Ten simple rules for dynamic causal modeling.
Neuroimage
; 49(4): 3099-109, 2010 Feb 15.
Article
en En
| MEDLINE
| ID: mdl-19914382
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 ten simple rules, we hope that this article serves as a helpful tutorial for the growing community of DCM users.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
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Encéfalo
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Mapeo Encefálico
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Teorema de Bayes
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Potenciales Evocados
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Modelos Neurológicos
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Red Nerviosa
Tipo de estudio:
Guideline
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Prognostic_studies
Idioma:
En
Revista:
Neuroimage
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2010
Tipo del documento:
Article