University Grenoble Alpes, CNRS, Grenoble INP, Gipsa-lab, 38000, Grenoble, France.
J Neural Eng. 2020 Aug 4;17(4):045012. doi: 10.1088/1741-2552/ab9fec.
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.
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. Graph measures are extracted and extensive comparisons with generative models of structured networks are conducted.
The sample size and filtering are critical to obtain significant correlations values and thereby detect connections between regions. This is necessary to construct networks different from random ones as shown using rs-fMRI brain networks of dead rats. Brain networks under anesthesia on rats have topological features that are mixing small-world, scale-free and random networks. Betweenness centrality indicates that hubs are present in brain networks obtained from anesthetized rats but locations of these hubs are altered by anesthesia.
Understanding the effects of anesthesia on brain areas is of particular importance in the context of animal research since animal models are commonly used to explore functions, evaluate lesions or illnesses, and test new drugs. More generally, results indicate that the use of correlations in the context of fMRI signals is robust but must be treated with caution. Solutions are proposed in order to control spurious correlations by setting them to zero.
连接网络对于使用功能磁共振成像（rs-fMRI）理解大脑静息状态活动至关重要。这些大脑网络的改变可能突出了关于大脑对不同疾病的弹性的重要发现。本文的重点是评估大脑网络估计的稳健性，在麻醉下区分它们，并将它们与生成模型进行比较。
由于数据的特性，大脑功能连接（FC）网络的提取具有难度且存在偏差：信噪比低、维度高、样本量小。我们建议使用小波相关来评估四种麻醉剂（异氟烷、依托咪酯、美托咪定、氨基甲酸乙酯）下大脑区域的 FC。然后，通过应用在给定小波分解尺度下使用样本数量计算的统计阈值，从功能连接矩阵中推导出网络。提取图度量并与结构化网络的生成模型进行广泛比较。
样本量和过滤对于获得显著相关值并从而检测区域之间的连接至关重要。这对于构建与随机网络不同的网络是必要的，正如使用死老鼠的 rs-fMRI 大脑网络所示。麻醉大鼠的大脑网络具有混合小世界、无标度和随机网络的拓扑特征。介数中心性表明，在麻醉大鼠获得的大脑网络中存在枢纽，但这些枢纽的位置被麻醉改变了。
了解麻醉对大脑区域的影响在动物研究背景下尤为重要，因为动物模型通常用于探索功能、评估损伤或疾病以及测试新药。更一般地，结果表明在 fMRI 信号的背景下使用相关性是稳健的，但必须谨慎对待。提出了一些解决方案，以便通过将虚假相关性设置为零来控制它们。
