Dynamic analysis of epileptic causal brain networks based on directional transfer function.
10.7507/1001-5515.202202022
- Author:
Ling HAN
1
;
Xinke SONG
1
;
Chunsheng LI
1
Author Information
1. Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, P. R. China.
- Publication Type:Journal Article
- Keywords:
Causal brain network;
Clustering coefficient;
Directional transfer function;
Global efficiency
- MeSH:
Humans;
Epilepsy;
Brain;
Seizures;
Electroencephalography;
Occipital Lobe
- From:
Journal of Biomedical Engineering
2022;39(6):1082-1088
- CountryChina
- Language:Chinese
-
Abstract:
Epilepsy is a neurological disease with disordered brain network connectivity. It is important to analyze the brain network mechanism of epileptic seizure from the perspective of directed functional connectivity. In this paper, causal brain networks were constructed for different sub-bands of epileptic electroencephalogram (EEG) signals in interictal, preictal and ictal phases by directional transfer function method, and the information transmission pathway and dynamic change process of brain network under different conditions were analyzed. Finally, the dynamic changes of characteristic attributes of brain networks with different rhythms were analyzed. The results show that the topology of brain network changes from stochastic network to rule network during the three stage and the node connections of the whole brain network show a trend of gradual decline. The number of pathway connections between internal nodes of frontal, temporal and occipital regions increase. There are a lot of hub nodes with information outflow in the lesion region. The global efficiency in ictal stage of α, β and γ waves are significantly higher than in the interictal and the preictal stage. The clustering coefficients in preictal stage are higher than in the ictal stage and the clustering coefficients in ictal stage are higher than in the interictal stage. The clustering coefficients of frontal, temporal and parietal lobes are significantly increased. The results of this study indicate that the topological structure and characteristic properties of epileptic causal brain network can reflect the dynamic process of epileptic seizures. In the future, this study has important research value in the localization of epileptic focus and prediction of epileptic seizure.
