Schizophrenia MEG Network Analysis Based on Kernel Granger Causality
Network analysis is an important approach to explore complex brain structures under different pathological and physiological conditions. In this paper, we employ the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) to construct directed weighted networks to characterize schizoph...
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MDPI AG
2023-06-01
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| Online Access: | https://www.mdpi.com/1099-4300/25/7/1006 |
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| author | Qiong Wang Wenpo Yao Dengxuan Bai Wanyi Yi Wei Yan Jun Wang |
| author_facet | Qiong Wang Wenpo Yao Dengxuan Bai Wanyi Yi Wei Yan Jun Wang |
| author_sort | Qiong Wang |
| collection | DOAJ |
| description | Network analysis is an important approach to explore complex brain structures under different pathological and physiological conditions. In this paper, we employ the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) to construct directed weighted networks to characterize schizophrenia magnetoencephalography (MEG). We first generate data based on coupled autoregressive processes to test the effectiveness of MKGC in comparison with the bivariate linear Granger causality and bivariate inhomogeneous polynomial kernel Granger causality. The test results suggest that MKGC outperforms the other two methods. Based on these results, we apply MKGC to construct effective connectivity networks of MEG for patients with schizophrenia (SCZs). We measure three network features, i.e., strength, nonequilibrium, and complexity, to characterize schizophrenia MEG. Our results suggest that MEG of the healthy controls (HCs) has a denser effective connectivity network than that of SCZs. The most significant difference in the in-connectivity strength is observed in the right frontal network (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo>=</mo><mn>0.001</mn></mrow></semantics></math></inline-formula>). The strongest out-connectivity strength for all subjects occurs in the temporal area, with the most significant between-group difference in the left occipital area (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo>=</mo><mn>0.0018</mn></mrow></semantics></math></inline-formula>). The total connectivity strength of the frontal, temporal, and occipital areas of HCs exhibits higher values compared with SCZs. The nonequilibrium feature over the whole brain of SCZs is significantly higher than that of the HCs (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo>=</mo><mn>0.012</mn></mrow></semantics></math></inline-formula>); however, the results of Shannon entropy suggest that healthy MEG networks have higher complexity than schizophrenia networks. Overall, MKGC provides a reliable approach to construct MEG brain networks and characterize the network characteristics. |
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| spelling | doaj.art-6f3f814ac4164d578f4f1be1fa7c62f62023-11-18T19:13:20ZengMDPI AGEntropy1099-43002023-06-01257100610.3390/e25071006Schizophrenia MEG Network Analysis Based on Kernel Granger CausalityQiong Wang0Wenpo Yao1Dengxuan Bai2Wanyi Yi3Wei Yan4Jun Wang5School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaSmart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaSchool of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaDepartment of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, ChinaSmart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaNetwork analysis is an important approach to explore complex brain structures under different pathological and physiological conditions. In this paper, we employ the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) to construct directed weighted networks to characterize schizophrenia magnetoencephalography (MEG). We first generate data based on coupled autoregressive processes to test the effectiveness of MKGC in comparison with the bivariate linear Granger causality and bivariate inhomogeneous polynomial kernel Granger causality. The test results suggest that MKGC outperforms the other two methods. Based on these results, we apply MKGC to construct effective connectivity networks of MEG for patients with schizophrenia (SCZs). We measure three network features, i.e., strength, nonequilibrium, and complexity, to characterize schizophrenia MEG. Our results suggest that MEG of the healthy controls (HCs) has a denser effective connectivity network than that of SCZs. The most significant difference in the in-connectivity strength is observed in the right frontal network (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo>=</mo><mn>0.001</mn></mrow></semantics></math></inline-formula>). The strongest out-connectivity strength for all subjects occurs in the temporal area, with the most significant between-group difference in the left occipital area (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo>=</mo><mn>0.0018</mn></mrow></semantics></math></inline-formula>). The total connectivity strength of the frontal, temporal, and occipital areas of HCs exhibits higher values compared with SCZs. The nonequilibrium feature over the whole brain of SCZs is significantly higher than that of the HCs (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo>=</mo><mn>0.012</mn></mrow></semantics></math></inline-formula>); however, the results of Shannon entropy suggest that healthy MEG networks have higher complexity than schizophrenia networks. Overall, MKGC provides a reliable approach to construct MEG brain networks and characterize the network characteristics.https://www.mdpi.com/1099-4300/25/7/1006kernel Granger causalityeffective networkschizophrenia MEGnonequilibriumcomplexity |
| spellingShingle | Qiong Wang Wenpo Yao Dengxuan Bai Wanyi Yi Wei Yan Jun Wang Schizophrenia MEG Network Analysis Based on Kernel Granger Causality Entropy kernel Granger causality effective network schizophrenia MEG nonequilibrium complexity |
| title | Schizophrenia MEG Network Analysis Based on Kernel Granger Causality |
| title_full | Schizophrenia MEG Network Analysis Based on Kernel Granger Causality |
| title_fullStr | Schizophrenia MEG Network Analysis Based on Kernel Granger Causality |
| title_full_unstemmed | Schizophrenia MEG Network Analysis Based on Kernel Granger Causality |
| title_short | Schizophrenia MEG Network Analysis Based on Kernel Granger Causality |
| title_sort | schizophrenia meg network analysis based on kernel granger causality |
| topic | kernel Granger causality effective network schizophrenia MEG nonequilibrium complexity |
| url | https://www.mdpi.com/1099-4300/25/7/1006 |
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