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On self-feedback connectivity in neural mass models applied to event-related potentials. Neuroimage 2015 Mar;108:364-76

Date

01/07/2015

Pubmed ID

25562823

DOI

10.1016/j.neuroimage.2014.12.067

Scopus ID

2-s2.0-84922234208 (requires institutional sign-in at Scopus site)   10 Citations

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.

Author List

Youssofzadeh V, Prasad G, Wong-Lin K

Author

Vahab Youssofzadeh PhD Assistant Professor in the Neurology department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Brain
Evoked Potentials
Feedback, Physiological
Humans
Models, Neurological
Nerve Net