Modules of spontaneous cortical activity in mouse cortex during quiet wakefulness: key nodes and shaping by neuromodulators.

尽管大脑中的神经活动是高度动态的，但它显示了根据大规模大脑网络进行结构化活动的证据，这可以驱动适应和不适应行为。这些大规模的网络已经在许多物种中被描述，并且反映了哺乳动物进化中产生的复杂生理原理的整合。然而，在健康的大脑中，神经活动的单位是如何随着时间的推移迅速转变成在几十分钟或更长的时间尺度上保持稳定的网络的，目前还不清楚。这是非常重要的，因为这些转变必须谨慎地加以管理，否则大规模的网络及其驱动的行为反应就会退化。这是我的研究项目的驱动问题。这项发现基金将利用老鼠作为一种可控制的神经系统来推进研究计划。我和其他人之前已经在自发的神经活动中发现了刻板的和重复的活动模式，这些活动的主题以半可预测的顺序从一个转移到另一个。可能的基序领域和控制基序转换的过程是理解稳定的大规模网络及其相关行为的关键。为了验证这一假设，我将使用体内皮层成像和基因编码钙指标来采样自发皮层活动的时代。然后，我建议使用新颖的无监督学习方法，能够描述可能的活动主题领域及其随时间的概率关系。除了描述破坏或驱动独特序列基序组装的行为后遗症外，还将使用化学遗传学和光遗传学方法测试这些模型的有效性。最后，我建议描述高度保守的神经调节系统的特征，这些神经调节系统具有分布到皮层的上升投影，以及它们在自发皮层基序的顺序转换中所起的作用。我们的方法是新颖的，探索基金将为高素质人才的培训和发展提供丰富而令人兴奋的机会。提出的工作将大大增加我们对大规模大脑网络的生理基础的理解，并为探索皮层活动动力学的生理学创造资源。

Though neural activity in the brain is highly dynamic, it shows evidence of structured activity according to large-scale brain networks, which can drive both adaptive and maladaptive behaviors. These large-scale networks have been described in many species, and reflect the integration of complex physiological principles arising from mammalian evolution. Yet, how units of neural activity that rapidly transition over time assemble into networks that are stable over tens of minutes and longer timescales in the healthy brain is unclear. This is of major importance, as these transitions must be carefully regulated or the large-scale networks, and the behavioral responses they drive, would degrade. This is my research program's driving question. This Discovery Grant will utilize the mouse as a tractable neural system to advance the research program. I, and others, have previously identified stereotyped and repeated patterns of activity within spontaneous neural activity, and these motifs of activity transition from one to another in semi-predictable sequences. The realm of possible motifs and the processes governing motif transitions is key to understanding stable large-scale networks over time and their associated repertoire of behaviours. To test this hypothesis, I will use in vivo cortical imaging and genetically encoded calcium indicators to sample epochs of spontaneous cortical activity. I then propose to use novel unsupervised learning approaches capable of describing the realm of possible activity motifs and their probabilistic relationships over time. The validity of these models will be tested using chemogenetic and optogenetic approaches, in addition to characterizing the behavioral sequelae of disrupting or driving unique sequential motif assemblies. Finally, I propose to characterize highly conserved neuromodulator systems with distributed ascending projections to cortex and their role in sculpting sequential transitions in spontaneous cortical motifs. Our approach is novel, and the Discovery Grant will provide fertile and exciting opportunities for highly qualified personnel training and development. The work proposed will dramatically increase our understanding of the physiological basis underlying large-scale brain networks, and creates a resource for probing the physiology of cortical activity dynamics.