title: Dynamic causal modelling of lateral interactions in the visual cortex.
abstract: This paper presents a dynamic causal model based upon neural field models of the Amari type. We consider the application of these models to non-invasive data, with a special focus on the mapping from source activity on the cortical surface to a single channel. We introduce a neural field model based upon the canonical microcircuit (CMC), in which neuronal populations are assigned to different cortical layers. We show that DCM can disambiguate between alternative (neural mass and field) models of cortical activity. However, unlike neural mass models, DCM with neural fields can address questions about neuronal microcircuitry and lateral interactions. This is because they are equipped with interlaminar connections and horizontal intra-laminar connections that are patchy in nature. These horizontal or lateral connections can be regarded as connecting macrocolumns with similar feature selectivity. Crucially, the spatial parameters governing horizontal connectivity determine the separation (width) of cortical macrocolumns. Thus we can estimate the width of macro columns, using non-invasive electromagnetic signals. We illustrate this estimation using dynamic causal models of steady-state or ongoing spectral activity measured using magnetoencephalography (MEG) in human visual cortex. Specifically, we revisit the hypothesis that the size of a macrocolumn is a key determinant of neuronal dynamics, particularly the peak gamma frequency. We are able to show a correlation, over subjects, between columnar size and peak gamma frequency - that fits comfortably with established correlations between peak gamma frequency and the size of visual cortex defined retinotopically. We also considered cortical excitability and assessed its relative influence on observed gamma activity. This example highlights the potential utility of dynamic causal modelling and neural fields in providing quantitative characterisations of spatially extended dynamics on the cortical surface - that are parameterised in terms of horizontal connections, implicit in the cortical micro-architecture and its synaptic parameters.
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK. Electronic address: d.pinotsis@ucl.ac.uk.
authors.forename: D A
authors.initials: DA
authors.lastname: Pinotsis
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
authors.forename: D S
authors.initials: DS
authors.lastname: Schwarzkopf
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
authors.forename: V
authors.initials: V
authors.lastname: Litvak
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
authors.forename: G
authors.initials: G
authors.lastname: Rees
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
authors.forename: G
authors.initials: G
authors.lastname: Barnes
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
authors.forename: K J
authors.initials: KJ
authors.lastname: Friston
mesh_terms: D000465:Algorithms; D001931:Brain Mapping; D003198:Computer Simulation; D006801:Humans; D015225:Magnetoencephalography; D008959:Models, Neurological; D014793:Visual Cortex / Q000502:physiology*
keywords: Connectivity; Dynamic causal modelling; Electrophysiology; MEG; Neural field theory; Visual cortex