Objective. Connectivity networks are crucial to understand the brain resting-state activity using functional magnetic resonance imaging (rs-fMRI). Alterations of these brain networks may highlight important findings concerning the resilience of the brain to different disorders. The focus of this paper is to evaluate the robustness of brain network estimations, discriminate them under anesthesia and compare them to generative models. Approach. The extraction of brain functional connectivity (FC) networks is difficult and biased due to the properties of the data: low signal to noise ratio, high dimension low sample size. We propose to use wavelet correlations to assess FC between brain areas under anesthesia using four anesthetics (isoflurane, etomidate, medetomidine, urethane). The networks are then deduced from the functional connectivity matrices by applying statistical thresholds computed using the number of samples at a given scale of wavelet decomposition. Gra...

中文翻译：

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使用静止状态功能磁共振成像在麻醉下的大鼠脑网络：与死大鼠的比较，随机噪声和网络生成模型。

目的。连接网络对于使用功能磁共振成像（rs-fMRI）了解大脑静止状态的活动至关重要。这些大脑网络的变化可能会突出显示有关大脑对不同疾病的适应力的重要发现。本文的重点是评估脑网络估计的鲁棒性，在麻醉下区分它们，并将其与生成模型进行比较。方法。由于数据的特性，脑功能连接（FC）网络的提取非常困难且有偏见：信噪比低，维数高，样本量小。我们建议使用小波相关性来评估使用四种麻醉剂（异氟烷，依托咪酯，美托咪定，氨基甲酸酯）在麻醉下大脑区域之间的FC。然后通过应用统计阈值从功能连通性矩阵推导网络，该统计阈值是使用给定小波分解规模下的样本数计算得出的。谢谢...