Dynamic causal modelling revisited

• DOI: 10.1016/j.neuroimage.2017.02.045
• Publication Date: 2017-02-17T15:16:28Z
• Authors: K.J. Friston; Katrin H. Preller; Chris Mathys; Hayriye Cagnan; Jakob Heinzle; Adeel Razi; Peter Zeidman
• Keywords: 2805 Cognitive Neuroscience; neural mass models; Adult; effective connectivity; Cognitive Neuroscience; Motion Perception; 610 Medicine & health; Models, Biological; Bayesian; Article; 03 medical and health sciences; 0302 clinical medicine; Humans; 10237 Institute of Biomedical Engineering; haemodynamic models; Dynamic causal modelling; Haemodynamic models; Neural mass models; Effective connectivity; Bayesian; Neural mass models; Effective connectivity; Functional Neuroimaging; Hemodynamics; Bayesian; Dynamic causal modelling; Effective connectivity; Haemodynamic models; Neural mass models; Brain; Electroencephalography; Magnetic Resonance Imaging; dynamic causal modelling; Haemodynamic models; Neurology; 10054 Clinic for Psychiatry, Psychotherapy, and Psychosomatics; 2808 Neurology; Neurovascular Coupling; Nerve Net; Dynamic causal modelling

NeuroImage, 199<br/>ISSN:1095-9572<br/>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.<br/>ISSN:1053-8119<br/>