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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 reports the results of changing the priors on measurement noise when characterising group differences for both spectral and stochastic DCMs. The left column shows the Bayesian parameter averages of the differences for spectral DCM and the right column for stochastic DCM — using the same format as in the previous figures. For these results, we kept the prior covariance of the (log) precision parameters constant whilst varying the prior expectation of (log) precision parameters within the range of 2 and 10 with a step size of 2 (except the value of 6 for which the results are already reported in Fig. 6).
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f0035: This figure reports the results of changing the priors on measurement noise when characterising group differences for both spectral and stochastic DCMs. The left column shows the Bayesian parameter averages of the differences for spectral DCM and the right column for stochastic DCM — using the same format as in the previous figures. For these results, we kept the prior covariance of the (log) precision parameters constant whilst varying the prior expectation of (log) precision parameters within the range of 2 and 10 with a step size of 2 (except the value of 6 for which the results are already reported in Fig. 6).

Mentions: We further investigated what effect changing the priors on measurement noise has when characterising group differences. The more precise belief that measurement noise is small may cause stochastic DCM to increase estimated neuronal fluctuations, which may shift parameter estimates towards their true values — and make the performance of the stochastic DCM approach that of spectral DCM. The results in Fig. 6 were obtained with a default value of 6 for the prior expectation of (log) noise precision and 1/128 for its prior covariance. To evaluate the effect of measurement noise on the group differences we kept the (hyper) prior covariance constant at 1/128 whilst varying the (hyper) prior expectation from 2 to 10 in steps of 2. The results are reported in Fig. 7. We notice that increasing the expected noise (log) precision provided increasingly accurate estimates for spectral DCM, whereas the most accurate estimates for stochastic DCM were recovered whilst using the default value of 6.


Construct validation of a DCM for resting state fMRI.

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

This figure reports the results of changing the priors on measurement noise when characterising group differences for both spectral and stochastic DCMs. The left column shows the Bayesian parameter averages of the differences for spectral DCM and the right column for stochastic DCM — using the same format as in the previous figures. For these results, we kept the prior covariance of the (log) precision parameters constant whilst varying the prior expectation of (log) precision parameters within the range of 2 and 10 with a step size of 2 (except the value of 6 for which the results are already reported in Fig. 6).
© Copyright Policy - CC BY
Related In: Results  -  Collection

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

f0035: This figure reports the results of changing the priors on measurement noise when characterising group differences for both spectral and stochastic DCMs. The left column shows the Bayesian parameter averages of the differences for spectral DCM and the right column for stochastic DCM — using the same format as in the previous figures. For these results, we kept the prior covariance of the (log) precision parameters constant whilst varying the prior expectation of (log) precision parameters within the range of 2 and 10 with a step size of 2 (except the value of 6 for which the results are already reported in Fig. 6).
Mentions: We further investigated what effect changing the priors on measurement noise has when characterising group differences. The more precise belief that measurement noise is small may cause stochastic DCM to increase estimated neuronal fluctuations, which may shift parameter estimates towards their true values — and make the performance of the stochastic DCM approach that of spectral DCM. The results in Fig. 6 were obtained with a default value of 6 for the prior expectation of (log) noise precision and 1/128 for its prior covariance. To evaluate the effect of measurement noise on the group differences we kept the (hyper) prior covariance constant at 1/128 whilst varying the (hyper) prior expectation from 2 to 10 in steps of 2. The results are reported in Fig. 7. We notice that increasing the expected noise (log) precision provided increasingly accurate estimates for spectral DCM, whereas the most accurate estimates for stochastic DCM were recovered whilst using the default value of 6.

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