This technical note presents a framework for investigating the underlying
mechanisms of neurovascular coupling in the human brain using multi-modal
magnetoencephalography (MEG) and functional magnetic resonance (fMRI)
neuroimaging data. This amounts to estimating the evidence for several
biologically informed models of neurovascular coupling using variational
Bayesian methods and selecting the most plausible explanation using Bayesian
model comparison. First, fMRI data is used to localise active neuronal sources.
The coordinates of neuronal sources are then used as priors in the
specification of a DCM for MEG, in order to estimate the underlying generators
of the electrophysiological responses. The ensuing estimates of neuronal
parameters are used to generate neuronal drive functions, which model the pre
or post synaptic responses to each experimental condition in the fMRI paradigm.
These functions form the input to a model of neurovascular coupling, the
parameters of which are estimated from the fMRI data. This establishes a
Bayesian fusion technique that characterises the BOLD response - asking, for
example, whether instantaneous or delayed pre or post synaptic signals mediate
haemodynamic responses. Bayesian model comparison is used to identify the most
plausible hypotheses about the causes of the multimodal data. We illustrate
this procedure by comparing a set of models of a single-subject auditory fMRI
and MEG dataset. Our exemplar analysis suggests that the origin of the BOLD
signal is mediated instantaneously by intrinsic neuronal dynamics and that
neurovascular coupling mechanisms are region-specific. The code and example
dataset associated with this technical note are available through the
statistical parametric mapping (SPM) software package