Depression recognition using high-order generalized multilayer brain functional network fused with EEG multi-domain information

Major Depressive Disorder (MDD) is a serious and highly heterogeneous psychological disorder. According to the network hypothesis, depression originates from abnormal neural network information processing, typically resulting in aberrant changes in the topological structure of the brain's functional network. Recent evidence further reveals that depression involves dynamic changes related to both within- and cross-frequency coupling. Therefore, we utilize second-order tensor expansion to integrate frequency- and time-varying multilayer brain functional networks based on node sharing, thus propose a generalized multilayer brain functional network (GMBFN) incorporating multi-domain information. Concurrently, we derive global and local topological properties from both the frequency and temporal domains to characterize the novel network structure. To uncover more reliable biomarkers and explore various coupling features that can assess the interaction between signals from different perspectives, we conduct research in two datasets employing four sets of within- and cross-frequency coupling. Leveraging the novel multi-domain high-order GMBFNs, abnormalities of information integration abilities in patients with MDD are observed, particularly in the theta-band and overall temporal-domain. Through the fusion of topological properties across both domains with multiple classifiers, the alpha-band can serve as a potential biomarker for depression identification. More importantly, the combination of global topological properties from both domains, on average, enhances the classification performance for identifying patients with MDD by 5.18% compared to using just one domain. This study presents a systematic framework for comprehending the aberrant mechanisms of MDD from multiple perspectives, offering significant value for clinical applications aimed at assisting in depression diagnosis and intervention.