Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are a powerful means of non-invasively measuring neural activity within the brain. Both techniques excel at providing different information. EEG measures voltage from the scalp and can sample data at the order of kHz, meaning that it can provide data on how on the response of a large population of pyramidal cells with the same orientation changes over the course of milliseconds (Lopes da Silva, 2013; Luck, 2013). A prominent technique that utilises EEG is the event-related potential (ERP) technique, which segments an EEG response for a very short time period after an event, that is repeated for a large number of trials (Luck, 2013). ERPs contain ‘peaks’ - or components - that represent the sum of responses within the cranium. The trouble with EEG is the inverse problem, whereby it is impossible to identify the source of voltage measurements on the scalp within the cranium (Luck, 2013).
fMRI, on the other hand, has incredibly good spatial resolution but suffers from poor temporal resolution. fMRI, unlike EEG, is not an electric response measured from a pyramidal cell. Instead, it a haemodynamic response that reflects changes in blood oxygenation as neurons engage in a process called the blood oxygen level dependent (BOLD) signal. Unlike EEG, which can measure responses over the course of milliseconds, the haemodynamic response evolves over the order of seconds. As a result, a compromise must be made between temporal resolution when using EEG or spatial resolution with fMRI.
One pursuit to overcome the limitations inherited by each of these techniques is to combine them (Turner et al., 2016; Debener et al., 2006; Wei et al., 2020). When EEG and fMRI are combined, they appear to be able to explain more variance in cognitive parameters compared to when behaviour is used by itself (Turner et al., 2016). Changes in the EEG signal as measured by ERPs are also capable of provide a rich amount of data over a small time period, which can be harnessed to identify several spatially separated regional activations as measured by fMRI (Debener et al., 2016).
- Bayesian fusion and multimodal DCM for EEG and fMRI. Huilin Wei, Amirhossein Jafarian, Peter Zeidman, Vladimir Litvak, Adeel Razi, Dewen Hu, Karl J.Friston. NeuroImage. May 2020
- Why more is better: Simultaneous modeling of EEG, fMRI, and behavioral data. Brandon M Turner, Christian A Rodriguez, Tony M Norcia, Samuel M McClure, Mark Steyvers. NeuroImage. March 2016
- EEG and MEG: relevance to neuroscience. Fernando Lopes da Silva. Neuron. December 2013
- Single-trial EEG-fMRI reveals the dynamics of cognitive function. Stefan Debener, Markus Ullsperger, Markus Siegel, Andreas K Engel. Trends in Cognitive Sciences. October 2006
- An Introduction to the Event-Related Potential Technique. Steven J. Luck. MIT Press. August 2005
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