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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    ABSTRACT: Detecting environmental change is fundamental for adaptive behaviour in an uncertain world. Previous work indicates the hippocampus supports generation of novelty signals via implementation of a match-mismatch detector that signals when an incoming sensory input violates expectations based on past experience. While existing work has emphasized the particular contribution of the hippocampus, here we ask which other brain structures also contribute to match-mismatch detection. Furthermore, we leverage the fine-grained temporal resolution of magnetoencephalography (MEG) to investigate whether mismatch computations are spectrally confined to the theta range, based on the prominence of this range of oscillations in models of hippocampal function. By recording MEG activity while human subjects perform a task that incorporates conditions of match-mismatch novelty we show that mismatch signals are confined to the theta band and are expressed in both the hippocampus and ventromedial prefrontal cortex (vmPFC). Effective connectivity analyses (dynamic causal modelling) show that the hippocampus and vmPFC work as a functional circuit during mismatch detection. Surprisingly, our results suggest that the vmPFC drives the hippocampus during the generation and processing of mismatch signals. Our findings provide new evidence that the hippocampal-vmPFC circuit is engaged during novelty processing, which has implications for emerging theories regarding the role of vmPFC in memory. Copyright © 2015. Published by Elsevier Inc.
    NeuroImage 07/2015; 120. DOI:10.1016/j.neuroimage.2015.07.016 · 6.36 Impact Factor
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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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    DESCRIPTION: In this work, we provide an extension to commonly used neural mass models (NMMs) by incorporating self-feedback connections within three main neuronal populations, including our proposed NMM with full self-feedback (FSM). Compared to a commonly used NMM (Jansen and Ritt model), dynamical system analysis and spectral representations show FSM to be capable of robustly generating all EEG rhythms over a wide range of frequencies. Under Bayesian inversion approach, we validate the NMMs fitted outputs with ERP data, and found that FSM best replicates all the individual channel data. Moreover, posterior correlation of interdependencies among model parameters shows that self-feedback within deep-layer excitatory neuronal population does not contribute much to the generated evoked response. Next, we incorporate inter-areal connectivity to the NMMs using dynamic causal modelling approach. Our results show a reasonable match with experimental recordings for both single and multi-unit channel data. Interestingly, we also found the multi-area Jansen-Rit model to perform as well as the FSM.
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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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    ABSTRACT: Networks of interacting oscillators abound in nature, and one of the prevailing challenges in science is how to characterize and reconstruct them from measured data. We present a method of reconstruction based on dynamical Bayesian inference that is capable of detecting the effective phase connectivity within networks of time-evolving coupled phase oscillators subject to noise. It not only reconstructs pairwise, but also encompasses couplings of higher degree, including triplets and quadruplets of interacting oscillators. Thus inference of a multivariate network enables one to reconstruct the coupling functions that specify possible causal interactions, together with the functional mechanisms that underlie them. The characteristic features of the method are illustrated by the analysis of a numerically generated example: a network of noisy phase oscillators with time-dependent coupling parameters. To demonstrate its potential, the method is also applied to neuronal coupling functions from single- and multi-channel electroencephalograph recordings. The cross-frequency δ, α to α coupling function, and the , α, γ to γ triplet are computed, and their coupling strengths, forms of coupling function, and predominant coupling components, are analysed. The results demonstrate the applicability of the method to multivariate networks of oscillators, quite generally.
    New Journal of Physics 03/2015; 17(3). DOI:10.1088/1367-2630/17/3/035002 · 3.56 Impact Factor
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