Study on functional connectivity of brain networks in different emotional states based on graph theory
10.19745/j.1003-8868.2023221
- VernacularTitle:基于图论的不同情绪状态下脑网络功能连接研究
- Author:
Xiao-Hua LI
1
;
Lei GAO
;
Ke-Jun YU
Author Information
1. 青岛市城阳区第二人民医院医学工程科,山东青岛 266000
- Keywords:
electroencephalogram;
graph theory;
brain network;
functional connectivity;
phase locking value
- From:
Chinese Medical Equipment Journal
2023;44(11):9-14
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the changes in the functional connectivity of the brain networks of individuals in different emotional states based on electroencephalogram(EEG)signals and quantitatively analyze the differential changes in the properties of the functional brain networks with global graph theory metrics.Methods The EEG signals of the calm state(enrolled into a calm state group)and the stress state(into a stress state group)were extracted from the Database for Emotion Analysis Using Physiological Signals(DEAP),and were divided into 4 frequency bands including a Theta band([4,8)Hz),an Alpha band([8,13)Hz),a Beta band([13,31)Hz)and a Gamma band([31,45)Hz),and the phase locking value(PLV)of each band was calculated to get a PLV brain network matrix,and then the topological structure of the attributes of the PLV network matrix was investigated with three kinds of global attributes of graph theory including the small-world attribute,clustering coefficient and characteristic path length.Comparison analyses were carried out on the functional connectivity of the brain networks and the global attributes of graph theory of the 2 groups of emotional states.SPSS 25.0 software was used for statistical analysis.Results Comparison of the functional connectivity between the 2 groups of emotional states showed statistically significant differences in connectivity between different brain regions in the 4 frequency bands(P＜0.05).The stress state group had the small-world attribute weakened at the Gamma band while the clustering coefficients and characteristic path lengths at the Alpha,Beta and Gamma bands increased obviously(P＜0.05)when compared with the calm state group;the stress state group and the calm state group had no statistical differences in the global attributes of graph theory at the Theta band(P＞0.05).Conclusion Different emotional states of individuals proves to be significantly characte-rized in terms of brain functional connectivity.Three global attributes of graph theory including the small-world attribute,clustering coefficient and characteristic path length can be used as the key feature parameters for the recognition of emotional states.References are provided for the diagnosis and treatment of emotional state-and affection-related brain functional dis-orders.[Chinese Medical Equipment Journal,2023,44(11):9-14]
