Dynamic causal modeling with neural fields

K.J. Friston,R.J. Moran,D.A. Pinotsis

doi:10.1016/j.neuroimage.2011.08.020

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.

Highlights

This paper is about estimating the spatiotemporal dynamics on cortical manifolds that produce electrophysiological measurements
We considered briefly the more structured spectral density of these predictions that derives from considering spatial dynamics
To quantify the effects of various parameters on the predictions, we examined the change in the spectral response with respect to each parameter

Summary

Introduction

This paper is about estimating the spatiotemporal dynamics on cortical manifolds that produce electrophysiological measurements. As we will see later, the impact of spatially extensive dynamics is not restricted to expression over space but can have profound effects on temporal (e.g., spectral) responses at one point (or averaged locally over the cortical surface) This means that neural field models may have a key role in the modeling of non-invasive electrophysiological data that does not resolve spatial activity directly. We can obtain an expression for the transfer function of the system of neural fields defined by Eq (2) by linearizing around a steady-state This allows us to express the system's spectral responses in terms of its key architectural parameters pertaining to postsynaptic filtering and the local connectivity patterns entailed by D(x, t).

Summary

A Jansen and Rit neural field model

48.73 Log-scaling

Conclusion