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Abstract:
Objectives: A consequent body of research has rapidly appeared to investigate the dynamic aspects of spontaneous brain activity. However, it is not clear to what extent the states driven from different imaging modalities reflect the common aspects of the underlying brain activity. In the current study we investigated mapping between the brain states inferred using EEG and fMRI.
Methods: We analyzed resting-state, eyes-closed recordings from 39 healthy male subjects over 2 sessions of simultaneous 3 Tesla fMRI and 64-channel EEG. Four microstate classes were identified using EEGLAB plugin for microstates analysis by Thomas Koenig (University of Bern, Switzerland). The primary nodes of the 7 conventional resting-state networks (RSNs) were used as regions of interest for BOLD activity and co-activation patterns (CAPs) extraction. We used support vector machine regression to predict the time courses of CAPs and BOLD activity based on the microstate time courses. Importantly, the mapping included a time scale adjustment by abstracting the feature space from time domain to a more concise representation (i.e. power spectrum density).
Results & Conclusions: Both CAP time series and BOLD activity can be efficiently predicted by the EEG microstate trajectories, but the prediction accuracy of the CAPs time courses is higher, compared to that of the activity itself. Moreover, prediction accuracy increases if the delay between CAPs and MS time courses is considered. These results shed lights on the common abstract representation of internal brain dynamics.
