Dynamic causal modelling revisited.

Friston KJ; Preller KH; Mathys C; Cagnan H; Heinzle J; Razi A; Zeidman P

doi:10.1016/j.neuroimage.2017.02.045

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Journal article

Dynamic causal modelling revisited.

Friston KJ The Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom. Electronic address: k.friston@ucl.ac.uk.
Preller KH The Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom; Neuropsychopharmacology and Brain Imaging, Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry Zurich, 8032 Zurich, Switzerland. Electronic address: preller@bli.uzh.ch.
Mathys C The Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, United Kingdom. Electronic address: c.mathys@ucl.ac.uk.
Cagnan H The Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom; MRC Brain Network Dynamics Unit (BNDU), Department of Pharmacology and Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK. Electronic address: hayriye.cagnan@ndcn.ox.ac.uk.
Heinzle J The Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, 8032 Zürich, Switzerland. Electronic address: heinzle@biomed.ee.ethz.ch.
Razi A The Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom; Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan. Electronic address: a.razi@ucl.ac.uk.
Zeidman P The Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom. Electronic address: peter.zeidman@ucl.ac.uk.

2017-02-22

Published in:

NeuroImage. - 2019

English 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.

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