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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 shows the posterior estimates that result from the Bayesian inversion of the simulated time series. The posterior means (grey bars) and 90% confidence intervals (pink bars) are shown with the true values (black bars) in the left column. The upper and lower panel reports spectral and stochastic DCMs respectively. The grey bars depict the posterior expectations of connections, where intrinsic (within region) or self-connections are parameterised in terms of their log scaling (such that a value of zero corresponds to a scaling of one). The extrinsic (between regions) connections are measured in Hz in the usual way. It can be seen that, largely, the true values fall within the Bayesian confidence intervals for spectral DCM but not for stochastic DCM. The right panel shows the same results but plotting the estimated connection strengths against their true values. For spectral DCM (resp. stochastic DCM), the blue (resp. cyan) circles correspond to extrinsic connections and the red (resp. magenta) circles to intrinsic connectivity.
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f0015: This figure shows the posterior estimates that result from the Bayesian inversion of the simulated time series. The posterior means (grey bars) and 90% confidence intervals (pink bars) are shown with the true values (black bars) in the left column. The upper and lower panel reports spectral and stochastic DCMs respectively. The grey bars depict the posterior expectations of connections, where intrinsic (within region) or self-connections are parameterised in terms of their log scaling (such that a value of zero corresponds to a scaling of one). The extrinsic (between regions) connections are measured in Hz in the usual way. It can be seen that, largely, the true values fall within the Bayesian confidence intervals for spectral DCM but not for stochastic DCM. The right panel shows the same results but plotting the estimated connection strengths against their true values. For spectral DCM (resp. stochastic DCM), the blue (resp. cyan) circles correspond to extrinsic connections and the red (resp. magenta) circles to intrinsic connectivity.

Mentions: The posterior estimates of the effective connectivity are shown in Fig. 3. Posterior expectations are presented as grey bars with pink bars indicating the 90% Bayesian confidence intervals. We have also superimposed the true connectivity as black bars for comparison. The upper left panel shows the posterior expectations for the spDCM inversion (shown as spectral in title), whilst the lower left panel shows the results for the stochastic scheme using generalised filtering. Clearly, the spectral DCM's estimates are very accurate, with most of the extrinsic connection strengths falling within 90% confidence intervals. The intrinsic connections (i.e. the self-connections) are modelled as a (log) scale parameter and have a prior mean of zero. These connections are still estimated with good accuracy showing around a 20% underestimation of the self-connectivity. The stochastic scheme also performed well, with estimates tending towards the true values but not as accurately as the deterministic (spectral) scheme. This reiterates the point that stochastic DCM can find it difficult to recover effective connectivity from data generated from graphs with reciprocal connectivity. We have also presented these results in a scatter plot to illustrate the relative accuracies of the spectral and stochastic estimates (the posterior estimates of spDCM are closer to the true parameters than those generated by the model based on sDCM). It can also be seen that the stochastic model underestimates the parameters, a behaviour which has previously been reported (Li et al., 2011) and is generally characteristic of approximate Bayesian inference schemes that contend with conditional dependencies.


Construct validation of a DCM for resting state fMRI.

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

This figure shows the posterior estimates that result from the Bayesian inversion of the simulated time series. The posterior means (grey bars) and 90% confidence intervals (pink bars) are shown with the true values (black bars) in the left column. The upper and lower panel reports spectral and stochastic DCMs respectively. The grey bars depict the posterior expectations of connections, where intrinsic (within region) or self-connections are parameterised in terms of their log scaling (such that a value of zero corresponds to a scaling of one). The extrinsic (between regions) connections are measured in Hz in the usual way. It can be seen that, largely, the true values fall within the Bayesian confidence intervals for spectral DCM but not for stochastic DCM. The right panel shows the same results but plotting the estimated connection strengths against their true values. For spectral DCM (resp. stochastic DCM), the blue (resp. cyan) circles correspond to extrinsic connections and the red (resp. magenta) circles to intrinsic connectivity.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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

f0015: This figure shows the posterior estimates that result from the Bayesian inversion of the simulated time series. The posterior means (grey bars) and 90% confidence intervals (pink bars) are shown with the true values (black bars) in the left column. The upper and lower panel reports spectral and stochastic DCMs respectively. The grey bars depict the posterior expectations of connections, where intrinsic (within region) or self-connections are parameterised in terms of their log scaling (such that a value of zero corresponds to a scaling of one). The extrinsic (between regions) connections are measured in Hz in the usual way. It can be seen that, largely, the true values fall within the Bayesian confidence intervals for spectral DCM but not for stochastic DCM. The right panel shows the same results but plotting the estimated connection strengths against their true values. For spectral DCM (resp. stochastic DCM), the blue (resp. cyan) circles correspond to extrinsic connections and the red (resp. magenta) circles to intrinsic connectivity.
Mentions: The posterior estimates of the effective connectivity are shown in Fig. 3. Posterior expectations are presented as grey bars with pink bars indicating the 90% Bayesian confidence intervals. We have also superimposed the true connectivity as black bars for comparison. The upper left panel shows the posterior expectations for the spDCM inversion (shown as spectral in title), whilst the lower left panel shows the results for the stochastic scheme using generalised filtering. Clearly, the spectral DCM's estimates are very accurate, with most of the extrinsic connection strengths falling within 90% confidence intervals. The intrinsic connections (i.e. the self-connections) are modelled as a (log) scale parameter and have a prior mean of zero. These connections are still estimated with good accuracy showing around a 20% underestimation of the self-connectivity. The stochastic scheme also performed well, with estimates tending towards the true values but not as accurately as the deterministic (spectral) scheme. This reiterates the point that stochastic DCM can find it difficult to recover effective connectivity from data generated from graphs with reciprocal connectivity. We have also presented these results in a scatter plot to illustrate the relative accuracies of the spectral and stochastic estimates (the posterior estimates of spDCM are closer to the true parameters than those generated by the model based on sDCM). It can also be seen that the stochastic model underestimates the parameters, a behaviour which has previously been reported (Li et al., 2011) and is generally characteristic of approximate Bayesian inference schemes that contend with conditional dependencies.

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