Department of Physiology, The University of Tokyo School of Medicine, Tokyo, Japan.
Department of Molecular Physiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Cereb Cortex. 2019 Apr 1;29(4):1496-1508. doi: 10.1093/cercor/bhy045.
Resting-state functional connectivity (FC) has become a major functional magnetic resonance imaging method to study network organization of human brains. There has been recent interest in the temporal fluctuations of FC calculated using short time windows ("dynamic FC") because this method could provide information inaccessible with conventional "static" FC, which is typically calculated using the entire scan lasting several tens of minutes. Although multiple studies have revealed considerable temporal fluctuations in FC, it is still unclear whether the fluctuations of FC measured in hemodynamics reflect the dynamics of underlying neural activity. We addressed this question using simultaneous imaging of neuronal calcium and hemodynamic signals in mice and found coordinated temporal dynamics of calcium FC and hemodynamic FC measured in the same short time windows. Moreover, we found that variation in transient neuronal coactivation patterns was significantly related to temporal fluctuations of sliding window FC in hemodynamics. Finally, we show that the observed dynamics of FC cannot be fully accounted for by simulated data assuming stationary FC. These results provide evidence for the neuronal origin of dynamic FC and further suggest that information relevant to FC is condensed in temporally sparse events that can be extracted using a small number of time points.
静息态功能连接(FC)已成为研究人类大脑网络组织的主要功能磁共振成像方法。最近,人们对使用短时间窗口计算的 FC 的时间波动(“动态 FC”)产生了兴趣,因为这种方法可以提供传统“静态”FC 无法获取的信息,而传统的“静态”FC 通常是使用持续数十分钟的整个扫描来计算的。尽管多项研究揭示了 FC 存在相当大的时间波动,但仍不清楚在血液动力学中测量的 FC 的波动是否反映了潜在神经活动的动力学。我们使用在小鼠中同时进行神经元钙和血液动力学信号成像的方法来解决这个问题,结果发现钙 FC 和在相同短时间窗口中测量的血液动力学 FC 的协调时间动态。此外,我们发现瞬时神经元共激活模式的变化与血液动力学中滑动窗口 FC 的时间波动显著相关。最后,我们表明,观察到的 FC 动态不能完全由假设静止 FC 的模拟数据来解释。这些结果为动态 FC 的神经元起源提供了证据,并进一步表明与 FC 相关的信息浓缩在时间稀疏的事件中,可以使用少量的时间点来提取这些事件。