静息状态大脑网络产生于电生理学上的不可见信号。

Wenyu Tu, Samuel R Cramer, Nanyin Zhang
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摘要

静息状态脑网络(RSN)已在健康和疾病中得到广泛应用,但其对潜在神经活动的解释尚不清楚。为了系统地研究这一基石问题,我们同时记录了大鼠大脑两个独立区域的全脑静息状态功能性磁共振成像(rsfMRI)和电生理信号。我们的数据表明,对于两个记录位点,带特异性局部场电位(LFP)功率导出的空间图可以解释血氧水平依赖(BOLD)信号获得的RSN高达90%的空间方差。矛盾的是,即使在控制了可能影响明显LFP-BOLD相关性的因素(如对比噪声比)之后,LFP频带功率的时间序列也只能解释来自同一位置的局部BOLD时间过程的高达35%的时间变化。此外,从rsfMRI信号中回归出LFP带功率不会影响BOLD衍生的RSN的空间模式,这共同表明电生理活动对rsfMRI的信号具有边际影响。这些发现在轻度镇静和清醒状态下都保持一致。为了调和静息状态电生理学和rsfMRI信号之间的空间和时间关系中的这种矛盾,我们提出了一个模型,假设rssfMRI信号是由电生理学不可见的神经活动驱动的,这些神经活动在神经-血管耦合中活跃,但在时间上与电生理数据弱相关。同时,电生理学和电生理学不可见/BOD活动的信号传导都受到相同解剖主干的约束,导致空间相似的RSN。这些数据和模型为我们解释RSN提供了一个新的视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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

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 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.

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