Hybrid High-order Brain Functional Networks for Schizophrenia-Aided Diagnosis

Background Electroencephalogram technology provides a reference for the study of schizophrenia. Constructing brain functional networks using electroencephalogram technology is one of the important methods to analyze the human brain. Current methods to construct brain functional networks often ignore the deeper interactions between brain regions and the phenomenons that the connectivity patterns of brain change over time. Methods Therefore, for the aided diagnosis of schizophrenia, a hybrid high-order brain functional network model is proposed, the model characterizing more complex functional interactions of brain includes static low-order multilayer brain functional networks and dynamic high-order multilayer brain functional networks. Results The results show that the classification method based on the proposed model is effective and efficient, with an accuracy of 94.05%, a sensitivity of 95.56% and a specificity of 92.31%. Conclusions Experimental results on the schizophrenia dataset show that the proposed method has satisfied performances; the complementarity between low-order and high-order multilayer brain functional networks could better capture brain functional interactions. The findings which suggest the importance of improved relationships between brain regions and temporal features of connectivities in the brain bring new biologically inspired implications.