Bayesian comparison of neurovascular coupling models using EEG-fMRI

Rosa, Maria J, Kilner, James M and Penny, Will D. (2011) Bayesian comparison of neurovascular coupling models using EEG-fMRI. PLoS Computational Biology, 7 (6). ISSN 1553-734X

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Abstract

Functional magnetic resonance imaging (fMRI), with blood oxygenation level-dependent (BOLD) contrast, is a widely used technique for studying the human brain. However, it is an indirect measure of underlying neuronal activity and the processes that link this activity to BOLD signals are still a topic of much debate. In order to relate findings from fMRI research to other measures of neuronal activity it is vital to understand the underlying neurovascular coupling mechanism. Currently, there is no consensus on the relative roles of synaptic and spiking activity in the generation of the BOLD response. Here we designed a modelling framework to investigate different neurovascular coupling mechanisms. We use Electroencephalographic (EEG) and fMRI data from a visual stimulation task together with biophysically informed mathematical models describing how neuronal activity generates the BOLD signals. These models allow us to non-invasively infer the degree of local synaptic and spiking activity in the healthy human brain. In addition, we use Bayesian model comparison to decide between neurovascular coupling mechanisms. We show that the BOLD signal is dependent upon both the synaptic and spiking activity but that the relative contributions of these two inputs are dependent upon the underlying neuronal firing rate. When the underlying neuronal firing is low then the BOLD response is best explained by synaptic activity. However, when the neuronal firing rate is high then both synaptic and spiking activity are required to explain the BOLD signal.

Item Type: Article
Uncontrolled Keywords: action potentials,adult,bayes theorem,electroencephalography,humans,magnetic resonance imaging,male,cardiovascular models,neurological models,oximetry,photic stimulation,computer-assisted signal processing,synapses,visual cortex
Faculty \ School: Faculty of Social Sciences > School of Psychology
Depositing User: Pure Connector
Date Deposited: 19 Aug 2017 05:06
Last Modified: 22 Apr 2020 05:22
URI: https://ueaeprints.uea.ac.uk/id/eprint/64593
DOI: 10.1371/journal.pcbi.1002070

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