Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals

Abstract

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal. To reconcile this disparity, we propose a model hypothesizing that a significant component of the rsfMRI signal is driven by neural activities that are not directly captured by electrophysiology, yet are active in neurovascular coupling. These "electrophysiology-invisible" signals exhibit weak temporal correlation with electrophysiology data. However, due to the shared anatomical backbone constraining both types of neural activities, they can produce similar spatial patterns in RSNs in parallel. These findings, along with our proposed model, offer a novel perspective on our understanding of RSN interpretation.