Comparing dynamic causal models of neurovascular coupling with fMRI and EEG/MEG

• Published in: NeuroImage (Orlando, Fla.) Vol. 216; p. 116734
• Main Authors: Jafarian, Amirhossein, Litvak, Vladimir, Cagnan, Hayriye, Friston, Karl J., Zeidman, Peter
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.08.2020; Elsevier Limited; Academic Press; Elsevier
• Subjects: Adult; Auditory Perception - physiology; Bayes Theorem; Bayesian analysis; Bayesian model comparison; Brain; Brain mapping; Dynamic causal modelling; EEG; Electroencephalography; Electroencephalography - methods; Functional magnetic resonance imaging; Functional Neuroimaging - methods; Hemodynamics; Humans; Hypothesis testing; Magnetic Resonance Imaging - methods; Magnetoencephalography - methods; Male; Mathematical models; Medical imaging; Models, Theoretical; Multimodal; Multimodal Imaging - methods; Neural mass models; Neuroimaging; Neurovascular coupling; Neurovascular Coupling - physiology; Population; Signal Processing, Computer-Assisted; Studies; Neural mass models; Multimodal; Bayesian model comparison; Dynamic causal modelling; Neurovascular coupling
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.116734

This technical note presents a dynamic causal modelling (DCM) procedure for evaluating different models of neurovascular coupling in the human brain – using combined electromagnetic (M/EEG) and functional magnetic resonance imaging (fMRI) data. This procedure compares the evidence for biologically informed models of neurovascular coupling using Bayesian model comparison. First, fMRI data are used to localise regionally specific neuronal responses. The coordinates of these responses are then used as the location priors in a DCM of electrophysiological responses elicited by the same paradigm. The ensuing estimates of model parameters are then used to generate neuronal drive functions, which model pre- or post-synaptic activity for each experimental condition. These functions form the input to a model of neurovascular coupling, whose parameters are estimated from the fMRI data. Crucially, this enables one to evaluate different models of neurovascular coupling, using Bayesian model comparison – asking, for example, whether instantaneous or delayed, pre- or post-synaptic signals mediate haemodynamic responses. We provide an illustrative application of the procedure using a single-subject auditory fMRI and MEG dataset. The code and exemplar data accompanying this technical note are available through the statistical parametric mapping (SPM) software. •A method is introduced for Bayesian fusion of M/EEG and BOLD fMRI data using dynamic causal modelling.•The key novel contribution is the introduction of neural drive functions, which link models of the two modalities.•Bayesian model comparison is used to select plausible hypotheses about the function of neurovascular coupling.