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

Resting-state brain networks (RSNs) have been widely applied in health and disease, but their interpretation in terms of the underlying neural activity is unclear. To systematically investigate this cornerstone issue, here we simultaneously recorded whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions in rats. Our data show that for both recording sites, band-specific local field potential (LFP) power-derived spatial maps can explain up to 90% of the spatial variance of RSNs obtained by the rsfMRI signal. Paradoxically, the time series of LFP band power can only explain up to 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 limited impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggest that the electrophysiological activity alone does not account for all effects in the rsfMRI signal. To further interpret this disparity, we propose a model hypothesizing that a significant component in the rsfMRI signal is driven by electrophysiology-invisible neural activities that are active in neurovascular coupling. Temporally, this electrophysiology-invisible signal is weakly correlated to electrophysiology data. However, as signaling of these two types of neural activities are both constrained by the same anatomical backbone, they can generate similar RSN spatial patterns. These data and the model provide a new perspective of our interpretation of RSNs.