Ma, Y.; Qian, J.; Gu, Q.; Yi, W.; Yan, W.; Yuan, J.; Wang, J. Network Analysis of Depression Using Magnetoencephalogram Based on Polynomial Kernel Granger Causality. Entropy2023, 25, 1330.
Ma, Y.; Qian, J.; Gu, Q.; Yi, W.; Yan, W.; Yuan, J.; Wang, J. Network Analysis of Depression Using Magnetoencephalogram Based on Polynomial Kernel Granger Causality. Entropy 2023, 25, 1330.
Ma, Y.; Qian, J.; Gu, Q.; Yi, W.; Yan, W.; Yuan, J.; Wang, J. Network Analysis of Depression Using Magnetoencephalogram Based on Polynomial Kernel Granger Causality. Entropy2023, 25, 1330.
Ma, Y.; Qian, J.; Gu, Q.; Yi, W.; Yan, W.; Yuan, J.; Wang, J. Network Analysis of Depression Using Magnetoencephalogram Based on Polynomial Kernel Granger Causality. Entropy 2023, 25, 1330.
Abstract
Depression is one of the psychiatric disorders characterized by anxiety, pessimism, and suicidal tendencies, which seriously affect the quality of life of patients and their families. In this paper, we used polynomial-based kernel Granger causality values as network node connectivity indicators to construct brain networks for 5 depressed patients and 11 healthy individuals’ magnetoencephalogram(MEG) under positive, neutral, and negative emotional stimuli, respectively, and found that depressed patients had closer information exchange between frontal and occipital regions compared to healthy individuals and other brain regions, and fewer causal connections in parietal and central regions. Further analysis of the topological properties of the network revealed that depressed patients had higher mean degrees under negative stimuli (p=0.008)and lower mean clustering coefficients than healthy individuals(p=0.034). Comparing the mean degree and mean clustering coefficient of the same sample under different emotional stimuli, we found that depressed patients had the greater mean degree and mean clustering coefficient under negative stimuli than neutral and positive stimuli. We also found that patients’ feature path lengths under negative and neutral stimuli significantly deviated from small-world attributes. The results suggest that analysis of nuclear Granger causality-based brain networks can effectively characterize depression pathology.
Keywords
Brain network; Magnetoencephalogram; Granger causality; Kernel function
Subject
Computer Science and Mathematics, Signal Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
