A novel index of functional connectivity: phase lag based on Wilcoxon signed rank test
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RESEARCH ARTICLE
A novel index of functional connectivity: phase lag based on Wilcoxon signed rank test Xuan Li1 • Yunqiao Wu1 • Mengting Wei2 • Yiyun Guo3 • Zhenhua Yu4 • Haixian Wang1 Zhanli Li4 • Hui Fan5
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Received: 10 January 2020 / Revised: 7 September 2020 / Accepted: 21 October 2020 Ó Springer Nature B.V. 2020
Abstract Phase synchronization has been an effective measurement of functional connectivity, detecting similar dynamics over time among distinct brain regions. However, traditional phase synchronization-based functional connectivity indices have been proved to have some drawbacks. For example, the phase locking value (PLV) index is sensitive to volume conduction, while the phase lag index (PLI) and the weighted phase lag index (wPLI) are easily affected by noise perturbations. In addition, thresholds need to be applied to these indices to obtain the binary adjacency matrix that determines the connections. However, the selection of the thresholds is generally arbitrary. To address these issues, in this paper we propose a novel index of functional connectivity, named the phase lag based on the Wilcoxon signed-rank test (PLWT). Specifically, it characterizes the functional connectivity based on the phase lag with a weighting procedure to reduce the influence of volume conduction and noise. Besides, it automatically identifies the important connections without relying on thresholds, by taking advantage of the framework of the Wilcoxon signed-rank test. The performance of the proposed PLWT index is evaluated on simulated electroencephalograph (EEG) datasets, as well as on two resting-state EEG datasets. The experimental results on the simulated EEG data show that the PLWT index is robust to volume conduction and noise. Furthermore, the brain functional networks derived by PLWT on the real EEG data exhibit a reasonable scale-free characteristic and high test–retest (TRT) reliability of graph measures. We believe that the proposed PLWT index provides a useful and reliable tool to identify the underlying neural interactions, while effectively diminishing the influence of volume conduction and noise. Keywords Phase synchronization Volume conduction Wilcoxon signed-rank test Graph measures Scale-free characteristic Test–retest reliability
Xuan Li, Yunqiao Wu, Mengting Wei these authors contributed equally to this work. & Haixian Wang [email protected]
3
Qingdao Port International Company, Ltd, Qingdao 266011, Shandong, People’s Republic of China
4
College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an 710054, Shanxi, People’s Republic of China
5
Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Shandong Technology and Business University, Yantai 264005, Shandong, People’s Republic of China
& Zhanli Li [email protected] 1
Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast Uni
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