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，但根据大规模大脑网络，它显示出结构化活动的证据，这可能会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，并反映了哺乳动物进化产生的复杂生理原理的整合。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上稳定的网络尚不清楚。这一点非常重要，因为必须仔细监管这些转变，否则大规模网络及其驱动的行为反应将会降级。这是我的研究项目的驾驶问题。这项发现奖助金将利用鼠标作为一个易于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换到另一个。可能的基序和支配基序转换的过程的领域是理解随着时间的推移稳定的大规模网络及其相关行为剧目的关键。为了验证这一假设，我将使用活体皮质成像和遗传编码的钙指示剂来采样自发皮质活动的时期。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主题及其随时间的概率关系的领域。这些模型的有效性将使用化学遗传学和光遗传学方法进行测试，此外还将表征扰乱或驱动独特序列基序组装的行为后遗症。最后，我建议描述高度保守的神经调节系统的特征，以及它们在塑造自发的皮质图案中的顺序转变中的作用。我们的方法是新颖的，探索助学金将为高素质人才的培训和发展提供丰厚和令人兴奋的机会。所提出的工作将极大地增加我们对大规模脑网络背后的生理学基础的理解，并为探索皮质活动动力学的生理学创造了资源。

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.