Neurovascular coupling: insights from multi-modal dynamic causal modelling of fMRI and MEG
Amirhossein Jafarian, Vladimir Litvak, Hayriye Cagnan, Karl J., Friston, Peter Zeidman

TL;DR
This paper introduces a Bayesian multi-modal framework combining fMRI and MEG data to investigate neurovascular coupling mechanisms in the human brain, allowing for model comparison and hypothesis testing.
Contribution
It presents a novel Bayesian fusion approach for jointly modeling fMRI and MEG data to infer neurovascular coupling mechanisms and their region-specific characteristics.
Findings
Neuronal activity influences BOLD signals instantaneously.
Neurovascular coupling mechanisms vary across brain regions.
The method effectively distinguishes between different neurovascular models.
Abstract
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.…
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Taxonomy
TopicsFunctional Brain Connectivity Studies · Neural dynamics and brain function · EEG and Brain-Computer Interfaces
