Construct validation of a DCM for resting state fMRI.
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.
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: email@example.com.Show MeSH
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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.
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: firstname.lastname@example.org.