Recurrence quantification analysis of dynamic brain networks,European Journal of Neuroscience

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Recurrence quantification analysis of dynamic brain networks
European Journal of Neuroscience ( IF 2.7 ) Pub Date : 2020-09-04 , DOI: 10.1111/ejn.14960
Marinho A. Lopes _{1,

2} , Jiaxiang Zhang ₂ , Dominik Krzemiński ₂ , Khalid Hamandi ₂ , Qi Chen ₃ , Lorenzo Livi _{4,

5} , Naoki Masuda _{1,

6,

7}

Affiliation

Evidence suggests that brain network dynamics are a key determinant of brain function and dysfunction. Here we propose a new framework to assess the dynamics of brain networks based on recurrence analysis. Our framework uses recurrence plots and recurrence quantification analysis to characterize dynamic networks. For resting‐state magnetoencephalographic dynamic functional networks (dFNs), we have found that functional networks recur more quickly in people with epilepsy than in healthy controls. This suggests that recurrence of dFNs may be used as a biomarker of epilepsy. For stereo electroencephalography data, we have found that dFNs involved in epileptic seizures emerge before seizure onset, and recurrence analysis allows us to detect seizures. We further observe distinct dFNs before and after seizures, which may inform neurostimulation strategies to prevent seizures. Our framework can also be used for understanding dFNs in healthy brain function and in other neurological disorders besides epilepsy.

中文翻译：

动态脑网络的递归量化分析

有证据表明，大脑网络动力学是大脑功能和功能障碍的关键决定因素。在这里，我们提出了一个基于递归分析评估大脑网络动态的新框架。我们的框架使用递归图和递归量化分析来表征动态网络。对于静息状态的脑磁图动态功能网络（dFN），我们发现癫痫患者的功能网络比健康对照组的复发更快。这表明dFN的复发可以用作癫痫的生物标志物。对于立体脑电图数据，我们发现与癫痫发作有关的dFN在发作开始之前就已出现，而复发分析使我们能够检测到癫痫发作。我们进一步观察到癫痫发作前后有不同的dFN，这可能有助于神经刺激策略预防癫痫发作。我们的框架还可以用于了解健康的脑功能和癫痫以外的其他神经系统疾病中的dFN。

更新日期：2020-09-04

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