Publication: Dynamic causal modelling revisited
Dynamic causal modelling revisited
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Friston, K. J., Preller, K. H., Mathys, C., Cagnan, H., Heinzle, J., Razi, A., & Zeidman, P. (2019). Dynamic causal modelling revisited. NeuroImage, 199, 730–744. https://doi.org/10.1016/j.neuroimage.2017.02.045
Abstract
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Abstract
This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. This provides a generative or dynamic causal model of laminar specific responses that can generate haemodynamic and electrophysiological measurements. In principle, this allows the fusion of haemodynamic and (event related or induced) electrophysiological responses. Furthermore, it enables Bayesian model comparison of competing hypotheses about ph
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Creators (Authors)
- Friston, K J
- Preller, Katrin H
- Mathys, Chris
- Cagnan, Hayriye
- Heinzle, Jakob
- Razi, Adeel
- Zeidman, Peter
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Friston, K. J., Preller, K. H., Mathys, C., Cagnan, H., Heinzle, J., Razi, A., & Zeidman, P. (2019). Dynamic causal modelling revisited. NeuroImage, 199, 730–744. https://doi.org/10.1016/j.neuroimage.2017.02.045
