The Neuroscience Graduate Program, The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, United States.
Center for Neural Engineering, Pennsylvania State University, University Park, United States.
Elife. 2024 Aug 5;13:RP95680. doi: 10.7554/eLife.95680.
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, implying the existence of an rsfMRI component contributed by 'electrophysiology-invisible' signals. These findings offer a novel perspective on our understanding of RSN interpretation.
静息态脑网络(RSNs)已广泛应用于健康和疾病领域,但关于 RSNs 与潜在神经活动之间的关系尚不清楚。为了解决这个基本问题,我们在大鼠的两个不同脑区同时记录了全脑静息态功能磁共振成像(rsfMRI)和电生理信号。我们的数据表明,对于两个记录部位,来自特定频段局部场电位(LFP)功率的空间图谱可以解释来自 rsfMRI 信号的 RSNs 的空间变异性高达 90%。令人惊讶的是,LFP 频段功率的时间序列最多只能解释来自同一部位的局部 rsfMRI 时间序列的 35%的时间方差。此外,从 rsfMRI 信号中回归 LFP 功率时间序列对基于 rsfMRI 的 RSNs 的空间模式几乎没有影响。静息态电生理和 rsfMRI 信号之间的空间和时间关系的这种差异表明,单独的电生理活动并不能完全解释 rsfMRI 信号中观察到的影响,这意味着存在由“电生理不可见”信号贡献的 rsfMRI 成分。这些发现为我们理解 RSN 解释提供了一个新的视角。