Department of Neuroscience, Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT, USA.
Program in Applied Mathematics, Yale University, New Haven, CT, USA.
Nat Neurosci. 2024 Jan;27(1):148-158. doi: 10.1038/s41593-023-01498-y. Epub 2023 Nov 30.
Experimental work across species has demonstrated that spontaneously generated behaviors are robustly coupled to variations in neural activity within the cerebral cortex. Functional magnetic resonance imaging data suggest that temporal correlations in cortical networks vary across distinct behavioral states, providing for the dynamic reorganization of patterned activity. However, these data generally lack the temporal resolution to establish links between cortical signals and the continuously varying fluctuations in spontaneous behavior observed in awake animals. Here, we used wide-field mesoscopic calcium imaging to monitor cortical dynamics in awake mice and developed an approach to quantify rapidly time-varying functional connectivity. We show that spontaneous behaviors are represented by fast changes in both the magnitude and correlational structure of cortical network activity. Combining mesoscopic imaging with simultaneous cellular-resolution two-photon microscopy demonstrated that correlations among neighboring neurons and between local and large-scale networks also encode behavior. Finally, the dynamic functional connectivity of mesoscale signals revealed subnetworks not predicted by traditional anatomical atlas-based parcellation of the cortex. These results provide new insights into how behavioral information is represented across the neocortex and demonstrate an analytical framework for investigating time-varying functional connectivity in neural networks.
跨物种的实验工作表明，自发产生的行为与大脑皮层内的神经活动变化密切相关。功能磁共振成像数据表明，皮质网络中的时间相关性在不同的行为状态下变化，为模式活动的动态重组提供了可能。然而，这些数据通常缺乏时间分辨率，无法建立皮质信号与在清醒动物中观察到的自发行为的连续变化之间的联系。在这里，我们使用宽场介观钙成像来监测清醒小鼠的皮层动力学，并开发了一种量化快速时变功能连接的方法。我们表明，自发行为表现为皮层网络活动幅度和相关性结构的快速变化。将介观成像与同时的细胞分辨率双光子显微镜结合使用，证明了相邻神经元之间以及局部和大规模网络之间的相关性也可以编码行为。最后，介观信号的动态功能连接揭示了传统基于皮质图谱的分区方法无法预测的子网。这些结果为行为信息如何在新皮层中表示提供了新的见解，并展示了一种用于研究神经网络中时变功能连接的分析框架。
