Dynamic Causal Models for phase coupling

Wellcome Trust Centre for Neuroimaging, University College, 12 Queen Square, London WC1N 3BG, UK.
Journal of Neuroscience Methods (Impact Factor: 2.05). 08/2009; 183(1):19-30. DOI: 10.1016/j.jneumeth.2009.06.029
Source: PubMed


This paper presents an extension of the Dynamic Causal Modelling (DCM) framework to the analysis of phase-coupled data. A weakly coupled oscillator approach is used to describe dynamic phase changes in a network of oscillators. The use of Bayesian model comparison allows one to infer the mechanisms underlying synchronization processes in the brain. For example, whether activity is driven by master-slave versus mutual entrainment mechanisms. Results are presented on synthetic data from physiological models and on MEG data from a study of visual working memory.

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Available from: Karl J Friston
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    • "To study the nature of hippocampal–prefrontal interactions in the theta regime we used DCM for phase-coupled data. DCM for phase coupling (Penny et al., 2009) is an extension of the DCM framework (Chen et al., 2008; David et al., 2006; Friston et al., 2003; Moran et al., 2009) to accommodate the analysis of data coupled in phase and uses a weakly coupled oscillator model to describe the dynamics of phase changes in a network. With this model-based connectivity approach it is possible to test hypotheses of master–slave relationships or mutual entrainment between regions in a given frequency regime. "
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    • "Motivated by the studies in Babajani-Feremi and Soltanian-Zadeh (2010, 2011) and Penny et al. (2009), we employed the VBEM algorithm to optimise the parameters of the proposed model with respect to observed ERP data. "
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    • "We show, however, that cross-frequency coupling functions and their associated causality can be inferred from real data, thus yielding the effective connectivity [21]. Our approach will be based on a coupled-phase-oscillator model [9] [22] and utilizes the recently proposed method of dynamical Bayesian inference [23] [24] [25] [26] [27]. Building on earlier work in this area [28] [29] [30], we will extend the method to encompass the inference of the coupling functions that prescribe the nature of the links (edges) between the oscillating nodes of a network. "
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