The default network refers to a network of brain regions more active in the resting state than during active engagement in a task. The anatomy and functional behaviour of the network has been well established through a decade of work which has a heavy bias towards fMRI-based investigation. EEG has great potential to increase our understanding of the default network, however to date the application of EEG in the area has been sparse and uncoordinated. Where it is deployed, often authors will attempt to make new inferences about the default network before their EEG signal has been established as truly reflecting activity in the network. The establishement of an agreed default network marker in the EEG signal would allow for much more coordination in the investigation of the network and allow the integration of results into a coherent whole.
The present work aimed to construct a robust and replicable approach to investigating whether aspects of the EEG signal may be reflective of default network activity. A three stage process was used. Firstly, the existing fMRI literature was studied to create a 'template' of default network activity during task and rest states. Secondly, the broader default network literature was studied to identify EEG signals which have been suggested to be reflective of default network activity. Finally, experiments were conducted collecting EEG data in simple task and rest states. The behaviour of the EEG signal was compared to the default network template. Very low frequency EEG and frontal midline theta were assessed on this basis.
The former was not found to demonstrate identifiable default network-like acitivty, however the interpretation of this negative finding was made difficult by the lack of a general understanding of EEG in the sub 1Hz frequency range. The latter was found to bear some hallmarks of default network activity – a change in overall power and a change in low frequency power fluctuations between conditions – however these changes were in the opposite direction from those predicted.
This partial fit to predictions was found to highlight strengths and weaknesses of this template matching approach. The weakness is that ambiguous results cannot readily be interpreted within an approach designed to make judgements one way or the other. The strength is that this ambiguity was not resolved with reference to the default network literature. The new aspect of the present work is that task-state signal was extracted purely from pretrial baseline periods free from the influence of event related activity, an approach does not appear to have been adopted in the fMRI literature which was used to construct the template. The benefit of these results, then, is that they pose a question which the existing literature cannot answer, suggesting future directions in both EEG and fMRI work.
