PDF(Pubmed)
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, implying the existence of an rsfMRI component contributed by \'electrophysiology-invisible\' signals. These findings offer a novel perspective on our understanding of RSN interpretation.
The brain contains many cells known as neurons that send and receive messages in the form of electrical signals. The neurons in different regions of the brain must coordinate their activities to enable the brain to operate properly. Researchers often use a method called resting-state functional magnetic resonance imaging (rsfMRI) to study how different areas of the brain work together. This method indirectly measures brain activity by detecting the changes in blood flow to different areas of the brain. Regions that are working together will become active (that is, have higher blood flow and corresponding rsfMRI signal) and inactive (have lower blood flow and a lower rsfMRI signal) at the same time. These coordinated patterns of brain activity are known as “resting-state brain networks” (RSNs). Previous studies have identified RSNs in many different situations, but we still do not fully understand how these changes in blood flow are related to what is happening in the neurons themselves. To address this question, Tu et al. performed rsfMRI while also measuring the electrical activity (referred to as electrophysiology signals) in two distinct regions of the brains of rats. The team then used the data to generate maps of RSNs in those brain regions. This revealed that rsfMRI signals and electrophysiology signals produced almost identical maps in terms of the locations of the RSNs. However, the electrophysiology signals only contributed a small amount to the changes in the local rsfMRI signals over time at the same recording site. This suggests that RSNs may arise from cell activities that are not detectable by electrophysiology but do regulate blood flow to neurons. The findings of Tu et al. offer a new perspective for interpreting how rsfMRI signals relate to the activities of neurons. Further work is needed to explore all the features of the electrophysiology signals and test other methods to compare these features with rsfMRI signals in the same locations.
The brain contains many cells known as neurons that send and receive messages in the form of electrical signals. The neurons in different regions of the brain must coordinate their activities to enable the brain to operate properly. Researchers often use a method called resting-state functional magnetic resonance imaging (rsfMRI) to study how different areas of the brain work together. This method indirectly measures brain activity by detecting the changes in blood flow to different areas of the brain. Regions that are working together will become active (that is, have higher blood flow and corresponding rsfMRI signal) and inactive (have lower blood flow and a lower rsfMRI signal) at the same time. These coordinated patterns of brain activity are known as “resting-state brain networks” (RSNs). Previous studies have identified RSNs in many different situations, but we still do not fully understand how these changes in blood flow are related to what is happening in the neurons themselves. To address this question, Tu et al. performed rsfMRI while also measuring the electrical activity (referred to as electrophysiology signals) in two distinct regions of the brains of rats. The team then used the data to generate maps of RSNs in those brain regions. This revealed that rsfMRI signals and electrophysiology signals produced almost identical maps in terms of the locations of the RSNs. However, the electrophysiology signals only contributed a small amount to the changes in the local rsfMRI signals over time at the same recording site. This suggests that RSNs may arise from cell activities that are not detectable by electrophysiology but do regulate blood flow to neurons. The findings of Tu et al. offer a new perspective for interpreting how rsfMRI signals relate to the activities of neurons. Further work is needed to explore all the features of the electrophysiology signals and test other methods to compare these features with rsfMRI signals in the same locations.
摘要：
静息状态脑网络(RSNs)已广泛应用于健康和疾病,但RSN在潜在神经活动方面的解释尚不清楚。为了解决这个基本问题，我们同时记录了大鼠两个不同脑区的全脑静息态功能磁共振成像(rsfMRI)和电生理信号.我们的数据显示，对于两个录音网站，从频带特定的局部场电位（LFP）功率得出的空间图可以占从rsfMRI信号得出的RSN的空间变异性的90％。令人惊讶的是,LFP频带功率的时间序列只能解释来自同一地点的局部rsfMRI时间过程的最大时间方差的35％。此外,从rsfMRI信号中回归LFP功率的时间序列对基于rsfMRI的RSN的空间模式的影响最小。静息态电生理和rsfMRI信号之间的空间和时间关系的差异表明，仅电生理活动并不能完全解释在rsfMRI信号中观察到的效应。暗示存在由“电生理不可见”信号贡献的rsfMRI组件。这些发现为我们对RSN解释的理解提供了新的视角。
大脑包含许多被称为神经元的细胞，这些细胞以电信号的形式发送和接收信息。大脑不同区域的神经元必须协调它们的活动，以使大脑能够正常运行。研究人员经常使用一种称为静息状态功能磁共振成像（rsfMRI）的方法来研究大脑的不同区域如何协同工作。这种方法通过检测流向大脑不同区域的血流变化来间接测量大脑活动。正在合作的地区将变得活跃(即，同时具有较高的血流量和相应的rsfMRI信号）和非活动（具有较低的血流量和较低的rsfMRI信号）。这些协调的大脑活动模式被称为“静息状态大脑网络”（RSN）。以前的研究已经在许多不同的情况下确定了RSN，但是我们仍然没有完全理解这些血流的变化与神经元本身发生的事情有关。为了解决这个问题，Tu等人。进行rsfMRI，同时测量大鼠大脑两个不同区域的电活动（称为电生理信号）。然后，该团队使用这些数据生成这些大脑区域的RSN图。这表明rsfMRI信号和电生理信号在RSN的位置方面产生几乎相同的图。然而,电生理信号仅对同一记录部位的局部rsfMRI信号随时间的变化贡献很小。这表明RSN可能来自电生理学无法检测到的细胞活动，但确实可以调节流向神经元的血流。Tu等人的发现。为解释rsfMRI信号与神经元活动的关系提供了一个新的视角。需要进一步的工作来探索电生理信号的所有特征并测试其他方法以将这些特征与相同位置的rsfMRI信号进行比较。
大脑包含许多被称为神经元的细胞，这些细胞以电信号的形式发送和接收信息。大脑不同区域的神经元必须协调它们的活动，以使大脑能够正常运行。研究人员经常使用一种称为静息状态功能磁共振成像（rsfMRI）的方法来研究大脑的不同区域如何协同工作。这种方法通过检测流向大脑不同区域的血流变化来间接测量大脑活动。正在合作的地区将变得活跃(即，同时具有较高的血流量和相应的rsfMRI信号）和非活动（具有较低的血流量和较低的rsfMRI信号）。这些协调的大脑活动模式被称为“静息状态大脑网络”（RSN）。以前的研究已经在许多不同的情况下确定了RSN，但是我们仍然没有完全理解这些血流的变化与神经元本身发生的事情有关。为了解决这个问题，Tu等人。进行rsfMRI，同时测量大鼠大脑两个不同区域的电活动（称为电生理信号）。然后，该团队使用这些数据生成这些大脑区域的RSN图。这表明rsfMRI信号和电生理信号在RSN的位置方面产生几乎相同的图。然而,电生理信号仅对同一记录部位的局部rsfMRI信号随时间的变化贡献很小。这表明RSN可能来自电生理学无法检测到的细胞活动，但确实可以调节流向神经元的血流。Tu等人的发现。为解释rsfMRI信号与神经元活动的关系提供了一个新的视角。需要进一步的工作来探索电生理信号的所有特征并测试其他方法以将这些特征与相同位置的rsfMRI信号进行比较。
