Elsevier

NeuroImage

Volume 199, 1 October 2019, Pages 730-744
NeuroImage

Dynamic causal modelling revisited

https://doi.org/10.1016/j.neuroimage.2017.02.045Get rights and content
Under a Creative Commons license
open access

Highlights

  • This paper describes a DCM for fMRI based on neural mass models and canonical microcircuits.

  • This enables the (Bayesian) fusion of EEG and fMRI data.

  • That encompasses the formal modelling of neurovascular coupling.

  • Offers a surprising insight into the relationship between haemodynamic and electrophysiological responses.

Abstract

This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. This provides a generative or dynamic causal model of laminar specific responses that can generate haemodynamic and electrophysiological measurements. In principle, this allows the fusion of haemodynamic and (event related or induced) electrophysiological responses. Furthermore, it enables Bayesian model comparison of competing hypotheses about physiologically plausible synaptic effects; for example, does attentional modulation act on superficial or deep pyramidal cells – or both? In this technical note, we describe the resulting dynamic causal model and provide an illustrative application to the attention to visual motion dataset used in previous papers. Our focus here is on how to answer long-standing questions in fMRI; for example, do haemodynamic responses reflect extrinsic (afferent) input from distant cortical regions, or do they reflect intrinsic (recurrent) neuronal activity? To what extent do inhibitory interneurons contribute to neurovascular coupling? What is the relationship between haemodynamic responses and the frequency of induced neuronal activity? This paper does not pretend to answer these questions; rather it shows how they can be addressed using neural mass models of fMRI timeseries.

Keywords

Dynamic causal modelling
Haemodynamic models
Neural mass models
Effective connectivity
Bayesian

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