title: On self-feedback connectivity in neural mass models applied to event-related potentials.
abstract: Neural mass models (NMMs) applied to neuroimaging data often do not emphasise intrinsic self-feedback within a neural population. However, based on mean-field theory, any population of coupled neurons is intrinsically endowed with effective self-coupling. In this work, we examine the effectiveness of three cortical NMMs with different self-feedbacks using a dynamic causal modelling approach. Specifically, we compare the classic Jansen and Rit (1995) model (no self-feedback), a modified model by Moran et al. (2007) (only inhibitory self-feedback), and our proposed model with inhibitory and excitatory self-feedbacks. Using bifurcation analysis, we show that single-unit Jansen-Rit model is less robust in generating oscillatory behaviour than the other two models. Next, under Bayesian inversion, we simulate single-channel event-related potentials (ERPs) within a mismatch negativity auditory oddball paradigm. We found fully self-feedback model (FSM) to provide the best fit to single-channel data. By analysing the posterior covariances of model parameters, we show that self-feedback connections are less sensitive to the generated evoked responses than the other model parameters, and hence can be treated analogously to "higher-order" parameter corrections of the original Jansen-Rit model. This is further supported in the more realistic multi-area case where FSM can replicate data better than JRM and MoM in the majority of subjects by capturing the finer features of the ERP data more accurately. Our work informs how NMMs with full self-feedback connectivity are not only more consistent with the underlying neurophysiology, but can also account for more complex features in ERP data.
authors.affiliation: Intelligent Systems Research Centre, School of Computing and Intelligent Systems, University of Ulster, Magee Campus, Northland Road, L'Derry BT48 7JL, UK.
authors.forename: Vahab
authors.initials: V
authors.lastname: Youssofzadeh
authors.affiliation: Intelligent Systems Research Centre, School of Computing and Intelligent Systems, University of Ulster, Magee Campus, Northland Road, L'Derry BT48 7JL, UK.
authors.forename: Girijesh
authors.initials: G
authors.lastname: Prasad
authors.affiliation: Intelligent Systems Research Centre, School of Computing and Intelligent Systems, University of Ulster, Magee Campus, Northland Road, L'Derry BT48 7JL, UK. Electronic address: k.wong-lin@ulster.ac.uk.
authors.forename: KongFatt
authors.initials: K
authors.lastname: Wong-Lin
mesh_terms: D001921:Brain / Q000502:physiology*; D005071:Evoked Potentials / Q000502:physiology*; D025461:Feedback, Physiological; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*
keywords: Auditory oddball paradigm; Bayesian inversion; DCM; ERP; Fully self-feedback model; Mismatch negativity