Fusion hidden Markov modeling reveals a dominant backbone state and transient alternatives in simultaneous resting-state EEG-fMRI

Abstract

Simultaneous EEG-fMRI offers a powerful way to study brain dynamics, but combining the two modalities in a common whole-brain model remains challenging. Here, I developed a fusion modeling framework for resting-state simultaneous EEG-fMRI that emphasized careful multimodal alignment, construction of a stable shared feature space, and cross-validated, reproducibility-based model selection. Using 15 eyes-open resting-state runs from 12 healthy adults in an open simultaneous EEG-fMRI dataset, I constructed a no-lag, 15-TR-minimum fusion dataset comprising 3550 retained TRs and 124.25 min of usable data. A leave-one-subject-out cross-validation sweep supported a parsimonious three-state fusion hidden Markov model. In the final full-data solution, one state emerged as a dominant backbone state with the highest occupancy, strongest persistence, and clearest canonical BOLD network organization. Two lower-occupancy states behaved as transient alternatives: one appeared as a broadly attenuated version of the backbone state, whereas the other showed more selective network reweighting. The states also differed in their descriptive cross-modal BOLD-EEG structure, suggesting that electrophysiological and hemodynamic network expression may align differently across latent brain states. These results provide both a practical whole-brain EEG-fMRI fusion workflow and a biologically interpretable account of low-order resting-state brain dynamics.