Neuroscientists have enjoyed much success in understanding brain functions by constructing brain connectivity networks using data collected under highly controlled experimental settings. However, these experimental settings bear little resemblance to our real-life experience in day-to-day interactions with the surroundings. To address this issue, neuroscientists have been measuring brain activity under natural viewing experiments in which the subjects are given continuous stimuli, such as watching a movie or listening to a story. The main challenge with this approach is that the measured signal consists of both the stimulus-induced signal, as well as intrinsic-neural and non-neuronal signals. By exploiting the experimental design, we propose to estimate stimulus-locked brain network by treating non-stimulus-induced signals as nuisance parameters. In many neuroscience applications, it is often important to identify brain regions that are connected to many other brain regions during cognitive process. We propose an inferential method to test whether the maximum degree of the estimated network is larger than a pre-specific number. We prove that the type I error can be controlled and that the power increases to one asymptotically. Simulation studies are conducted to assess the performance of our method. Finally, we analyze a functional magnetic resonance imaging dataset obtained under the Sherlock Holmes movie stimuli.

中文翻译：

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估计和推断刺激锁定时变大脑连接网络的最大程度

神经科学家通过使用在高度控制的实验环境下收集的数据构建大脑连接网络，在理解大脑功能方面取得了巨大成功。然而，这些实验设置与我们与周围环境的日常互动的现实生活体验几乎没有相似之处。为了解决这个问题，神经科学家一直在自然观看实验下测量大脑活动，在这些实验中，受试者会受到连续的刺激，例如看电影或听故事。这种方法的主要挑战是测量的信号由刺激引起的信号以及内在神经和非神经元信号组成。通过利用实验设计，我们建议通过将非刺激引起的信号视为滋扰参数来估计刺激锁定的大脑网络。在许多神经科学应用中，识别在认知过程中与许多其他大脑区域相连的大脑区域通常很重要。我们提出了一种推理方法来测试估计网络的最大度是否大于预先指定的数字。我们证明了 I 类误差是可以控制的，并且幂逐渐增加到 1。进行模拟研究来评估我们方法的性能。最后，我们分析了在福尔摩斯电影刺激下获得的功能磁共振成像数据集。