Ten simple rules for dynamic causal modeling
Laboratory for Social and Neural Systems Research · University College London · +3 more institutions
Abstract
Dynamic causal modeling (DCM) is a generic Bayesian framework for inferring hidden neuronal states from measurements of brain activity. It provides posterior estimates of neurobiologically interpretable quantities such as the effective strength of synaptic connections among neuronal populations and their context-dependent modulation. DCM is increasingly used in the analysis of a wide range of neuroimaging and electrophysiological data. Given the relative complexity of DCM, compared to conventional analysis techniques, a good knowledge of its theoretical foundations is needed to avoid pitfalls in its application and interpretation of results. By providing good practice recommendations for DCM, in the form of…
Citation impact
- FWCI
- 15.57
- Percentile
- 100%
- References
- 96
Authors
6- KΕKlaas Ε. StephanCorresponding
Laboratory for Social and Neural Systems Research, University College London, Wellcome Centre for Human Neuroimaging, University of Zurich, National Hospital for Neurology and Neurosurgery
- WPW.D. Penny
National Hospital for Neurology and Neurosurgery, University College London, Wellcome Centre for Human Neuroimaging
- RMRosalyn Moran
National Hospital for Neurology and Neurosurgery, Wellcome Centre for Human Neuroimaging, University College London
- HEHanneke E.M. den Ouden
- JDJean Daunizeau
Laboratory for Social and Neural Systems Research, University of Zurich, National Hospital for Neurology and Neurosurgery, University College London, Wellcome Centre for Human Neuroimaging
Topics & keywords
- Computer science
- Simple (philosophy)
- Neuroimaging
- Context (archaeology)
- Interpretation (philosophy)
- Bayesian probability
- Causal model
- Machine learning
- Quality Education