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Neuroimage. 2012 Jan 16;59(2):1261-74. doi: 10.1016/j.neuroimage.2011.08.020. Epub 2011 Sep 5.

Dynamic causal modeling with neural fields.

Author information

1
The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK. d.pinotsis@fil.ion.ucl.ac.uk

Abstract

The aim of this paper is twofold: first, to introduce a neural field model motivated by a well-known neural mass model; second, to show how one can estimate model parameters pertaining to spatial (anatomical) properties of neuronal sources based on EEG or LFP spectra using Bayesian inference. Specifically, we consider neural field models of cortical activity as generative models in the context of dynamic causal modeling (DCM). This paper considers the simplest case of a single cortical source modeled by the spatiotemporal dynamics of hidden neuronal states on a bounded cortical surface or manifold. We build this model using multiple layers, corresponding to cortical lamina in the real cortical manifold. These layers correspond to the populations considered in classical (Jansen and Rit) neural mass models. This allows us to formulate a neural field model that can be reduced to a neural mass model using appropriate constraints on its spatial parameters. In turn, this enables one to compare and contrast the predicted responses from equivalent neural field and mass models respectively. We pursue this using empirical LFP data from a single electrode to show that the parameters controlling the spatial dynamics of cortical activity can be recovered, using DCM, even in the absence of explicit spatial information in observed data.

PMID:
21924363
PMCID:
PMC3236998
DOI:
10.1016/j.neuroimage.2011.08.020
[Indexed for MEDLINE]
Free PMC Article

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