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This technical note presents a framework for investigating the underlyingmechanisms of neurovascular coupling in the human brain using multi-modalmagnetoencephalography (MEG) and functional magnetic resonance (fMRI)neuroimaging data. This amounts to estimating the evidence for severalbiologically informed models of neurovascular coupling using variationalBayesian methods and selecting the most plausible explanation using Bayesianmodel comparison. First, fMRI data is used to localise active neuronal sources.The coordinates of neuronal sources are then used as priors in thespecification of a DCM for MEG, in order to estimate the underlying generatorsof the electrophysiological responses. The ensuing estimates of neuronalparameters are used to generate neuronal drive functions, which model the preor post synaptic responses to each experimental condition in the fMRI paradigm.These functions form the input to a model of neurovascular coupling, theparameters of which are estimated from the fMRI data. This establishes aBayesian fusion technique that characterises the BOLD response - asking, forexample, whether instantaneous or delayed pre or post synaptic signals mediatehaemodynamic responses. Bayesian model comparison is used to identify the mostplausible hypotheses about the causes of the multimodal data. We illustratethis procedure by comparing a set of models of a single-subject auditory fMRIand MEG dataset. Our exemplar analysis suggests that the origin of the BOLDsignal is mediated instantaneously by intrinsic neuronal dynamics and thatneurovascular coupling mechanisms are region-specific. The code and exampledataset associated with this technical note are available through thestatistical parametric mapping (SPM) software package.

Neurovascular coupling: insights from multi-modal dynamic causal modelling of fMRI and MEG