Dynamic causal modelling revisited

K. J. Friston, Katrin H. Preller, Chris Mathys, Hayriye Cagnan, Jakob Heinzle, Adeel Razi, Peter Zeidman

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Pages (from-to)730-744
Number of pages15
JournalNeuroImage
Volume199
DOIs
Publication statusPublished - 1 Oct 2019
Externally publishedYes

Keywords

  • Bayesian
  • Dynamic causal modelling
  • Effective connectivity
  • Haemodynamic models
  • Neural mass models

Cite this

Friston, K. J., Preller, K. H., Mathys, C., Cagnan, H., Heinzle, J., Razi, A., & Zeidman, P. (2019). Dynamic causal modelling revisited. NeuroImage, 199, 730-744. https://doi.org/10.1016/j.neuroimage.2017.02.045