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Open AccessArticle

Network Entropy for the Sequence Analysis of Functional Connectivity Graphs of the Brain

1
School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
2
Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla FIN-40014, Finland
3
Department of Psychology, University of Jyvaskyla, Jyvaskyla FIN-40014, Finland
*
Authors to whom correspondence should be addressed.
Entropy 2018, 20(5), 311; https://doi.org/10.3390/e20050311
Received: 1 March 2018 / Revised: 11 April 2018 / Accepted: 17 April 2018 / Published: 25 April 2018
(This article belongs to the Special Issue Graph and Network Entropies)
Dynamic representation of functional brain networks involved in the sequence analysis of functional connectivity graphs of the brain (FCGB) gains advances in uncovering evolved interaction mechanisms. However, most of the networks, even the event-related ones, are highly heterogeneous due to spurious interactions, which bring challenges to revealing the change patterns of interactive information in the complex dynamic process. In this paper, we propose a network entropy (NE) method to measure connectivity uncertainty of FCGB sequences to alleviate the spurious interaction problem in dynamic network analysis to realize associations with different events during a complex cognitive task. The proposed dynamic analysis approach calculated the adjacency matrices from ongoing electroencephalpgram (EEG) in a sliding time-window to form the FCGB sequences. The probability distribution of Shannon entropy was replaced by the connection sequence distribution to measure the uncertainty of FCGB constituting NE. Without averaging, we used time frequency transform of the NE of FCGB sequences to analyze the event-related changes in oscillatory activity in the single-trial traces during the complex cognitive process of driving. Finally, the results of a verification experiment showed that the NE of the FCGB sequences has a certain time-locked performance for different events related to driver fatigue in a prolonged driving task. The time errors between the extracted time of high-power NE and the recorded time of event occurrence were distributed within the range [−30 s, 30 s] and 90.1% of the time errors were distributed within the range [−10 s, 10 s]. The high correlation (r = 0.99997, p < 0.001) between the timing characteristics of the two types of signals indicates that the NE can reflect the actual dynamic interaction states of brain. Thus, the method may have potential implications for cognitive studies and for the detection of physiological states. View Full-Text
Keywords: network entropy; connectivity; brain network; dynamic network analysis; event-related analysis; driver fatigue network entropy; connectivity; brain network; dynamic network analysis; event-related analysis; driver fatigue
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Zhang, C.; Cong, F.; Kujala, T.; Liu, W.; Liu, J.; Parviainen, T.; Ristaniemi, T. Network Entropy for the Sequence Analysis of Functional Connectivity Graphs of the Brain. Entropy 2018, 20, 311.

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