Ten simple rules for dynamic causal modeling

Stephan, K E, Penny, W D ORCID: https://orcid.org/0000-0001-9064-1191, Moran, R J, den Ouden, H E M, Daunizeau, J and Friston, K J (2010) Ten simple rules for dynamic causal modeling. NeuroImage, 49 (4). pp. 3099-3109. ISSN 1053-8119

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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 ten simple rules, we hope that this article serves as a helpful tutorial for the growing community of DCM users.

Item Type: Article
Additional Information: Copyright 2009 Elsevier Inc. All rights reserved.
Uncontrolled Keywords: algorithms,animals,bayes theorem,brain,brain mapping,causality,computer simulation,evoked potentials,humans,neurological models,nerve net,automated pattern recognition
Faculty \ School: Faculty of Social Sciences > School of Psychology
UEA Research Groups: Faculty of Social Sciences > Research Centres > Centre for Behavioural and Experimental Social Sciences
Depositing User: Pure Connector
Date Deposited: 22 Aug 2017 06:35
Last Modified: 19 Apr 2023 22:33
URI: https://ueaeprints.uea.ac.uk/id/eprint/64612
DOI: 10.1016/j.neuroimage.2009.11.015

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