectations over subjects for the two schemes.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4295921&req=5

f0055: This figure plots the distribution of the posterior expectations of the two schemes over subjects for the strongest connection from left IPC to right IPC (see Fig. 10). The posterior expectations were ranked in descending order. The upper left panel shows the posterior expectations (light grey bars) for the spectral DCM with superimposed confidence interval (pink bars). A similar plot for stochastic DCM is shown in the lower left panel. We also show scatter plot of the posterior expectations over subjects for the two schemes.
Mentions: Finally, to assess the comparative reproducibility of spectral and stochastic DCM estimates we examined the distribution of connection strengths (and their respective confidence intervals) for the most significant connection (from left to right IPC) over subjects — ranked from the highest to the lowest posterior expectation. The two distributions for spectral (upper left) and stochastic estimators (lower left) are shown in Fig. 11. It can be seen that there is a remarkable consistency over subjects for both estimators — except that one participant's connection was in the opposite direction for spectral DCM. Furthermore, again we see that stochastic estimators are shrunk towards the prior expectations (of zero) relative to the spectral estimators. We also included a scatter plot of the two distributions (right panel) over participants which showed high correlations over participants' estimates.

Bottom Line: Dynamic causal modelling (DCM) is a framework that allows for the identification of the causal (directed) connections among neuronal systems--known as effective connectivity.We also simulated group differences and compared the ability of spectral and stochastic DCMs to identify these differences.We show that spectral DCM was not only more accurate but also more sensitive to group differences.

View Article: PubMed Central - PubMed

Affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK; Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan. Electronic address: a.razi@ucl.ac.uk.

Show MeSH
Related in: MedlinePlus