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

虽然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的结构化活动的证据，这可以驱动适应性和适应不良行为。这些大规模的网络已经在许多物种中被描述，并反映了哺乳动物进化中产生的复杂生理原则的整合。然而，在健康的大脑中，随着时间的推移快速转变的神经活动单元如何组装成在数十分钟和更长时间尺度内稳定的网络尚不清楚。这一点非常重要，因为这些转变必须受到仔细的监管，否则大规模网络及其驱动的行为反应将会退化。这是我的研究项目的驱动问题。这项发现补助金将利用小鼠作为一个易于处理的神经系统来推进研究计划。我和其他人以前已经确定了自发神经活动中的刻板和重复的活动模式，这些活动模式以半可预测的顺序从一个过渡到另一个。可能的基序和控制基序转换的过程的领域是理解稳定的大规模网络随着时间的推移及其相关的行为库的关键。 为了验证这一假设，我将使用体内皮层成像和遗传编码的钙指标来采样自发皮层活动的时期。然后，我建议使用新的无监督学习方法，能够描述可能的活动模式及其随时间的概率关系的领域。除了表征破坏或驱动独特的顺序基序组装的行为后遗症之外，还将使用化学遗传学和光遗传学方法来测试这些模型的有效性。最后，我建议高度保守的神经调节系统的特点与分布式的上升预测皮质和他们的作用，在雕塑自发皮质图案的顺序过渡。我们的方法是新颖的，发现补助金将为高素质的人才培训和发展提供丰富和令人兴奋的机会。这项工作将极大地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了一个资源。

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.