Document Detail


Dynamic causal models and autopoietic systems.
MedLine Citation:
PMID:  18575681     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Dynamic Causal Modelling (DCM) and the theory of autopoietic systems are two important conceptual frameworks. In this review, we suggest that they can be combined to answer important questions about self-organising systems like the brain. DCM has been developed recently by the neuroimaging community to explain, using biophysical models, the non-invasive brain imaging data are caused by neural processes. It allows one to ask mechanistic questions about the implementation of cerebral processes. In DCM the parameters of biophysical models are estimated from measured data and the evidence for each model is evaluated. This enables one to test different functional hypotheses (i.e., models) for a given data set. Autopoiesis and related formal theories of biological systems as autonomous machines represent a body of concepts with many successful applications. However, autopoiesis has remained largely theoretical and has not penetrated the empiricism of cognitive neuroscience. In this review, we try to show the connections that exist between DCM and autopoiesis. In particular, we propose a simple modification to standard formulations of DCM that includes autonomous processes. The idea is to exploit the machinery of the system identification of DCMs in neuroimaging to test the face validity of the autopoietic theory applied to neural subsystems. We illustrate the theoretical concepts and their implications for interpreting electroencephalographic signals acquired during amygdala stimulation in an epileptic patient. The results suggest that DCM represents a relevant biophysical approach to brain functional organisation, with a potential that is yet to be fully evaluated.
Authors:
Olivier David
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2008-05-28
Journal Detail:
Title:  Biological research     Volume:  40     ISSN:  0716-9760     ISO Abbreviation:  Biol. Res.     Publication Date:  2007  
Date Detail:
Created Date:  2008-06-25     Completed Date:  2009-01-26     Revised Date:  2013-06-05    
Medline Journal Info:
Nlm Unique ID:  9308271     Medline TA:  Biol Res     Country:  Chile    
Other Details:
Languages:  eng     Pagination:  487-502     Citation Subset:  IM    
Affiliation:
Inserm, U836, Grenoble Institut des Neurosciences, University Hospital, Bát. EJ Safra, BP 170, 38042 Grenoble Cedex 9, France. odavid@ujf-grenoble.fr
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Brain / physiology*
Humans
Models, Neurological
Nerve Net / physiology*
Synaptic Transmission / physiology*
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