Abstract
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.
Highlights
To interpret the blood oxygenation-level dependent (BOLD) contrast, and its disruption due to aging (Tarantini et al 2017), disease (Shabir et al 2018), or pharmacological interventions (Otsu et al 2015), a better understanding of the biological mechanisms of neurovascular coupling is useful
The novel contribution of this work is to establish a relatively straightforward multi-modal dynamic causal modelling (DCM) procedure that flexibly connects laminar-specific neural mass models, which are fitted to electrophysiological data, with neurovascular models, which are fitted to functional magnetic resonance imaging (fMRI) data, via simulated neuronal drive functions
The neuronal drive functions act as a bridge between the fMRI and MEG modalities, enabling multi-modal analyses to be conducted with any of the neural mass models implemented within the DCM framework
Summary
To interpret the blood oxygenation-level dependent (BOLD) contrast, and its disruption due to aging (Tarantini et al 2017), disease (Shabir et al 2018), or pharmacological interventions (Otsu et al 2015), a better understanding of the biological mechanisms of neurovascular coupling is useful. There are many outstanding questions about the origin of BOLD in the human brain (Arthurs & Boniface, 2002; Hall et al, 2016) Is it driven by pre- or post-synaptic potentials of neuronal populations? In Alzheimer’s disease, a reduction in induced blood flow – in response to neuronal demands for energy – has been implicated in cognitive decline (Shabir et al 2018; Snyder et al 2015). Another example is aging, where there is a progressive reduction in the efficacy of neurovascular coupling (Lipecz et al 2019). These motivate the importance of an efficient approach to disambiguate the neurovascular mechanisms that underwrite neural and haemodynamic responses
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