Dynamic causal modelling revisited

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 haemodynam...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Vol. 199; pp. 730 - 744
Main Authors: Friston, K.J., Preller, Katrin H., Mathys, Chris, Cagnan, Hayriye, Heinzle, Jakob, Razi, Adeel, Zeidman, Peter
Format: Journal Article
Language:English
Published: United States Elsevier Inc 01.10.2019
Elsevier Limited
Academic Press
Subjects:
Adult
Attention
Bayesian
Bayesian analysis
Blood
Brain - diagnostic imaging
Brain - physiology
Cortex
Dynamic causal modelling
Effective connectivity
Electroencephalography
Functional magnetic resonance imaging
Functional Neuroimaging - methods
Haemodynamic models
Hemodynamics
Hemodynamics - physiology
Humans
Interneurons
Magnetic Resonance Imaging
Mathematical models
Models, Biological
Motion detection
Motion Perception - physiology
Nerve Net - diagnostic imaging
Nerve Net - physiology
Neural mass models
Neurovascular Coupling - physiology
Population
Pyramidal cells
Sensory neurons
Time series
Visual perception
Haemodynamic models
Neural mass models
Bayesian
Dynamic causal modelling
Effective connectivity
ISSN:1053-8119, 1095-9572, 1095-9572
Online Access:Get full text
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Summary: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. •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.
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ISSN:1053-8119
1095-9572
1095-9572
DOI:10.1016/j.neuroimage.2017.02.045