Dynamic causal models of neural system dynamics: current state and future extensions

Journal of biosciences

Klaas E Stephan1¹  Lee M Harrison¹  Stefan J Kiebel¹  Karl J Friston¹  Will D Penny¹  Olivier David² 

[1] Wellcome Department of Imaging Neuroscience, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK$$;INSERM U594 Neuroimagerie Fonctionnelle et Métabolique, Université Joseph Fourier, CHU – Pavillon B – BP 217, 38043 Grenoble Cedex 09, France$$

关键词: Dynamic causal modelling;    EEG;    effective connectivity;    event-related potentials;    fMRI;    neural system;   

DOI  :  

来源: Indian Academy of Sciences

【 摘 要 】

Complex processes resulting from interaction of multiple elements can rarely be understood by analytical scientific approaches alone; additional, mathematical models of system dynamics are required. This insight, which disciplines like physics have embraced for a long time already, is gradually gaining importance in the study of cognitive processes by functional neuroimaging. In this field, causal mechanisms in neural systems are described in terms of effective connectivity. Recently, dynamic causal modelling (DCM) was introduced as a generic method to estimate effective connectivity from neuroimaging data in a Bayesian fashion. One of the key advantages of DCM over previous methods is that it distinguishes between neural state equations and modality-specific forward models that translate neural activity into a measured signal. Another strength is its natural relation to Bayesian model selection (BMS) procedures. In this article, we review the conceptual and mathematical basis of DCM and its implementation for functional magnetic resonance imaging data and event-related potentials. After introducing the application of BMS in the context of DCM, we conclude with an outlook to future extensions of DCM. These extensions are guided by the long-term goal of using dynamic system models for pharmacological and clinical applications, particularly with regard to synaptic plasticity.

【 授权许可】

Unknown