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

ABSTRACT 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, Sep 27, 2015
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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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    • "There is now a library of DCMs and variants differ according to their level of biological realism and the data features which they explain. The DCM approach can be applied to fMRI (Friston et al., 2003, 2014), electroencephalographic (EEG), and Magnetoencephalographic (MEG) data (Moran et al., 2007; Penny et al., 2009; Daunizeau et al., 2009b). This paper extends the DCM approach to fNIRS. "
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    ABSTRACT: Functional near-infrared spectroscopy (fNIRS) is an emerging technique for measuring changes in cerebral hemoglobin concentration via optical absorption changes. Although there is great interest in using fNIRS to study brain connectivity, current methods are unable to infer the directionality of neuronal connections. In this paper, we apply Dynamic Causal Modelling (DCM) to fNIRS data. Specifically, we present a generative model of how observed fNIRS data are caused by interactions among hidden neuronal states. Inversion of this generative model, using an established Bayesian framework (variational Laplace), then enables inference about changes in directed connectivity at the neuronal level. Using experimental data acquired during motor imagery and motor execution tasks, we show that directed (i.e., effective) connectivity from supplementary motor area to primary motor cortex is negatively modulated by motor imagery, and this suppressive influence causes reduced activity in primary motor cortex during motor imagery. These results are consistent with findings of previous functional magnetic resonance imaging (fMRI) studies, suggesting that the proposed method enables one to infer directed interactions in the brain mediated by neuronal dynamics from measurements of optical density changes. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
    NeuroImage 02/2015; 8. DOI:10.1016/j.neuroimage.2015.02.035 · 6.36 Impact Factor
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